Pan-cancer analysis of TCGA data reveals notable signaling pathways

BACKGROUND

A signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype. The Cancer Genome Atlas (TCGA) makes available gene expression level data on cases and controls in ten different types of cancer including breast cancer, colon adenocarcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian carcinoma, rectum adenocarcinoma, and uterine corpus endometriod carcinoma. Signaling Pathway Impact Analysis (SPIA) is a software package that analyzes gene expression data to identify whether a pathway is relevant in a given condition.

METHODS

We present the results of a study that uses SPIA to investigate all 157 signaling pathways in the KEGG PATHWAY database. We analyzed each of the ten cancer types mentioned above separately, and we perform a pan-cancer analysis by grouping the data for all the cancer types.

RESULTS

In each analysis several pathways were found to be markedly more significant than all the other pathways. We call them notable. Research has already established a connection between many of these pathways and the corresponding cancer type. However, some of our discovered pathways appear to be new findings. Altogether there were 37 notable findings in the separate analyses, 26 of them occurred in 7 pathways. These 7 pathways included the 4 notable pathways discovered in the pan-cancer analysis. So, our results suggest that these 7 pathways account for much of the mechanisms of cancer. Furthermore, by looking at the overlap among pathways, we identified possible regions on the pathways where the aberrant activity is occurring.

CONCLUSIONS

We obtained 37 notable findings concerning 18 pathways. Some of them appear to be new discoveries. Furthermore, we identified regions on pathways where the aberrant activity might be occurring. We conclude that our results will prove to be valuable to cancer researchers because they provide many opportunities for laboratory and clinical follow-up studies.

System analysis of VEGFA in renal cell carcinoma: The expression, prognosis, gene regulation network and regulation targets

BACKGROUND

VEGFA is one of the most important regulators of angiogenesis and plays a crucial role in cancer angiogenesis and progression. Recent studies have highlighted a relationship between VEGFA expression and renal cell carcinoma occurrence. However, the expression level, gene regulation network, prognostic value, and target prediction of VEGFA in renal cell carcinoma remain unclear. Therefore, system analysis of the expression, gene regulation network, prognostic value, and target prediction of VEGFA in patients with renal cell carcinoma is of great theoretical significance as there is a clinical demand for the discovery of new renal cell carcinoma treatment targets and strategies to further improve renal cell carcinoma treatment efficacy.

METHODS

This study used multiple free online databases, including cBioPortal, TRRUST, GeneMANIA, GEPIA, Metascape, UALCAN, LinkedOmics, Metascape, and TIMER for the abovementioned analysis.

RESULTS

VEGFA was upregulated in patients with kidney renal clear cell carcinoma (KIRC) and kidney chromophobe (KICH), and downregulated in patients with kidney renal papillary cell carcinoma (KIRP). Moreover, genetic alterations of VEGFA were found in patients with renal cell carcinoma as follows: 4% (KIRC), 8% (KICH), and 4% (KIRP). The promoter methylation of VEGFA was lower and higher in patients with clinical stages of KIRC and stage 1 KIRP, respectively. VEGFA expression significantly correlated with KIRC and KIRP pathological stages. Furthermore, patients with KICH and KIRP having low VEGFA expression levels had a longer survival than those having high VEGFA expression levels. VEGFA and its neighboring genes functioned in the regulation of protein methylation and glycosylation, as well as muscle fiber growth and differentiation in patients with renal cell carcinoma. Gene Ontology enrichment analysis revealed that the functions of VEGFA and its neighboring genes in patients with renal cell carcinoma are mainly related to cell adhesion molecule binding, catalytic activity, acting on RNA, ATPase activity, actin filament binding, protease binding, transcription coactivator activity, cysteine-type peptidase activity, and calmodulin binding. Transcription factor targets of VEGFA and its neighboring genes in patients with renal cell carcinoma were found: HIF1A, TFAP2A, and ESR1 in KIRC; STAT3, NFKB1, and HIPK2 in KICH; and FOXO3, TFAP2A, and ETS1 in KIRP. We further explored the VEGFA-associated kinase (ATM in KICH as well as CDK1 and AURKB in KIRP) and VEGFA-associated microRNA (miRNA) targets (MIR-21 in KICH as well as MIR-213, MIR-383, and MIR-492 in KIRP). Furthermore, the following genes had the strongest correlation with VEGFA expression in patients with renal cell carcinoma: NOTCH4, GPR4, and TRIB2 in KIRC; CKMT2, RRAGD, and PPARGC1A in KICH; and FLT1, C6orf223, and ESM1 in KIRP. VEGFA expression in patients with renal cell carcinoma was positively associated with immune cell infiltration, including CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells.

CONCLUSIONS

This study revealed VEGFA expression and potential gene regulatory network in patients with renal cell carcinoma, thereby laying a foundation for further research on the role of VEGFA in renal cell carcinoma occurrence. Moreover, the study provides new renal cell carcinoma therapeutic targets and prognostic biomarkers as a reference for fundamental and clinical research.

SKA3 Serves as a Biomarker for Poor Prognosis in Kidney Renal Papillary Cell Carcinoma

Background

There is a surprising paucity of studies investigating the potential mechanism of SKA3 in the progression and prognosis of kidney renal papillary cell carcinoma (KIRP).

Methods

We used TCGA and other databases to analyze the expression, clinical value, and potential mechanisms of SKA3 in KIRP patients. We also explored therapeutic agents for KIRP through GSCALite.

Results

SKA3 mRNA expression was significantly upregulated and the area under the curve was 0.792 (95% CI 0.727–0.856). Increased SKA3 expression was related to shorter overall survival, disease-specific survival and progression-free survival. Hub genes in protein–protein interactions were CDK1, CDC20, CCNB1, CCNA2, BUB1, AURKB, BUB1B, PLK1, CCNB2, and MAD2L1, which were differentially expressed and also associated with KIRP prognosis. Gene-set enrichment analysis indicated that E2F targets, epithelial–mesenchymal transition, glycolysis, the WNT signaling pathway, and other pathways were highly enriched upon SKA3 upregulation. Gene-set variation analysis of SKA3 and its ten hub genes showed that the significant correlation of cancer-related pathways included the cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and Ras/MAPK. In addition, we found that MEK inhibitors, ie, trametinib, selumetinib, PD0325901, and RDEA119, may be feasible targeting agents for KIRP patients.

Conclusion

SKA3 might contribute to poor prognosis of KIRP through cell cycle, DNA damage, hormone androgen receptor, hormone estrogen receptor, PI3K/Akt, and RAS/MAPK. SKA3 potentially serves as a prognostic biomarker and target for KIRP.

Prognostic risk signature based on the expression of three m6A RNA methylation regulatory genes in kidney renal papillary cell carcinoma

In this study, we investigated the prognostic significance of the expression of N6-methyladenosine (m6A) RNA methylation regulatory genes in kidney renal papillary cell carcinoma (KIRP). RNA-sequencing data analysis showed that 14 of 20 major m6A RNA methylation regulatory genes were differentially expressed in the KIRP tissues from The Cancer Genome Atlas (TCGA) database. We constructed a prognostic risk signature with three m6A RNA methylation regulatory genes, IGF2BP3, KIAA1429 and HNRNPC, based on the results from univariate and LASSO Cox regression analyses. Multivariate Cox regression analysis confirmed that the risk score based on the three-gene prognostic risk signature was an independent predictive factor in KIRP. The overall survival of high-risk KIRP patients was significantly shorter than the low-risk KIRP patients. Expression of the three prognostic risk-related genes correlated with the AJCC and TNM stages of KIRP patients from TCGA and GEPIA datasets. ROC curve analysis showed that the three-gene prognostic risk signature precisely predicted the 1-year, 3-year and 5-year survival of KIRP patients. These findings demonstrate that expression of three prognostic risk-related m6A RNA methylation regulatory genes accurately predicts survival outcomes in KIRP patients.

CTHRC1 Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Kidney Renal Papillary Cell Carcinoma and Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) are the most common RCC types. RCC has high immune infiltration levels, and immunotherapy is currently one of the most promising treatments for RCC. Collagen triple helix repeat containing 1 (CTHRC1) is an extracellular matrix protein that regulates tumor invasion and modulates the tumor microenvironment. However, the association of CTHRC1 with the prognosis and tumor-infiltrating lymphocytes of KIRP and KIRC has not been reported. We examined the CTHRC1 expression differences in multiple tumor tissues and normal tissues via exploring TIMER, Oncomine, and UALCAN databases. Then, we searched the Kaplan-Meier plotter database to evaluate the correlation of CTHRC1 mRNA level with clinical outcomes. Subsequently, the TIMER platform and TISIDB website were chosen to assess the correlation of CTHRC1 with tumor immune cell infiltration level. We further explored the causes of aberrant CTHRC1 expression in tumorigenesis. We found that CTHRC1 level was significantly elevated in KIRP and KIRC tissues relative to normal tissues. CTHRC1 expression associates with tumor stage, histology, lymph node metastasis, and poor clinical prognosis in KIRP. The CTHRC1 level correlates to tumor grade, stage, nodal metastasis, and worse survival prognosis. Additionally, CTHRC1 is positively related to different tumor-infiltrating immune cells in KIRP and KIRC. Moreover, CTHRC1 was closely correlated with the gene markers of diverse immune cells. Also, high CTHRC1 expression predicted a worse prognosis in KIRP and KIRC based on immune cells. Copy number variations (CNV) and DNA methylation might contribute to the abnormal upregulation of CTHRC1 in KIRP and KIRC. In conclusion, CTHRC1 can serve as a biomarker to predict the prognosis and immune infiltration in KIRP and KIRC.

Glycolytic regulatory enzyme PFKFB3 as a prognostic and tumor microenvironment biomarker in human cancers

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFK-2/FBPase-2, PFKFB3) is a glycolysis regulatory enzyme and plays a key role in oncogenesis of several cancers. However, the systematic study of crosstalk between PFKFB3 and Tumor microenvironment (TME) in pan-cancer has less been examined. In this study, we conducted a comprehensive analysis of the relationship between PFKFB3 expression, patient prognostic, Tumor mutational burden (TMB), Microsatellite instability (MSI), DNA mismatch repair (MMR), and especially TME, including immune infiltration, immune regulator, and immune checkpoint, across 33 types of tumors using datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We found that PFKFB3 expression was significantly correlated with patient prognostic and TME factors in various tumors. Moreover, we confirmed that PFKFB3 was an independent prognostic factor for kidney renal papillary cell carcinoma (KIRP), and established a risk prognostic model based on the expression of PFKFB3 as a clinical risk factor, which has a good predictive ability. Our study indicated that PFKFB3 is a potent regulatory factor for TME and has the potential to be a valuable prognostic biomarker in human tumor therapy.

PYCR in Kidney Renal Papillary Cell Carcinoma: Expression, Prognosis, Gene Regulation Network, and Regulation Targets

BACKGROUND

Pyrroline-5-carboxylate reductase (PYCR) includes three human genes encoding three isozymes, PYCR1, PYCR2, and PYCR3 (or PYCRL), which facilitate the final step in the conversion of glutamine to proline. These genes play important roles in regulating the cell cycle and redox homeostasis as well as promoting growth signaling pathways. Proline is abnormally upregulated in a variety of cancers, and as the last key enzyme in proline production, PYCR plays an integral role in promoting tumorigenesis and cancer progression. However, its role in patients with kidney renal papillary cell carcinoma (KIRP) has not been fully elucidated. In this study, we aimed to systematically analyze the expression, gene regulatory network, prognostic value, and target prediction of PYCR in patients with KIRP, elucidate the association between PYCR expression and KIRP, and identify potential new targets for the clinical treatment of KIRP.

METHODS

We systematically analyzed the expression, prognosis, gene regulatory network, and regulatory targets of PYCR1, PYCR2, and PYCRL in KIRP using multiple online databases including cBioPortal, STRING, MethSurv, GeneMANIA, Gene Expression Profiling Interactive Analysis (GEPIA), Metascape, UALCAN, LinkedOmics, and TIMER.

RESULTS

The expression levels of PYCR1, PYCR2, and PYCRL were considerably upregulated in patients with KIRP based on sample type, sex, age, and individual cancer stage. PYCR1 and PYCR2 transcript levels were markedly upregulated in females than in males, and patients aged 21-40 years had higher PYCR1 and PYCR2 transcript levels than those in other age groups. Interestingly, PYCR2 transcript levels gradually decreased with age. In addition, the expressions of PYCR1 and PYCR2 were notably correlated with the pathological stage of KIRP. Patients with KIRP with low PYCR1 and PYCR2 expression had longer survival than those with high PYCR1 and PYCR2 expression. PYCR1, PYCR2, and PYCRL were altered by 4%, 7%, and 6%, respectively, in 280 patients with KIRP. The methylation levels of cytosine-phosphate-guanine (CpG) sites in PYCR were markedly correlated with the prognosis of patients with KIRP. PYCR1, PYCR2, PYCRL, and their neighboring genes form a complex network of interactions. The molecular functions of the genes, as demonstrated by their corresponding Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, included calcium channel activity, phospholipid binding, RNA polymerase II-specificity, and kinase and GTPase-regulatory activities. PYCR1, PYCR2, and PYCRL targeted miR-21, miR-221, and miR-222, resulting in a better prognosis of KIRP. We analyzed mRNA sequencing data from 290 patients with KIRP and found that ADA, NPM3, and TKT were positively associated with PYCR1 expression; PFDN2, JTB, and HAX1 were positively correlated with PYCR2 expression; SHARPIN, YDJC, and NUBP2 were positively correlated with PYCRL expression; PYCR1 was positively correlated with B cell and CD8+ T-cell infiltration levels; macrophage infiltration was negatively correlated with PYCR2 expression; and PYCRL expression was negatively correlated with B-cell, CD8+ T cell, and dendritic cell infiltration levels.

CONCLUSIONS

PYCR1, PYCR2, and PYCRL may be potential therapeutic and prognostic biomarkers for patients with KIRP. The regulation of microRNAs (miRNAs), including miR-21, miR-221, and miR-222, may prove an important strategy for KIRP treatment.

Construction and validation of an autophagy-related long non-coding RNA signature to predict the prognosis of kidney renal papillary cell carcinoma

To identify the autophagy-related long non-coding RNAs (ARlncRNAs) associated with the prognosis of kidney renal papillary cell carcinoma (KIRP), thereby establishing a clinical prognostic model. The gene expression matrix and clinical survival information of patients with KIRP were downloaded from The Cancer Genome Atlas database, and were divided into the training and testing groups. ARlncRNAs associated with the KIRP prognosis were analyzed by univariate, Least Absolute Shrinkage and Selection Operator (LASSO(, and multivariate Cox regression to construct a signature. We combined clinical factors associated with the prognosis with ARlncRNAs to establish a prognostic model of patients with KIRP. A nomogram was established to predict 1-year, 3-year, and 5-year survival of patients with KIRP. Besides, we built the lncRNA-messenger RNA co-expression network and used Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis to detect the biological functions of ARlncRNAs. LEF1-AS1, CU634019.6, C2orf48, AC027228.2, and AC107464.3 were identified. A prognosis-related ARlncRNAs signature was constructed in the training group and validated in the testing group. Patients with KIRP with a low risk score had significantly longer survival time than those with a high risk score. The risk score significantly affected the prognosis of patients, thereby being used for modeling. The area under the receiver operating characteristic curve values of 1-year, 3-year, and 5-year overall survival were 0.80, 0.78, and 0.84 in the training group, respectively. The signature had high concordance index and good accuracy in predicting the prognosis, which were confirmed by the nomogram. The prognosis-related ARlncRNAs signature we identified had a more accurate prediction for the prognosis of patients with KIRP.

Prognostic value of CDCA3 in kidney renal papillary cell carcinoma

Kidney renal papillary cell carcinoma (KIRP) is a type of low-grade malignant renal cell carcinoma. Huge challenges remain in the treatment of KIRP. Cell division cycle associated 3 (CDCA3) participates in human physiological and pathological processes. However, its role in KIRP has not been established. Here, we evaluated the prognostic value of CDCA3 in KIRP using a comprehensive bioinformatics approach. Data for CDCA3 expression in KIRP were obtained from online database. Different expression genes between high and low CDCA3 expression groups were identified and evaluated by performing Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A gene set enrichment analysis was performed to elucidate the function and pathway differences between the different. Differences in immune cell infiltration between low and high CDCA3 expression groups were analyzed by a single-sample GSEA method for immune cells. A protein-protein interaction network was generated and hub genes were identified. UALCAN was used to analyze associations between the mRNA expression levels of CDCA3 in KIRP tissues with clinicopathologic parameters. The diagnostic efficacy of CDCA3 for KIRP was analyzed by ROC analysis. Logistic regression was used to analyze relationships between the clinicopathological characteristics and CDCA3 expression. Our results indicated that high CDCA3 mRNA expression is significantly associated with some clinicopathologic parameters in KIRP patients High CDCA3 mRNA expression associated with a shorter overall survival, progression-free interval, and disease-special survival. Taken together, CDCA3 is a potential target for the development of anti-KIRP therapeutics and is an efficient prognostic marker.

Bioinformatics analysis highlights CCNB1 as a potential prognostic biomarker and an anti-kidney renal papillary cell carcinoma drug target

Kidney renal papillary cell carcinoma (KIRP) is a common urinary tumor that causes lymph node invasion. Once metastatic, the prognosis is poor and there is a lack of effective early diagnostic markers for this tumor. The expression of CCNB1 in KIRP tumor tissues was significantly higher than that in normal tissues in The Cancer Genome Atlas database with or without the genotype-tissue expression database, and a consistent result was obtained in 32 paired tissues. In addition, CCNB1 expression increased remarkably with the progression of the T and M stages. Moreover, using the online HPA database, we verified that the immunohistochemical scores of CCNB1 in KIRP were higher than those in the normal kidney tissues. The higher expression group of CCNB1 showed a worse prognosis in KIRP. Moreover, the receiver operating characteristic curve, univariate and multivariate analyses, and construction of the column diagram further illustrated that CCNB1 was an independent prognostic factor for KIRP. Meanwhile, CCNB1 could better predict the 1- and 3-year survival rates of KIRP. Six genes were significantly and positively co-expressed with CCNB1. We also found that the CCNB1 high-expression group was enriched in the ECM_RECEPTOR_INTERACTION and FOCAL_ADHESION pathways. Finally, drug sensitivity analysis combined with molecular docking identified 5 targeting drugs with the strongest binding activity to CCNB1. CCNB1 is a potential and reliable biomarker for KIRP diagnosis and can be used to predict the survival of patients with KIRP. The 5 selected drugs targeting CCNB1 may provide new hopes for patients with KIRP metastasis.

An in silico investigation of SPC24 as a putative biomarker of Kidney Renal Clear Cell Carcinoma and Kidney Renal Papillary Cell Carcinoma for predicting prognosis and/or immune infiltration

BACKGROUND AND OBJECTIVE

SPC24 was reported to be correlated with the development of many cancers. However, its role in renal cancer was unclear. Our aim was to explore role of SPC24 in Kidney Renal Clear Cell Carcinoma (KIRC) and Kidney Renal Papillary Cell Carcinoma (KIRP) of types of renal cancer.

METHODS

SPC24 expression in KIRC and KIRP were firstly analyzed. Subsequently, the correlation between SPC24 expression and TNM staging of KIRC and KIRP and accuracy of SPC24 in diagnosing KIRC and KIRP were explored. Moreover, the correlation between SPC24 expression and prognosis of KIRC and KIRP were analyzed. Univariate and multivariate analysis were performed to identify prognostic factors in KIRC and KIRP and nomograms were constructed. The correlation between SPC24 expression and immune cell infiltration, immune molecules, microsatellite instability (MSI), and tumor mutational burden (TMB) were further explored. Finally, the correlations between SPC24 expression and prognosis of KIRC based on different immune cell enrichment were analyzed.

RESULTS

SPC24 was significantly up-regulated in multiple cancers, especially KIRC and KIRP. SPC24 expression was significantly correlated with the T.N.M stage of KIRC and KIRP, and up-regulated SPC24 suggested worse prognosis. Besides, SPC24 possess good accuracy in diagnosing KIRC and KIRP. The SPC24-based nomograms displayed satisfactory efficacy in KIRC and KIRP. Moreover, we found SPC24 expression was closely correlated with immune cell infiltration, immune molecules and TMB in KIRC and up-regulated SPC24 revealed poor prognosis based on different immune cell enrichment.

CONCLUSION

SPC24 has potential to be a biomarker predicting the prognosis and/or immune infiltration of KIRC and KIRP.

The Role of MIEF2 in Cisplatin Sensitivity in KIRP Patients: Insights from Four-gene Mitochondrial Fusion RNA Markers

BACKGROUND

Mitochondrial fusion is vital for cellular function and has been increasingly linked to cancer development. Kidney renal papillary cell carcinoma (KIRP), the second most common renal cell carcinoma, presents diverse prognostic outcomes. Identifying novel biomarkers is critical for improving prognosis and treatment response in KIRP.

OBJECTIVE

This study aims to explore the gene expression associated with mitochondrial fusion and establish a novel gene signature model to predict KIRP prognosis and cisplatin sensitivity.

METHODS

We analyzed RNA sequencing data and clinical records of 285 KIRP patients from The Cancer Genome Atlas (TCGA). LASSO regression identified four key mitochondrial fusion-related genes (BNIP3, GDAP1, MIEF2, PRKN). Multivariate Cox regression evaluated their association with overall survival. Risk stratification was developed based on gene expression. We assessed immunotherapy responses using checkpoint inhibitor scores, tumor mutation burden, TIDE scores, and tumor microenvironment characteristics. Cisplatin sensitivity was evaluated via correlation analysis of gene expression levels and half-maximal inhibitory concentration (IC50). In vitro loss- and gain-of-function experiments in KIRP cell lines (Caki-2, ACHN) assessed MIEF2's role in cisplatin sensitivity.

RESULTS

The gene signature successfully stratified patients into high- and low-risk groups, with significant survival differences. The area under the ROC curve (AUC) for the risk model was 0.782. MIEF2 was notably associated with cisplatin sensitivity, confirmed through functional experiments. Patients in the high-risk group exhibited lower MIEF2 expression and increased cisplatin sensitivity.

Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma

Background

Identifying Kidney Renal Papillary Cell Carcinoma (KIRP) patients with high-risk, guiding individualized diagnosis and treatment of patients, and identifying effective prognostic targets are urgent problems to be solved in current research on KIRP.

Methods

In this study, data of multi omics for patients with KIRP were collected from TCGA database, including mRNAs, lncRNAs, miRNAs, data of methylation, and data of gene mutations. Data of multi-omics related to prognosis of patients with KIRP were selected for each omics level. Further, multi omics data related to prognosis were integrated into cluster analysis based on ten clustering algorithms using MOVICS package. The multi omics-based cancer subtype (MOCS) were compared on biological characteristics, immune microenvironmental cell abundance, immune checkpoint, genomic mutation, drug sensitivity using R packages, including GSVA, clusterProfiler, TIMER, CIBERSORT, CIBERSORT-ABS, quanTIseq, MCPcounter, xCell, EPIC, GISTIC, and pRRophetic algorithms.

Results

The top ten OS-related factors for KIRP patients were annotated. Patients with KIRP were divided into MOCS1, MOCS2, and MOCS3. Patients in the MOCS3 subtype were observed with shorter overall survival time than patients in the MOCS1 and MOCS2 subtypes. MOCS1 was negatively correlated with immune-related pathways, and we found global dysfunction of cancer-related pathways among the three MOCS subtypes. We evaluated the activity profiles of regulons among the three MOCSs. Most of the metabolism-related pathways were activated in MOCS2. Several immune microenvironmental cells were highly infiltrated in specific MOCS subtype. MOCS3 showed a significantly lower tumor mutation burden. The CNV occurrence frequency was higher in MOCS1. As for treatment, we found that these MOCSs were sensitive to different drugs and treatments. We also analyzed single-cell data for KIRP.

Conclusion

Based on a variety of algorithms, this study determined the risk classifier based on multi-omics data, which could guide the risk stratification and medication selection of patients with KIRP.