Transcriptomic characterization and innovative molecular classification of clear cell renal cell carcinoma in the Chinese population

Abstract

Background Large-scale initiatives like The Cancer Genome Atlas (TCGA) performed genomics studies on predominantly Caucasian kidney cancer. In this study, we aimed to investigate genomics of Chinese clear cell renal cell carcinoma (ccRCC). Methods We performed whole-transcriptomic sequencing on 55 tumor tissues and 11 matched normal tissues from Chinese ccRCC patients. We systematically analyzed the data from our cohort and comprehensively compared with the TCGA ccRCC cohort. Results It found that PBRM1 mutates with a frequency of 11% in our cohort, much lower than that in TCGA Caucasians (33%). Besides, 31 gene fusions including 5 recurrent ones, that associated with apoptosis, tumor suppression and metastasis were identified. We classified our cohort into three classes by gene expression. Class 1 shows significantly elevated gene expression in the VEGF pathway, while Class 3 has comparably suppressed expression of this pathway. Class 2 is characterized by increased expression of extracellular matrix organization genes and is associated with high-grade tumors. Applying the classification to TCGA ccRCC patients revealed better distinction of tumor prognosis than reported classifications. Class 2 shows worst survival and Class 3 is a rare subtype ccRCC in the TCGA cohort. Furthermore, computational analysis on the immune microenvironment of ccRCC identified immune-active and tolerant tumors with significant increased macrophages and depleted CD4 positive T-cells, thus some patients may benefit from immunotherapies. Conclusion In summary, results presented in this study shed light into distinct genomic expression profiles in Chinese population, modified the stratification patterns by new molecular classification, and gave practical guidelines on clinical treatment of ccRCC patients.

BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

“…The GEPIA web tool was used to determine BUB1B expression in The Cancer Genome Atlas (TCGA) (KIRC, KIRP, KICH) dataset [20]. mRNA expression profiles in the GSE40435 [21], GSE66272 [22], GSE67501 [23], GSE105261 [24], GSE126964 [25], and GSE150404 (Miekus et al, not yet published) file were downloaded from the Gene Expression Omnibus (GEO) data repository. CPTAC (Clinical Proteomic Tumor Analysis Consortium) data was downloaded from the ProTrack web tool [26].…”

“…The GEPIA web tool was used to determine BUB1B expression in The Cancer Genome Atlas (TCGA) (KIRC, KIRP, KICH) dataset [20]. mRNA expression profiles in the GSE40435 [21], GSE66272 [22], GSE67501 [23], GSE105261 [24], GSE126964 [25], and GSE150404 (Miekus et al…”

Identification of Mast Cell-Based Molecular Subtypes and a Predictive Signature in Clear Cell Renal Cell Carcinoma

“…Beuselinck et al used unsupervised transcriptome analysis to identify four robust KIRC subtypes that were associated with different responses to sunitinib treatment ( Beuselinck et al, 2015 ). Zhao et al classified KIRC in the Chinese population into three classes based on gene expression, which provides practical guidelines on clinical treatment of patients with KIRC ( Zhao et al, 2020 ). However, few studies have examined the role of mast cells in KIRC and the molecular mechanisms underlying their effects.…”

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma

“…Comparative mRNA stemness indices (mRNAsi) of stem cell and non-stem cell indicated that the undifferentiated samples tended to obtain lower mRNAsi within the model. Additionally, the model was externally validated in 227 stem cell samples (GSE30625) [ 22 ] and other two KIRC datasets (66 samples in GSE126964, 265 samples in GSE73731 [ 20 , 21 ]. Consistent with our previous results that undifferentiated samples scored lower mRNAsi, we distinguished stem cell from somatic cell in a strong capability with an AUC of 0.968 by mRNAsi values (Fig.…”

“…We also downloaded PCBC RNA sequence data from the PCBC Synapse Portal ( https://www.synapse.org/pcbc ), consisting 16 ESC, 77 iPSC, 66 SC-derived EB, 29 SC-derived MESO, 29 SC-derived ECTO, and 36 SC-derived DE PCBC dataset [ 13 , 18 , 19 ]. Independent clear cell renal cell carcinoma validation sets (GSE73731 [ 20 ] and GSE126964 [ 21 ]) and stem cell validation set (GSE30652 [ 22 ]) with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ).…”