Mouse over the terms for more detail; many indicate links which you can click for dedicated pages about the topic. Tag cloud generated 29 August, 2019 using data from PubMed, MeSH and CancerIndex
Mutated Genes and Abnormal Protein Expression (114)
Clicking on the Gene or Topic will take you to a separate more detailed page. Sort this list by clicking on a column heading e.g. 'Gene' or 'Topic'.
|VHL ||3p25.3 ||RCA1, VHL1, pVHL, HRCA1 || ||-VHL and Kidney Cancer || 509|
|MTOR ||1p36.22 ||SKS, FRAP, FRAP1, FRAP2, RAFT1, RAPT1 || ||-MTOR and Renal Cell Carcinoma || 185|
|HIF1A ||14q23.2 ||HIF1, MOP1, PASD8, HIF-1A, bHLHe78, HIF-1alpha, HIF1-ALPHA, HIF-1-alpha || ||-HIF1A and Kidney Cancer || 159|
|PRCC ||1q23.1 ||TPRC, RCCP1 ||Translocation ||-t(X;1)(p11;q21) in Papillary Renal Cell Carcinoma |
-PRCC and Renal Cell Carcinoma
|MET ||7q31.2 ||HGFR, AUTS9, RCCP2, c-Met, DFNB97 || ||-C-MET and Renal Cell Carcinoma || 116|
|FH ||1q43 ||MCL, FMRD, HsFH, LRCC, HLRCC, MCUL1 || ||-FH and Kidney Cancer || 109|
|FLCN ||17p11.2 ||BHD, FLCL || ||-FLCN and Kidney Cancer || 99|
|TSC2 ||16p13.3 ||LAM, TSC4, PPP1R160 || ||-TSC2 and Kidney Cancer || 72|
|CA9 ||9p13.3 ||MN, CAIX || ||-CA9 and Kidney Cancer || 67|
|PAX8 ||2q13 || || ||-PAX8 and Kidney Cancer || 62|
|SDHB ||1p36.13 ||IP, SDH, CWS2, PGL4, SDH1, SDH2, SDHIP || ||-SDHB and Kidney Cancer || 52|
|TFEB ||6p21.1 ||TCFEB, BHLHE35, ALPHATFEB || ||-TFEB and Kidney Cancer || 51|
|PBRM1 ||3p21.1 ||PB1, BAF180 || ||-PBRM1 and Renal Cell Carcinoma || 51|
|ACHE ||7q22.1 ||YT, ACEE, ARACHE, N-ACHE || ||-ACHE and Kidney Cancer || 50|
|BAP1 ||3p21.1 ||UCHL2, hucep-6, HUCEP-13 || ||-BAP1 and Renal Cell Carcinoma || 47|
|SMARCB1 ||22q11.23 ||RDT, CSS3, INI1, SNF5, Snr1, BAF47, MRD15, RTPS1, Sfh1p, hSNFS, SNF5L1, SWNTS1, PPP1R144 || ||-SMARCB1 and Kidney Cancer || 47|
|TSC1 ||9q34.13 ||LAM, TSC || ||-TSC1 and Kidney Cancer || 44|
|SETD2 ||3p21.31 ||LLS, HYPB, SET2, HIF-1, HIP-1, KMT3A, HBP231, HSPC069, p231HBP || ||-SETD2 and Renal Cell Carcinoma || 42|
|MITF ||3p13 ||MI, WS2, CMM8, WS2A, COMMAD, bHLHe32 || ||-MITF and Kidney Cancer || 40|
|PAX2 ||10q24.31 ||FSGS7, PAPRS || ||-PAX2 and Kidney Cancer || 38|
|SLC2A1 ||1p34.2 ||CSE, PED, DYT9, GLUT, DYT17, DYT18, EIG12, GLUT1, HTLVR, GLUT-1, SDCHCN, GLUT1DS || ||-GLUT1 expression in Kidney Cancer || 35|
|AMACR ||5p13.2 ||RM, RACE, CBAS4, P504S, AMACRD || ||-AMACR and Renal Cell Carcinoma || 33|
|CD99 ||Xp22.33 and Yp11.2 ||MIC2, HBA71, MIC2X, MIC2Y, MSK5X || ||-CD99 and Kidney Cancer || 31|
|KDM5C ||Xp11.22 ||MRXJ, SMCX, MRX13, MRXSJ, XE169, MRXSCJ, JARID1C, DXS1272E || ||-KDM5C and Kidney Cancer || 24|
|CDKN1C ||11p15.4 ||BWS, WBS, p57, BWCR, KIP2, p57Kip2 || ||-CDKN1C and Kidney Cancer || 18|
|NONO ||Xq13.1 ||P54, NMT55, NRB54, MRXS34, P54NRB, PPP1R114 || ||-NONO and Kidney Cancer || 18|
|ASPSCR1 ||17q25.3 ||TUG, ASPL, ASPS, RCC17, UBXD9, UBXN9, ASPCR1 || ||-ASPSCR1 and Kidney Cancer || 18|
|SDHC ||1q23.3 ||CYBL, PGL3, QPS1, SDH3, CYB560 || ||-SDHC and Kidney Cancer || 17|
|POLE ||12q24.3 ||FILS, POLE1, CRCS12 || ||-POLE and Kidney Cancer || 15|
|BCOR ||Xp11.4 ||MAA2, ANOP2, MCOPS2 || ||-BCOR and Kidney Cancer || 15|
|SDHA ||5p15.33 ||FP, PGL5, SDH1, SDH2, SDHF, CMD1GG || ||-SDHA and Kidney Cancer || 14|
|WT1-AS ||11p13 ||WIT1, WIT-1, WT1AS, WT1-AS1 || ||-WT1-AS and Kidney Cancer || 13|
|SFPQ ||1p34.3 ||PSF, POMP100, PPP1R140 || ||-SFPQ and Kidney Cancer || 13|
|ZEB2 ||2q22.3 ||SIP1, SIP-1, ZFHX1B, HSPC082, SMADIP1 || ||-ZEB2 and Kidney Cancer || 9|
|EGLN3 ||14q13.1 ||PHD3, HIFPH3, HIFP4H3 || ||-EGLN3 and Kidney Cancer || 9|
|EPAS1 ||2p21-p16 ||HLF, MOP2, ECYT4, HIF2A, PASD2, bHLHe73 || ||-EPAS1 and Renal Cell Carcinoma || 8|
|HNF1B ||17q12 ||FJHN, HNF2, LFB3, TCF2, HPC11, LF-B3, MODY5, TCF-2, VHNF1, HNF-1B, HNF1beta, HNF-1-beta || ||-HNF1B and Renal Cell Carcinoma || 8|
|CITED1 ||Xq13.1 ||MSG1 || ||-CITED1 and Kidney Cancer || 8|
|KISS1 ||1q32.1 ||HH13, KiSS-1 || ||-KISS1 and Renal Cell Carcinoma || 7|
|MEST ||7q32.2 ||PEG1 || ||-MEST and Kidney Cancer || 7|
|RNF139 ||8q24.13 ||RCA1, TRC8, HRCA1 ||Translocation ||-t(3;8)(p14.2;q24.1) in Hereditary Renal Cell Carcinoma |
-RNF139 and Kidney Cancer
|YWHAE ||17p13.3 ||MDS, HEL2, MDCR, KCIP-1, 14-3-3E || ||-YWHAE and Kidney Cancer || 7|
|OSCAR ||19q13.42 ||PIGR3, PIgR-3 || ||-OSCAR and Kidney Cancer || 7|
|MEG3 ||14q32.2 ||GTL2, FP504, prebp1, PRO0518, PRO2160, LINC00023, NCRNA00023, onco-lncRNA-83 || ||-MEG3 and Kidney Cancer || 7|
|KISS1R ||19p13.3 ||HH8, CPPB1, GPR54, AXOR12, KISS-1R, HOT7T175 || ||-KISS1R and Renal Cell Carcinoma || 6|
|EGLN1 ||1q42.2 ||HPH2, PHD2, SM20, ECYT3, HALAH, HPH-2, HIFPH2, ZMYND6, C1orf12, HIF-PH2 || ||-EGLN1 and Kidney Cancer || 6|
|KCNQ1OT1 ||11p15.5 ||LIT1, Kncq1, KvDMR1, KCNQ10T1, KCNQ1-AS2, KvLQT1-AS, NCRNA00012 || ||-KCNQ1OT1 and Kidney Cancer || 6|
|CXCR3 ||Xq13.1 ||GPR9, MigR, CD182, CD183, Mig-R, CKR-L2, CMKAR3, IP10-R || ||-CXCR3 and Kidney Cancer || 6|
|FLT4 ||5q35.3 ||PCL, FLT-4, FLT41, LMPH1A, VEGFR3, VEGFR-3 || ||-FLT4 and Renal Cell Carcinoma || 6|
|STIM1 ||11p15.4 ||GOK, TAM, TAM1, IMD10, STRMK, D11S4896E || ||-STIM1 and Kidney Cancer || 6|
|CLTC ||17q23.1 ||Hc, CHC, CHC17, CLH-17, CLTCL2 || ||-CLTC and Kidney Cancer || 6|
|TGFBI ||5q31.1 ||CSD, CDB1, CDG2, CSD1, CSD2, CSD3, EBMD, LCD1, BIGH3, CDGG1 || ||-TGFBI and Kidney Cancer || 5|
|KDM6A ||Xp11.3 ||UTX, KABUK2, bA386N14.2 || ||-KDM6A and Kidney Cancer || 5|
|ACTB ||7p22.1 ||BRWS1, PS1TP5BP1 || ||-ACTB and Kidney Cancer || 5|
|MINA ||3q11.2 ||ROX, MDIG, NO52, MINA53 || ||-MINA and Kidney Cancer || 5|
|CA12 ||15q22.2 ||CAXII, CA-XII, T18816, HsT18816 || ||-CA12 and Kidney Cancer || 5|
|FABP7 ||6q22.31 ||MRG, BLBP, FABPB, B-FABP || ||-FABP7 and Kidney Cancer || 5|
|KRT7 ||12q13.13 ||K7, CK7, SCL, K2C7 || ||-KRT7 and Kidney Cancer || 5|
|PPIA ||7p13 ||CYPA, CYPH, HEL-S-69p || ||-PPIA and Renal Cell Carcinoma || 5|
|LDHA ||11p15.1 ||LDHM, GSD11, PIG19, HEL-S-133P || ||-LDHA and Kidney Cancer || 5|
|RAB25 ||1q22 ||CATX-8, RAB11C || ||-RAB25 and Kidney Cancer || 5|
|VIM ||10p13 ||HEL113, CTRCT30 || ||-VIM and Kidney Cancer || 5|
|RBX1 ||22q13.2 ||ROC1, RNF75, BA554C12.1 || ||-RBX1 and Kidney Cancer || 5|
|LGALS3 ||14q22.3 ||L31, GAL3, MAC2, CBP35, GALBP, GALIG, LGALS2 || ||-LGALS3 and Kidney Cancer || 4|
|NUTM2B ||10q22.3 ||FAM22B, bA119F19.1 || ||-NUTM2B and Kidney Cancer || 4|
|EGR2 ||10q21.3 ||AT591, CMT1D, CMT4E, KROX20 || ||-EGR2 and Kidney Cancer || 4|
|RAP1GAP ||1p36.12 ||RAPGAP, RAP1GA1, RAP1GAP1, RAP1GAPII || ||-RAP1GAP and Kidney Cancer || 4|
|GATA5 ||20q13.33 ||CHTD5, GATAS, bB379O24.1 || ||-GATA5 and Renal Cell Carcinoma || 4|
|ROR2 ||9q22.31 ||BDB, BDB1, NTRKR2 || ||-ROR2 and Renal Cell Carcinoma || 4|
|NNAT ||20q11.23 ||Peg5 || ||-NNAT and Kidney Cancer || 4|
|TNFSF15 ||9q32 ||TL1, TL1A, VEGI, TNLG1B, VEGI192A || ||-TNFSF1 and Kidney Cancer || 4|
|CD70 ||19p13.3 ||CD27L, CD27-L, CD27LG, TNFSF7, TNLG8A || ||-CD70 and Renal Cell Carcinoma || 4|
|CSF1R ||5q32 ||FMS, CSFR, FIM2, HDLS, C-FMS, CD115, CSF-1R, M-CSF-R || ||-CSF1R and Kidney Cancer || 4|
|CD74 ||5q33.1 ||II, DHLAG, HLADG, Ia-GAMMA || ||-CD74 and Kidney Cancer || 4|
|SLIT2 ||4p15.31 ||SLIL3, Slit-2 || ||-SLIT2 and Kidney Cancer || 4|
|CDCP1 ||3p21.31 ||CD318, TRASK, SIMA135 || ||-CDCP1 and Kidney Cancer || 4|
|CREB3L1 ||11p11.2 ||OASIS || ||-CREB3L1 and Kidney Cancer || 4|
|CAST ||5q15 ||BS-17, PLACK || ||-CAST and Kidney Cancer || 4|
|IL16 ||15q25.1 ||LCF, NIL16, PRIL16, prIL-16 || ||-IL16 and Kidney Cancer || 4|
|MT1G ||16q13 ||MT1, MT1K || ||-MT1G and Kidney Cancer || 4|
|ENO1 ||1p36.23 ||NNE, PPH, MPB1, ENO1L1, HEL-S-17 || ||-ENO1 and Kidney Cancer || 4|
|SEMA3B ||3p21.31 ||SemA, SEMA5, SEMAA, semaV, LUCA-1 || ||-SEMA3B and Kidney Cancer || 4|
|SPINT2 ||19q13.2 ||PB, Kop, HAI2, DIAR3, HAI-2 || ||-SPINT2 and Kidney Cancer || 4|
|MIRLET7I ||12q14.1 ||LET7I, let-7i, MIRNLET7I, hsa-let-7i || ||-MicroRNA let-7i and Kidney Cancer || 3|
|HCK ||20q11.21 ||JTK9, p59Hck, p61Hck || ||-HCK and Kidney Cancer || 3|
|CXCL11 ||4q21.1 ||IP9, H174, IP-9, b-R1, I-TAC, SCYB11, SCYB9B || ||-CXCL11 and Kidney Cancer || 3|
|NOX4 ||11q14.3 ||KOX, KOX-1, RENOX || ||-NOX4 and Kidney Cancer || 3|
|ITGB2 ||21q22.3 ||LAD, CD18, MF17, MFI7, LCAMB, LFA-1, MAC-1 || ||-ITGB2 and Renal Cell Carcinoma || 3|
|CAV2 ||7q31.2 ||CAV || ||-CAV2 and Kidney Cancer || 3|
|PKHD1 ||6p12.3-p12.2 ||FPC, FCYT, ARPKD, TIGM1 || ||-PKHD1 and Renal Cell Carcinoma || 3|
|LRRC3B ||3p24.1 ||LRP15 || ||-LRRC3B and Kidney Cancer || 3|
|CALCA ||11p15.2 ||CT, KC, PCT, CGRP, CALC1, CGRP1, CGRP-I || ||-CALCA and Kidney Cancer || 3|
|BNIP3L ||8p21.2 ||NIX, BNIP3a || ||-BNIP3L and Kidney Cancer || 3|
|KRT19 ||17q21.2 ||K19, CK19, K1CS || ||-KRT19 and Kidney Cancer || 3|
|CCN5 ||20q13.12 ||CT58, WISP2, CTGF-L || ||-WISP2 and Kidney Cancer || 2|
|TPM1 ||15q22.2 ||CMH3, TMSA, CMD1Y, LVNC9, C15orf13, HEL-S-265, HTM-alpha || ||-TPM1 and Renal Cell Carcinoma || 2|
|SLC34A2 ||4p15.2 ||NPTIIb, NAPI-3B, NAPI-IIb || ||-SLC34A2 and Kidney Cancer || 2|
|RARRES3 ||11q12.3 ||RIG1, TIG3, HRSL4, HRASLS4, PLA1/2-3 || ||-RARRES3 and Kidney Cancer || 2|
|IGF2-AS ||11p15.5 ||PEG8, IGF2AS, IGF2-AS1 || ||-IGF2-AS and Kidney Cancer || 2|
|ARID2 ||12q12 ||p200, BAF200 || ||-ARID2 and Kidney Cancer || 2|
|CTDSPL ||3p22.2 ||PSR1, SCP3, HYA22, RBSP3, C3orf8 || ||-CTDSPL and Kidney Cancer || 2|
|VCAM1 ||1p21.2 ||CD106, INCAM-100 || ||-VCAM1 and Kidney Cancer || 2|
|SLC22A18 ||11p15.4 ||HET, ITM, BWR1A, IMPT1, TSSC5, ORCTL2, BWSCR1A, SLC22A1L, p45-BWR1A || ||-SLC22A18 and Kidney Cancer || 2|
|IFNA17 ||9p21.3 ||IFNA, INFA, LEIF2C1, IFN-alphaI || ||-IFNA17 and Kidney Cancer || 1|
|PECAM1 ||17q23.3 ||CD31, PECA1, GPIIA', PECAM-1, endoCAM, CD31/EndoCAM || ||-PECAM1 and Kidney Cancer || 1|
|HMGA1 ||6p21.31 ||HMG-R, HMGIY, HMGA1A || ||-HMGA1 and Kidney Cancer || 1|
|KLLN ||10q23.31 ||CWS4, KILLIN || ||-KLLN and Kidney Cancer || 1|
|IFNA2 ||9p21.3 ||IFNA, INFA2, IFNA2B, IFN-alphaA || ||-IFNA2 and Kidney Cancer || 1|
|MIR106A ||Xq26.2 ||mir-106, MIRN106A, mir-106a || ||-MIR106A and Kidney Cancer || 1|
|PDGFRL ||8p22 ||PDGRL, PRLTS || ||-PDGFRL and Kidney Cancer || 1|
|MIR10B ||2q31.1 ||MIRN10B, mir-10b, miRNA10B, hsa-mir-10b || ||-MIR10B and Kidney Cancer || 1|
|IFNA7 ||9p21.3 ||IFNA-J, IFN-alphaJ || ||-IFNA7 and Kidney Cancer || |
|FHIT ||3p14.2 ||FRA3B, AP3Aase ||Translocation ||-t(3;8)(p14.2;q24.1) in Hereditary Renal Cell Carcinoma || |
|TFE3 ||Xp11.23 ||TFEA, RCCP2, RCCX1, bHLHe33 ||Translocation ||-t(X;1)(p11;q21) in Papillary Renal Cell Carcinoma || |
Note: list is not exhaustive. Number of papers are based on searches of PubMed (click on topic title for arbitrary criteria used).
Recurrent Structural Abnormalities
Selected list of common recurrent structural abnormalities
This is a highly selective list aiming to capture structural abnormalies which are frequesnt and/or significant in relation to diagnosis, prognosis, and/or characterising specific cancers. For a much more extensive list see the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer.
Grünwald V, Doehn C, Goebell PJ[Molecular tumor board-renal cell carcinoma].
Urologe A. 2019; 58(7):768-773 [PubMed
] Related Publications
The introduction of molecular targeted agents has fundamentally changed the treatment of metastatic renal cell carcinoma. A first wave of development was based on the improved understanding of tumor biology since the discovery of the importance of the von Hippel-Lindau gene as the key driver of the disease and paved the way for antiangiogenic agents. Of relevance is the overexpression of proangiogenic and proliferation-promoting factors (VEGF, vascular endothelial growth factor; PDGF, platelet-derived growth factor) as well as an overactivation of the PI3K-Akt signaling pathway: the target structure is the "mammalian target of rapamycin" (mTOR) molecule, which is involved in the regulation of cell proliferative processes. VEGF-, PDGF-, and mTOR-signals and signaling pathways are central targets of current targeted substances. A second wave is certainly to be seen in the development of therapeutic approaches with the targeted activation and modulation of the immune system, which has brought "immunotherapy" back into the focus of interest. Central development is the application of immune-checkpoint inhibitors, with the help of which (re-)activation of the cellular defense, especially of T cells, takes place, which per se holds the potential of a cytoreductive therapy by killing the tumor cells. Even though the prognosis has improved significantly due to the rapid development of recent years, treatment remains challenging as most patients experience progress, and long-term survival is only achieved in about 20% of cases because some patients are primarily refractory or do not respond. The more intensive interlocking of molecular biology, pathology, clinical research, and interdisciplinary uro-oncology, as is the claim of molecular tumor boards, can contribute to the individual selection of a suitable therapy strategy and, thus, establish the latest findings and developments for the benefit of patients in the clinic.
The formation and maintenance of renal cell carcinomas (RCC) involve many cell types, such as cancer stem and differentiated cells, endothelial cells, fibroblasts and immune cells. These all contribute to the creation of a favorable tumor microenvironment to promote tumor growth and metastasis. Extracellular vesicles (EVs) are considered to be efficient messengers that facilitate the exchange of information within the different tumor cell types. Indeed, tumor EVs display features of their originating cells and force recipient cells towards a pro-tumorigenic phenotype. This review summarizes the recent knowledge related to the biological role of EVs, shed by renal tumor cells and renal cancer stem cells in different aspects of RCC progression, such as angiogenesis, immune escape and tumor growth. Moreover, a specific role for renal cancer stem cell derived EVs is described in the formation of the pre-metastatic niche. We also highlight the tumor EV cargo, especially the oncogenic miRNAs, which are involved in these processes. Finally, the circulating miRNAs appear to be a promising source of biomarkers in RCC.
Xp11.2 translocation renal cell carcinoma (Xp11 tRCC) is a rare sporadic pediatric kidney cancer caused by constitutively active TFE3 fusion proteins. Tumors in patients with Xp11 tRCC tend to recur and undergo frequent metastasis, in part due to lack of methods available to detect early-stage disease. Here we generated transgenic (Tg) mice overexpressing the human PRCC-TFE3 fusion gene in renal tubular epithelial cells, as an Xp11 tRCC mouse model. At 20 weeks of age, mice showed no histological abnormalities in kidney but by 40 weeks showed Xp11 tRCC development and related morphological and histological changes. MicroRNA (miR)-204-5p levels in urinary exosomes of 40-week-old Tg mice showing tRCC were significantly elevated compared with levels in control mice. MicroRNA-204-5p expression also significantly increased in primary renal cell carcinoma cell lines established both from Tg mouse tumors and from tumor tissue from 2 Xp11 tRCC patients. All of these lines secreted miR-204-5p-containing exosomes. Notably, we also observed increased miR-204-5p levels in urinary exosomes in 20-week-old renal PRCC-TFE3 Tg mice prior to tRCC development, and those levels were equivalent to those in 40-week-old Tg mice, suggesting that miR-204-5p increases follow expression of constitutively active TFE3 fusion proteins in renal tubular epithelial cells prior to overt tRCC development. Finally, we confirmed that miR-204-5p expression significantly increases in noncancerous human kidney cells after overexpression of a PRCC-TFE3 fusion gene. These findings suggest that miR-204-5p in urinary exosomes could be a useful biomarker for early diagnosis of patients with Xp11 tRCC.
Maleckaite R, Zalimas A, Bakavicius A, et al.DNA methylation of metallothionein genes is associated with the clinical features of renal cell carcinoma.
Oncol Rep. 2019; 41(6):3535-3544 [PubMed
] Related Publications
Metallothioneins are low‑weight cysteine‑rich proteins responsible for metal ion homeostasis in a cell and, thus, capable of regulating cell proliferation and differentiation. Deregulation of metallothionein genes has been reported in various human tumors. However, their role in renal cell carcinoma (RCC) has been poorly investigated. In the present study, we aimed to evaluate the importance of promoter DNA methylation of selected metallothionein genes for RCC. Based on the initial analysis of kidney renal clear cell carcinoma dataset from The Cancer Genome Atlas, genes MT1E, MT1F, MT1G and MT1M were selected for qualitative methylation analysis in 30 tumors (including 10 multifocal cases), 10 pericancerous, and 30 non‑cancerous renal tissues (NRT). Methylation of MT1E and MT1M was tumor‑specific (P=0.0056 and P=0.0486, respectively) and showed moderate interfocal variation in paired tumor foci. Methylated promoter status of the two genes was associated with larger tumor size (P=0.0110 and P=0.0156, respectively). Furthermore, aberrant MT1E methylation was more frequent in tumors having necrotic zones (P=0.0449) or characterized with higher differentiation grade (P=0.0144), while MT1M was more commonly methylated in tumors with higher Fuhrman grade (P=0.0272). Only unmethylated MT1F promoter status was observed in all analyzed samples. Gene expression analysis (51 RCC and 9 NRT) revealed MT1G downregulation in tumors (P<0.0001), while lower MT1E expression levels were associated with the promoter methylation (P=0.0077). In clear cell RCC, MT1E, MT1G and MT1M expression was higher than that noted in other histological tumor subtypes (all P<0.0500). In addition, some associations were observed between metabolic syndrome‑related clinical parameters and promoter methylation or gene expression. In conclusion, the present study revealed the potential role of MT1E and MT1M promoter methylation in RCC development.
BACKGROUND: In clinical practice, the detection of biomarkers is mostly based on primary tumors for its convenience in acquisition. However, immune checkpoints may express differently between primary and metastatic tumor. Therefore, we aimed to compare the differential expressions of PD-1, PD-L1 and PD-L2 between the primary and metastatic sites of renal cell carcinoma (RCC).
METHODS: Patients diagnosed with RCC by resection or fine needle aspiration of metastasis were included. Immunohistochemistry (IHC) was applied to detect PD-1, PD-L1 and PD-L2 expressions. SPSS 22.0 was applied to conduct Chi-square, consistency tests and Cox's proportional hazards regression models. GraphPad Prism 6 was used to plot survival curves and R software was used to calculate Predictive accuracy (PA).
RESULTS: In the whole cohort (N = 163), IHC results suggested a higher detection rate of PD-L1 in the metastasis than that of the primary site (χ2 = 4.66, p = 0.03), with a low consistent rate of 32.5%. Among different metastatic tumors, PD-1 was highly expressed in the lung/lymph node (65.3%) and poorly expressed in the brain (10.5%) and visceral metastases (12.5%). PD-L1 was highly expressed in lung/lymph node (37.5%) and the bone metastases (12.2%) on the contrary. In terms of survival analysis, patients with PD-1 expression either in the primary or metastasis had a shorter overall survival (OS) (HR: 1.59, 95% CI 1.08-2.36, p = 0.02). Also, PD-L1 expression in the primary was associated with a shorter OS (HR 2.55, 95% CI 1.06-6.15, p = 0.04). In the multivariate analysis, the predictive accuracy of the whole model for PFS was increased from 0.683 to 0.699 after adding PD-1.
CONCLUSION: PD-1, PD-L1 and PD-L2 were differentially expressed between primary and metastatic tumors. Histopathological examination of these immune check points in metastatic lesions of mRCC should be noticed, and its accurate diagnosis may be one of the effective ways to realize the individualized treatment.
Chen J, Chen J, He F, et al.Design of a Targeted Sequencing Assay to Detect Rare Mutations in Circulating Tumor DNA.
Genet Test Mol Biomarkers. 2019; 23(4):264-269 [PubMed
] Related Publications
BACKGROUND: Qualitative and quantitative detection of circulating tumor DNA (ctDNA) is a liquid biopsy technology used for early cancer diagnosis. However, the plasma ctDNA content is extremely low, so it is difficult to detect somatic mutations of tumors using conventional sequencing methods. Target region sequencing (TRS) technology, through enrichment of the target genomic region followed by next generation sequencing, overcomes this challenge and has been widely used in ctDNA sequencing.
METHODS: We designed a ctDNA sequencing panel to capture 128 tumor genes, and tested the performance of the panel by running TRS for ctDNA of a clear cell renal cell carcinoma (ccRCC) patient and 12 breast cancer patients.
RESULTS: TRS using the new ctDNA panel at more than 500 × coverage depth achieved almost the same accuracy as traditional whole-exome sequencing (WES). PBRM1 p.L641V was detected in the plasma sample of the ccRCC patient with an allele frequency of 0.2%. The ctDNA of 12 breast cancer patients was sequenced at a depth of 500-fold, achieving 99.89% coverage; 34 genes were detected with mutations, including the drug target genes BRCA2, PTEN, TP53, APC, KDR, and NOTCH2.
CONCLUSIONS: This TRS new ctDNA panel can be used to detect mutations in cell-free DNA from multiple types of cancer.
BACKGROUND: Clear cell renal cell carcinoma (CCRCC) is characterized by a highly metastatic potential. The stromal communication between stem cells and cancer cells critically influences metastatic dissemination of cancer cells.
METHODS: The effect of exosomes isolated from cancer stem cells (CSCs) of CCRCC patients on the progress of epithelial-mesenchymal transition (EMT) and lung metastasis of CCRCC cells were examined.
RESULTS: CSCs exosomes promoted proliferation of CCRCC cells and accelerated the progress of EMT. Bioactive miR-19b-3p transmitted to cancer cells by CSC exosomes induced EMT via repressing the expression of PTEN. CSCs exosomes derived from CCRCC patients with lung metastasis produced the strongest promoting effect on EMT. Notably, CD103
CONCLUSIONS: CSC exosomes transported miR-19b-3p into CCRCC cells and initiated EMT promoting metastasis. CD103
Although transforming growth factor beta (TGF-β) is known to be involved in the pathogenesis and progression of many cancers, its role in renal cancer has not been fully investigated. In the present study, we examined the role of TGF-β in clear cell renal carcinoma (ccRCC) progression in vitro and in vivo. First, expression levels of TGF-β signaling pathway components were examined. Microarray and immunohistochemical analyses showed that the expression of c-Ski, a transcriptional corepressor of Smad-dependent TGF-β and bone morphogenetic protein (BMP) signaling, was higher in ccRCC tissues than in normal renal tissues. Next, a functional analysis of c-Ski effects was carried out. Bioluminescence imaging of renal orthotopic tumor models demonstrated that overexpression of c-Ski in human ccRCC cells promoted in vivo tumor formation. Enhancement of tumor formation was also reproduced by the introduction of a dominant-negative mutant TGF-β type II receptor into ccRCC cells. In contrast, introduction of the BMP signaling inhibitor Noggin failed to accelerate tumor formation, suggesting that the tumor-promoting effect of c-Ski depends on the inhibition of TGF-β signaling rather than of BMP signaling. Finally, the molecular mechanism of the tumor-suppressive role of TGF-β was assessed. Although TGF-β signaling did not affect tumor angiogenesis, apoptosis of ccRCC cells was induced by TGF-β. Taken together, these findings suggest that c-Ski suppresses TGF-β signaling in ccRCC cells, which, in turn, attenuates the tumor-suppressive effect of TGF-β.
Luo Q, Cui M, Deng Q, Liu JComprehensive analysis of differentially expressed profiles and reconstruction of a competing endogenous RNA network in papillary renal cell carcinoma.
Mol Med Rep. 2019; 19(6):4685-4696 [PubMed
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Long noncoding RNAs (lncRNAs) function as competing endogenous RNAs (ceRNAs). ceRNA networks may serve important roles in various tumors, as demonstrated by an increasing number of studies; however, papillary renal cell carcinoma (PRCC)‑associated ceRNA networks mediated by lncRNAs remain unknown. Increased knowledge of ceRNA networks in PRCC may aid the identification of novel targets and biomarkers in the treatment of PRCC. In the present study, a comprehensive investigation of mRNA, lncRNA, and microRNA (miRNA) expression in PRCC was conducted using sequencing data from The Cancer Genome Atlas. Differential expression (DE) profiles of mRNAs, lncRNAs and miRNAs were evaluated, with 1,970 mRNAs, 1,201 lncRNAs and 96 miRNAs identified as genes with significantly different expression between PRCC and control paracancerous tissues. Based on the identified DEmRNAs, a protein‑protein interaction network was generated using the STRING database. Furthermore, a ceRNA network for PRCC was determined using a targeted assay combined with the DE of miRNAs, mRNAs and lncRNAs, enabling the identification of important lncRNA‑miRNA and miRNA‑mRNA pairs. Analysis of the ceRNA network led to the extraction of a subnetwork and the identification of lncRNA maternally expressed 3 (MEG3), lncRNA PWRN1, miRNA (miR)‑508, miR‑21 and miR519 as important genes. Reverse transcription‑quantitative polymerase chain reaction analysis was conducted to validate the results of the bioinformatics analyses; it was revealed that lncRNA MEG3 expression levels were downregulated in PRCC tumor tissues compared with adjacent non‑tumor tissues. In addition, survival analysis was conducted to investigate the association between identified genes and the prognosis of patients with PRCC, indicating the potential involvement of 13 mRNAs, 15 lncRNAs and six miRNAs. In conclusion, the present study may improve understanding of the regulatory mechanisms of ceRNA networks in PRCC and provide novel insight for future studies of prognostic biomarkers and potential therapeutic targets.
Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumor. Tumor recurrence and metastasis is the primary cause of cancer‑associated mortality in patients with ccRCC. Therefore, identification of efficient diagnostic and prognostic molecular markers may improve survival times. The GSE46699, GSE36895, GSE53000 and GSE53757 gene datasets were downloaded from the Gene Expression Omnibus database and contained 196 ccRCC samples and 164 adjacent normal kidney samples. Bioinformatics analysis was used to integrate the four microarray datasets to identify and analyze differentially expressed genes. Functional analysis revealed that there were 12 genes associated with cancer, based on the tumor‑associated gene database. Erb‑B2 receptor tyrosine kinase 4, centrosomal protein 55 (CEP55) and vascular endothelial growth factor A are oncogenes, all of which were associated with tumor stage, whereas only CEP55 was significantly associated with survival time as determined by Gene Expression Profiling Interactive Analysis. The mRNA expression levels of CEP55 in ccRCC samples were significantly higher than those observed in adjacent normal kidney tissues based on The Cancer Genome Atlas data and reverse transcription‑polymerase chain reaction results. The receiver operating characteristic curve analysis revealed that CEP55 may be considered a diagnostic biomarker for ccRCC with an area under the curve of >0.85 in the training and validation sets. High CEP55 expression was strongly associated with sex, histological grade, stage, T classification, N classification and M classification. Univariate and multivariate Cox proportional hazards analyses demonstrated that CEP55 expression was an independent risk factor for poor prognosis. In addition, gene set enrichment analysis indicated that high CEP55 expression was associated with immunization, cell adhesion, inflammation, the Janus kinase/signal transducer and activator of transcription signaling pathway and cell proliferation. In conclusion, CEP55 was increased in ccRCC samples, and may be considered a potential diagnostic and prognostic biomarker for ccRCC.
Clear cell renal cell carcinoma (ccRCC) was the most aggressive histological type of renal cell carcinoma (RCC) and accounted for 70-80% of cases of all RCC. The aim of this study was to identify the potential biomarker in ccRCC and explore their underlying mechanisms. Four profile datasets were downloaded from the GEO database to identify DEGs. GO and KEGG analysis of DEGs were performed by DAVID. A protein-protein interaction (PPI) network was constructed to predict hub genes. The hub gene expression within ccRCC across multiple datasets and the overall survival analysis were investigated utilizing the Oncomine Platform and UALCAN dataset, separately. A meta-analysis was performed to explore the relationship between the hub genes: EGFR and ccRCC. 127 DEGs (55 upregulated genes and 72 downregulated genes) were identified from four profile datasets. Integrating the result from PPI network, Oncomine Platform, and survival analysis, EGFR, FLT1, and EDN1 were screened as key factors in the prognosis of ccRCC. GO and KEGG analysis revealed that 127 DEGs were mainly enriched in 21 terms and 4 pathways. The meta-analysis showed that there was a significant difference of EGFR expression between ccRCC tissues and normal tissues, and the expression of EGFR in patients with metastasis was higher. This study identified 3 importance genes (EGFR, FLT1, and EDN1) in ccRCC, and EGFR may be a potential prognostic biomarker and novel therapeutic target for ccRCC, especially patients with metastasis.
BACKGROUND: Large-scale genetic screening using CRISPR-Cas9 technology has emerged as a powerful approach to uncover and validate gene functions. The ability to control the timing of genetic perturbation during CRISPR screens will facilitate precise dissection of dynamic and complex biological processes. Here, we report the optimization of a drug-inducible CRISPR-Cas9 system that allows high-throughput gene interrogation with a temporal control.
RESULTS: We designed multiple drug-inducible sgRNA expression vectors and measured their activities using an EGFP gene disruption assay in 11 human and mouse cell lines. The optimal design allows for a tight and inducible control of gene knockout in vitro, and in vivo during a seven-week-long experiment following hematopoietic reconstitution in mice. We next performed parallel genome-wide loss-of-function screens using the inducible and constitutive CRISPR-Cas9 systems. In proliferation-based dropout screens, these two approaches have similar performance in discriminating essential and nonessential genes. In a more challenging phenotypic assay that requires cytokine stimulation and cell staining, we observed similar sensitivity of the constitutive and drug-induced screening approaches in detecting known hits. Importantly, we demonstrate minimal leakiness of our inducible CRISPR screening platforms in the absence of chemical inducers in large-scale settings.
CONCLUSIONS: In this study, we have developed a drug-inducible CRISPR-Cas9 system that shows high cleavage efficiency upon induction but low background activity. Using this system, we have achieved inducible gene disruption in a wide range of cell types both in vitro and in vivo. For the first time, we present a systematic side-by-side comparison of constitutive and drug-inducible CRISPR-Cas9 platforms in large-scale functional screens. We demonstrate the tightness and efficiency of our drug-inducible CRISPR-Cas9 system in genome-wide pooled screening. Our design increases the versatility of CRISPR-based genetic screening and represents a significant upgrade on existing functional genomics toolbox.
The genetic landscape of clear cell renal cell carcinoma (ccRCC) had been investigated extensively but its evolution patterns remained unclear. Here we analyze the clonal architectures of 473 patients from three different populations. We find that the mutational signatures vary substantially across different populations and evolution stages. The evolution patterns of ccRCC have great inter-patient heterogeneities, with del(3p) being regarded as the common earliest event followed by three early departure points: VHL and PBRM1 mutations, del(14q) and other somatic copy number alterations (SCNAs) including amp(7), del(1p) and del(6q). We identify three prognostic subtypes of ccRCC with distinct clonal architectures and immune infiltrates: long-lived patients, enriched with VHL but depleted of BAP1 mutations, have high levels of Th17 and CD8
Transcriptional networks are critical for the establishment of tissue-specific cellular states in health and disease, including cancer. Yet, the transcriptional circuits that control carcinogenesis remain poorly understood. Here we report that Kruppel like factor 6 (KLF6), a transcription factor of the zinc finger family, regulates lipid homeostasis in clear cell renal cell carcinoma (ccRCC). We show that KLF6 supports the expression of lipid metabolism genes and promotes the expression of PDGFB, which activates mTOR signalling and the downstream lipid metabolism regulators SREBF1 and SREBF2. KLF6 expression is driven by a robust super enhancer that integrates signals from multiple pathways, including the ccRCC-initiating VHL-HIF2A pathway. These results suggest an underlying mechanism for high mTOR activity in ccRCC cells. More generally, the link between super enhancer-driven transcriptional networks and essential metabolic pathways may provide clues to the mechanisms that maintain the stability of cell identity-defining transcriptional programmes in cancer.
AIM: To characterize personal driver genes in clear cell renal cell carcinoma independent of somatic mutation frequencies.
METHODS: Personal cancer driver genes were predicted by Integrated CAncer GEnome Score in 417 patients with clear cell renal cell carcinoma using 26 786 somatic mutations from The Cancer Genome Atlas, followed by an integrated investigation on personal driver genes.
RESULTS: A total of 233 personal driver genes were determined by Integrated CAncer GEnome Score. The coexpression network analysis found 5 coexpressed modules. The blue module was significantly negatively correlated with all 5 clinical features, including cancer stage, lymph node metastasis, distant metastasis, age, and survival status (death). CTNNB1, TGFBR2, KDR, FLT1, and INSR were the hub genes in the blue module. The expression of 79 personal driver genes was significantly associated with clinical outcomes of patients with clear cell renal cell carcinoma.
CONCLUSIONS: The set of personal driver genes sheds insights into the tumorigenesis of clear cell renal cell carcinoma and paves the way for developing personalized medicine for clear cell renal cell carcinoma.
Yin X, Wang B, Gan W, et al.TFE3 fusions escape from controlling of mTOR signaling pathway and accumulate in the nucleus promoting genes expression in Xp11.2 translocation renal cell carcinomas.
J Exp Clin Cancer Res. 2019; 38(1):119 [PubMed
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BACKGROUND: Xp11.2 translocation renal cell carcinoma (tRCC) is mainly caused by translocation of the TFE3 gene located on chromosome Xp11.2 and is characterized by overexpression of the TFE3 fusion gene. Patients are diagnosed with tRCC usually before 45 years of age with poor prognosis. We investigated this disease using two tRCC cell lines, UOK109 and UOK120, in this study.
METHODS: The purpose of this study was to investigate the pathogenic mechanism of TFE3 fusions in tRCC based on its subcellular localization, nuclear translocation and transcriptional activity. The expression of TFE3 fusions and other related genes were analyzed by quantitative reverse transcription PCR (qRT-PCR) and Western blot. The subcellular localization of TFE3 was determined using immunofluorescence. The transcriptional activity of TFE3 fusions was measured using a luciferase reporter assay and ChIP analysis. In some experiments, TFE3 fusions were depleted by RNAi or gene knockdown. The TFE3 fusion segments were cloned into a plasmid expression system for expression in cells.
RESULTS: Our results demonstrated that TFE3 fusions were overexpressed in tRCC with a strong nuclear retention irrespective of treatment with an mTORC1 inhibitor or not. TFE3 fusions lost its co-localization with lysosomal proteins and decreased its interaction with the chaperone 14-3-3 proteins in UOK109 and UOK120 cells. However, the fusion segments of TFE3 could not translocate to the nucleus and inhibition of Gsk3β could increase the cytoplasmic retention of TFE3 fusions. Both the luciferase reporter assay and ChIP analysis demonstrated that TFE3 fusions could bind to the promoters of the target genes as a wild-type TFE3 protein. Knockdown of TFE3 results in decreased expression of those genes responsible for lysosomal biogenesis and other target genes. The ChIP-seq data further verified that, in addition to lysosomal genes, TFE3 fusions could regulate genes involved in cellular responses to hypoxic stress and transcription.
CONCLUSIONS: Our results indicated that the overexpressed TFE3 fusions were capable of escaping from the control by the mTOR signaling pathway and were accumulated in the nucleus in UOK109 and UOK120 cells. The nuclear retention of TFE3 fusions promoted the expression of lysosomal genes and other target genes, facilitating cancer cell resistance against an extreme environment.
Circular RNA (circRNA) is a group of RNA families generated by RNA circularization, which was discovered ubiquitously across different cancers. However, the internal structure of circRNA is difficult to determine due to alternative splicing that occurs in its exons and introns. Furthermore, cancer-specific alternative splicing of circRNA is less likely to be identified. Here, we proposed a de novo algorithm, CircSplice, that could identify internal alternative splicing in circRNA and compare differential circRNA splicing events between different conditions ( http://gb.whu.edu.cn/CircSplice or https://github.com/GeneFeng/CircSplice ). By applying CircSplice in clear cell renal cell carcinoma and bladder cancer, we detected 4498 and 2977 circRNA alternative splicing (circ-AS) events in the two datasets respectively and confirmed the expression of circ-AS events by RT-PCR. We further inspected the distributions and patterns of circ-AS in cancer and adjacent normal tissues. To further understand the potential functions of cancer-specific circ-AS, we classified those events into tumor suppressors and oncogenes and performed pathway enrichment analysis. This study is the first comprehensive view of cancer-specific circRNA alternative splicing, which could contribute significantly to regulation and functional research of circRNAs in cancers.
Dong D, Mu Z, Wei N, et al.Long non-coding RNA ZFAS1 promotes proliferation and metastasis of clear cell renal cell carcinoma via targeting miR-10a/SKA1 pathway.
Biomed Pharmacother. 2019; 111:917-925 [PubMed
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BACKGROUND: LncRNA ZFAS1 (ZNFX1 antisense RNA1) plays key roles in the occurrence and progression of various cancers, including colorectal cancer, glioma, lung cancer, gastric cancer, and so on. To date, relatively little is known about its potential role in development and/or progression of clear cell renal cell carcinoma (ccRCC).
METHODS: Expression level of ZFAS1 and miR-10a in 60 ccRCC and 20 adjacent non-tumor tissues were determined by using qRT-PCR. The effect of knockdown of ZFAS1 on cell proliferation, migration and invasion were measured by CCK-8 assay, transwell migration and invasion assay, respectively. The correlation of ZFAS1 and miR-10a was analyzed by bioinformatics DIANA TOOLS. Protein and mRNA expression of spindle and kinetochore-associated protein 1(SKA1) were determined by western blot and qRT-PCR analysis, respectively. Interactions between ZFAS1 and miR-10a were verified by luciferase reporter assay and RNA immunoprecipitation (RIP) assay, and interactions between miR-10a and SKA1 was verified by a luciferase reporter assay.
RESULTS: We observed that high-level expression of ZFAS1 is positively correlated with poor prognosis and shorter overall survival (OS) in patients with ccRCC. Knockdown of ZFAS1 significantly suppressed proliferation, migration and invasion of ccRCC cells. Furthermore, miR-10a was identified as a target of ZFAS1. Silencing miR-10a could attenuate the ability of ZFAS1 in promoting proliferation and metastasis of ccRCC. Subsequently, our studies validated that SKA1, as a key downstream target protein for miR-10a, is responsible for the biological role of ZFAS1. ZFAS1 positively regulated SKA1 expression via sponging miR-10a.
CONCLUSIONS: Taken together, our findings suggest that ZFAS1 promotes growth and metastasis of ccRCC via targeting miR-10a/SKA1 pathway, which may represent a novel therapeutic target or biomarker for ccRCC.
Papillary renal cell carcinoma (PRCC) accounts for 15‑20% of all kidney neoplasms and continually attracts attention due to the increase in the incidents in which it occurs. The molecular mechanism of PRCC remains unclear and the efficacy of drugs that treat PRCC lacks sufficient evidence in clinical trials. Therefore, it is necessary to investigate the underlying mechanism in the development of PRCC and identify additional potential anti‑PRCC drugs for its treatment. The differently expressed genes (DEGs) of PRCC were identified, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional annotation. Then, potential drugs for PRCC treatment were predicted by Connectivity Map (Cmap) based on DEGs. Furthermore, the latent function of query drugs in PRCC was explored by integrating drug‑target, drug‑pathway and drug‑protein interactions. In total, 627 genes were screened as DEGs, and these DEGs were annotated using KEGG pathway analyses and were clearly associated with the complement and coagulation cascades, amongst others. Then, 60 candidate drugs, as predicted based on DEGs, were obtained from the Cmap database. Vorinostat was considered as the most promising drug for detailed discussion. Following protein‑protein interaction (PPI) analysis and molecular docking, vorinostat was observed to interact with C3 and ANXN1 proteins, which are the upregulated hub genes and may serve as oncologic therapeutic targets in PRCC. Among the top 20 metabolic pathways, several significant pathways, such as complement and coagulation cascades and cell adhesion molecules, may greatly contribute to the development and progression of PRCC. Following the performance of the PPI network and molecular docking tests, vorinostat exhibited a considerable and promising application in PRCC treatment by targeting C3 and ANXN1.
Basic helix‑loop‑helix family member e41 (BHLHE41) serves an important role in regulating cell differentiation, circadian rhythms and the response to hypoxia. However, the roles of BHLHE41 in clear cell renal cell carcinoma (ccRCC) remain unclear. The aim of the present study was to analyze the expression of BHLHE41 in ccRCC and investigate the effect of downregulated BHLHE41 on the growth and migration of ccRCC cells. The expression of BHLHE41 in ccRCC was demonstrated to be significantly increased in gene expression microarray datasets and RNA sequencing data. Reverse transcription‑quantitative polymerase chain reaction and western blot analysis demonstrated that BHLHE41 expression in fresh ccRCC tissues was increased, compared with than their adjacent non‑tumorous controls. BHLHE41 knockdown significantly reduced cell proliferation and migration of A498 and CAKI‑1 cells. For the investigation of the molecules mediated by BHLHE41, immunoblotting analyses revealed that phosphorylation of p70S6K and protein levels of E‑cadherin were reduced. Additionally, a lower frequency methylation was determined in the BHLHE41 3'‑untranslated region through The Cancer Genome Atlas dataset analysis for the first time. These observations demonstrated that BHLHE41 could be a biomarker and an oncogene for ccRCC.
Osako Y, Yoshino H, Sakaguchi T, et al.Potential tumor‑suppressive role of microRNA‑99a‑3p in sunitinib‑resistant renal cell carcinoma cells through the regulation of RRM2.
Int J Oncol. 2019; 54(5):1759-1770 [PubMed
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Sunitinib is the most common primary molecular‑targeted agent for metastatic clear cell renal cell carcinoma (ccRCC); however, intrinsic or acquired sunitinib resistance has become a significant problem in medical practice. The present study focused on microRNA (miR)‑99a‑3p, which was significantly downregulated in clinical sunitinib‑resistant ccRCC tissues in previous screening analyses, and investigated the molecular network associated with it. The expression levels of miR‑99a‑3p and its candidate target genes were evaluated in RCC cells, including previously established sunitinib‑resistant 786‑o (SU‑R‑786‑o) cells, and clinical ccRCC tissues, using reverse transcription‑quantitative polymerase chain reaction. Gain‑of‑function studies demonstrated that miR‑99a‑3p significantly suppressed cell proliferation and colony formation in RCC cells, including the SU‑R‑786‑o cells, by inducing apoptosis. Based on in silico analyses and RNA sequencing data, followed by luciferase reporter assays, ribonucleotide reductase regulatory subunit‑M2 (RRM2) was identified as a direct target of miR‑99a‑3p in the SU‑R‑786‑o cells. Loss‑of‑function studies using small interfering RNA against RRM2 revealed that cell proliferation and colony growth were significantly inhibited via induction of apoptosis, particularly in the SU‑R‑786‑o cells. Furthermore, the RRM2 inhibitor Didox (3,4‑dihydroxybenzohydroxamic acid) exhibited anticancer effects in the SU‑R‑786‑o cells and other RCC cells. To the best of our knowledge, this is the first report demonstrating that miR‑99a‑3p directly regulates RRM2. Identifying novel genes targeted by tumor‑suppressive miR‑99a‑3p in sunitinib‑resistant RCC cells may improve our understanding of intrinsic or acquired resistance and facilitate the development of novel therapeutic strategies.
Zhang B, Wu Q, Wang Z, et al.The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high-throughput data.
Mol Genet Genomic Med. 2019; 7(5):e607 [PubMed
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BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC.
METHODS: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC.
RESULTS: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression.
CONCLUSIONS: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.
Al-Maghrabi J, Mufti S, Gomaa WThe incidence of renal cell carcinoma associated with Xp11.2 translocation/TFE3 gene fusion in Saudi adult patients with renal cancer: a retrospective tissue microarray analysis.
Pol J Pathol. 2018; 69(4):376-383 [PubMed
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Renal cell carcinoma (RCC) is the most common renal tumour. RCC with Xp11.2 translocation/TFE3 (transcription factor E3) gene fusions (Xp11.2 RCC) is positive for immunostain labelling by TFE3 antibody. This tumour is rarely described in adults. This study aims to evaluate the frequency of RCC with Xp11.2 in a subset of Saudi adult patients with RCC. 112 RCCs diagnosed in 1995-2016 were retrieved from the Department of Pathology at King Abdulaziz University and King and Faisal Specialist Hospital and Research Centre, Saudi Arabia. Tissue microarrays were constructed and TFE3 immunostaining was performed. TFE3 immunostaining was considered positive when diffuse strong nuclear immunostaining was detected. TFE3 immunostaining-positive tumours were confirmed by fluorescence in situ hybridisation. 4.5% of RCCs were shown to be Xp11.2 RCC by TFE3 immunostaining. TFE3-positive tumours have a papillary configuration, nested pattern, or both. Positive tumours show male predominance, more occurrences in middle age, high grade, and large-sized tumours with necrosis. Two tumours were FISH-positive. Xp11.2 RCC is rare in Saudi adult patients. Xp11.2 RCCs tend to be large sized and higher grade. TFE3 immunostaining should be considered in RCC that are histologically suggestive to confirm the diagnosis of Xp11.2.
Li H, Pan X, Gui Y, et al.Upregulation of miR-183-5p predicts worse survival in patients with renal cell cancer after surgery.
Cancer Biomark. 2019; 24(2):153-158 [PubMed
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OBJECTIVE: Renal cell carcinoma (RCC) is one of the most common genitourinary cancers, and advanced RCC usually leads to poor prognosis. Therefore, identifying novel biomarkers for predicting the progression and prognosis of RCC is essential. The present study aims to evaluate the clinical value of miR-183-5p in RCC development and prognosis after surgery.
MATERIALS AND METHODS: We enrolled a total of 284 patients who received partial or radical nephrectomy from April 2003 to May 2013 at a single institution. The clinical and pathological characteristics of the patients were collected, including age, gender, tumor size, tumor stage, as well as follow-up information. The expression levels of miR-183-5p of all the patients were calculated from FFPE specimens. Cox regression analyses were performed to approve the effect of miR-183-5p expression on patient survival. Kaplan-Meier method was used to analyze the patient survival curves.
RESULTS: After controlling for gender, age, tumor size and tumor stage in the multivariate analysis, we found that high expression of miR-183-5p was independently associated lower overall survival (HR = 0.550, 95% CI = 0.364-0.832, p= 0.005). The Kaplan-Meier analysis also showed that patients with high expression of miR-183-5p had a significantly poor prognosis (p= 0.006). These results was verified by analyzing the data of 506 cases from The Cancer Genome Atlas database (TCGA).
CONCLUSION: Our results indicated that the high miR-183-5p expression is an independent factor for predicting RCC's worse prognosis.
BACKGROUND: Identification of genetic factors causing predisposition to renal cell carcinoma has helped improve screening, early detection, and patient survival.
METHODS: We report the characterization of a proband with renal and thyroid cancers and a family history of renal and other cancers by whole-exome sequencing (WES), coupled with WES analysis of germline DNA from additional affected and unaffected family members.
RESULTS: This work identified multiple predicted protein-damaging variants relevant to the pattern of inherited cancer risk. Among these, the proband and an affected brother each had a heterozygous Ala45Thr variant in SDHA, a component of the succinate dehydrogenase (SDH) complex. SDH defects are associated with mitochondrial disorders and risk for various cancers; immunochemical analysis indicated loss of SDHB protein expression in the patient's tumor, compatible with SDH deficiency. Integrated analysis of public databases and structural predictions indicated that the two affected individuals also had additional variants in genes including TGFB2, TRAP1, PARP1, and EGF, each potentially relevant to cancer risk alone or in conjunction with the SDHA variant. In addition, allelic imbalances of PARP1 and TGFB2 were detected in the tumor of the proband.
CONCLUSION: Together, these data suggest the possibility of risk associated with interaction of two or more variants.
Objective: The tumor susceptibility gene 101 (TSG101) is closely associated with various tumor types, but its role in the pathogenesis of renal cell carcinoma (RCC) is still unknown. This study used RNA interference to silence the expression of TSG101 in RCC cell lines and explore the role of TSG101 in RCC.
Methods: Immunohistochemistry and western blot were performed to detect the expression of TSG101 in 15 paired renal tumor samples. A small interfering RNA (siRNA) targeting TSG101 was transfected into A498 and 786-O cell lines. The Cell Counting Kit-8 (CCK-8) assay and colony formation assay were used to observe the changes in cell proliferation after transfection. Flow cytometry was used to detect the effect on the cell cycle. Western blot was conducted to study the changes of related functional proteins.
Results: The expression of TSG101 was higher in RCC tissues than in adjacent normal tissues. The CCK-8 assay showed that the proliferation and colony formation of the A498 and 786-O cell lines were attenuated after suppression of TSG101. Flow cytometry showed that silencing of TSG101 induced G0/G1 arrest. The western blot results revealed that the levels of cell cycle-related proteins (c-myc, cyclin E1 and cyclin-dependent kinase 2 (CDK2)) were markedly decreased in the siRNA groups.
Conclusions: TSG101 promotes proliferation of RCC cells. This positive effect on tumor growth involves activation of c-myc and cyclin E1/CDK2 and their effect on cell cycle distribution.
Wang G, Zhang ZJ, Jian WG, et al.Novel long noncoding RNA OTUD6B-AS1 indicates poor prognosis and inhibits clear cell renal cell carcinoma proliferation via the Wnt/β-catenin signaling pathway.
Mol Cancer. 2019; 18(1):15 [PubMed
] Free Access to Full Article Related Publications
BACKGROUND: The long noncoding RNA (lncRNA) OTUD6B antisense RNA 1 (OTUD6B-AS1) is oriented in an antisense direction to the protein-coding gene OTUD6B on the opposite DNA strand. TCGA database data show that the expression of the lncRNA OTUD6B-AS1 is downregulated and that OTUD6B-AS1 acts as an antioncogene in a variety of tumors. However, the expression and biological functions of the lncRNA OTUD6B-AS1 are still unknown in tumors, including clear cell renal cell carcinoma (ccRCC).
METHODS: The expression level of OTUD6B-AS1 was measured in 75 paired human ccRCC tissue and corresponding adjacent normal renal tissue samples. The correlations between the OTUD6B-AS1 expression level and clinicopathological features were evaluated using the chi-square test. The effects of OTUD6B-AS1 on ccRCC cells were determined via MTT assay, clone formation assay, transwell assay, and flow cytometry. Furthermore, the impact of OTUD6B-AS1 overexpression on the activation of the Wnt/β-catenin signaling pathway was investigated. Finally, ACHN cells with OTUD6B-AS1 overexpression were subcutaneously injected into nude mice to evaluate the influence of OTUD6B-AS1 on tumor growth in vivo.
RESULTS: In this study, we found that the expression of the lncRNA OTUD6B-AS1 was downregulated in ccRCC tissue samples and that patients with low OTUD6B-AS1 expression had shorter overall survival than patients with high OTUD6B-AS1 expression, which showed that the different expression level of OTUD6B-AS1 indirectly correlated with survival of patients. Lentivirus-mediated OTUD6B-AS1 overexpression significantly decreased the proliferation of ccRCC cells and promoted the apoptosis of the cells. Furthermore, OTUD6B-AS1 overexpression partly inhibited cell migration and invasion. The overexpression of OTUD6B-AS1 decreased the activity of the Wnt/β-catenin pathway and suppressed the expression of epithelial-to-mesenchymal transition (EMT)-related proteins (E-cadherin, N-cadherin and Snail) in ccRCC cells. In addition, compared with the parental ACHN cells, OTUD6B-AS1-overexpressing ACHN cells injected into nude mice exhibited decreased tumor growth in vivo.
CONCLUSIONS: Taken together, our findings present a road map for targeting the newly identified lncRNA OTUD6B-AS1 to suppress ccRCC progression in cell lines, and these results elucidate a novel potential therapeutic target for ccRCC treatment.
Bi-allelic inactivation of the VHL gene on chromosome 3p is the characteristic feature in most clear cell renal cell carcinomas (ccRCC). Frequent gene alterations were also identified in SETD2, BAP1 and PBRM1, all of which are situated on chromosome 3p and encode histone/chromatin regulators. The relationship between gene mutation, loss of protein expression and the correlations with clinicopathological parameters is important for the understanding of renal cancer progression. We analyzed PBRM1 and BAP1 protein expression as well as the tri-methylation state of H3K36 as a surrogate marker for SETD2 activity in more than 700 RCC samples. In ccRCC loss of nuclear PBRM1 (68%), BAP1 (40%) and H3K36me3 (47%) expression was significantly correlated with each other, advanced tumor stage, poor tumor differentiation (P < .0001 each), and necrosis (P < .005) Targeted next generation sequencing of 83 ccRCC samples demonstrated a significant association of genetic mutations in PBRM1, BAP1, and SETD2 with absence of PBRM1, BAP1, and HEK36me3 protein expression (P < .05, each). By assigning the protein expression patterns to evolutionary subtypes, we revealed similar clinical phenotypes as suggested by TRACERx Renal. Given their important contribution to tumor suppression, we conclude that combined functional inactivation of PBRM1, BAP1, SETD2 and pVHL is critical for ccRCC progression.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer and is associated with poor prognosis if metastasized. Up to one third of patients with local disease at diagnosis will develop metastasis after nephrectomy, and there is a need for new molecular markers to identify patients with high risk of tumor progression. In the present study, we performed genome-wide promoter DNA methylation analysis at diagnosis to identify DNA methylation profiles associated with risk for progress.
METHOD: Diagnostic tissue samples from 115 ccRCC patients were analysed by Illumina HumanMethylation450K arrays and methylation status of 155,931 promoter associated CpGs were related to genetic aberrations, gene expression and clinicopathological parameters.
RESULTS: The ccRCC samples separated into two clusters (cluster A/B) based on genome-wide promoter methylation status. The samples in these clusters differed in tumor diameter (p < 0.001), TNM stage (p < 0.001), morphological grade (p < 0.001), and patients outcome (5 year cancer specific survival (pCSS
CONCLUSION: DNA methylation analysis at diagnosis in ccRCC has the potential to improve outcome-prediction in non-metastatic patients at diagnosis.