Analyzing and validating the prognostic value and immune microenvironment of clear cell renal cell carcinoma

Bioinformatics analysis and experimental validation of m6A and cuproptosis-related lncRNA NFE4 in clear cell renal cell carcinoma

PURPOSE

This study aimed to construct an m6A and cuproptosis-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using the information acquired from The Cancer Genome Atlas (TCGA) database.

METHODS

First, the co-expression analysis was performed to identify lncRNAs linked with N6-methyladenosine (m6A) and cuproptosis in ccRCC. Then, a model encompassing four candidate lncRNAs was constructed via univariate, least absolute shrinkage together with selection operator (LASSO), and multivariate regression analyses. Furthermore, Kaplan-Meier, principal component, functional enrichment annotation, and nomogram analyses were performed to develop a risk model that could effectively assess medical outcomes for ccRCC cases. Moreover, the cellular function of NFE4 in Caki-1/OS-RC-2 cultures was elucidated through CCK-8/EdU assessments and Transwell experiments. Dataset outcomes indicated that NFE4 can have possible implications in m6A and cuproptosis, and may promote ccRCC progression.

RESULTS

We constructed a panel of m6A and cuproptosis-related lncRNAs to construct a prognostic prediction model. The Kaplan-Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. Furthermore, the m6A and cuproptosis-related lncRNA model indicated higher diagnostic efficiency than other clinical features. Moreover, the NFE4 function analysis indicated a gene associated with m6A and cuproptosis-related lncRNAs in ccRCC. It was also revealed that the proliferation and migration of Caki-1 /OS-RC-2 cells were inhibited in the NFE4 knockdown group.

CONCLUSION

Overall, this study indicated that NFE4 and our constructed risk signature could predict outcomes and have potential clinical value.

MiR-29 and MiR-140 regulate TRAIL-induced drug tolerance in lung cancer

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has chemotherapeutic potential as a regulator of an extrinsic apoptotic ligand, but its effect as a drug is limited by innate and acquired resistance. Recent findings suggest that an intermediate drug tolerance could mediate acquired resistance, which has made the main obstacle for limited utility of TRAIL as an anti-cancer therapeutics. We propose miRNA-dependent epigenetic modification drives the drug tolerant state in TRAIL-induced drug tolerant (TDT). Transcriptomic analysis revealed miR-29 target gene activation in TDT cells, showing oncogenic signature in lung cancer. Also, the restored TRAIL-sensitivity was associated with miR-29ac and 140-5p expressions, which is known as tumor suppressor by suppressing oncogenic protein RSK2 (p90 ribosomal S6 kinase), further confirmed in patient samples. Moreover, we extended this finding into 119 lung cancer cell lines from public data set, suggesting a significant correlation between TRAIL-sensitivity and RSK2 mRNA expression. Finally, we found that increased RSK2 mRNA is responsible for NF-κB activation, which we previously showed as a key determinant in both innate and acquired TRAIL-resistance. Our findings support further investigation of miR-29ac and -140-5p inhibition to maintain TRAIL-sensitivity and improve the durability of response to TRAIL in lung cancer.

Elevated ADH5 expression suggested better prognosis in kidney renal clear cell carcinoma (KIRC) and related to immunity through single-cell and bulk RNA-sequencing

BACKGROUND

Despite the rapid advances in modern medical technology, kidney renal clear cell carcinoma (KIRC) remains a challenging clinical problem in urology. Researchers urgently search for useful markers to break through the therapeutic conundrum due to its high lethality. Therefore, the study explores the value of ADH5 on overall survival (OS) and the immunology of KIRC.

METHODS

The gene expression matrix and clinical information on ADH5 in the TCGA database were validated using external databases and qRT-PCR. To confirm the correlation between ADH5 and KIRC prognosis, univariate/multivariate Cox regression analysis was used. We also explored the signaling pathways associated with ADH5 in KIRC and investigated its association with immunity.

RESULTS

The mRNA and protein levels showed an apparent downregulation of ADH5 in KIRC. Correlation analysis revealed that ADH5 was directly related to histological grade, clinical stage, and TMN stage (p < 0.05). Univariate and multivariate Cox regression analysis identified ADH5 as an independent factor affecting the prognosis of KIRC. Enrichment analysis looked into five ADH5-related signaling pathways. The results showed no correlation between ADH5 and TMB, TNB, and MSI. From an immunological perspective, ADH5 was found to be associated with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Lower ADH5 expression was associated with greater responsiveness to immunotherapy. Single-cell sequencing revealed that ADH5 is highly expressed in immune cells.

CONCLUSION

ADH5 could be a promising prognostic biomarker and a potential therapeutic target for KIRC. Besides, it was found that KIRC patients with low ADH5 expression were more sensitive to immunotherapy.

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma

Background

Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC.

Methods

Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion).

Results

Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion.

Conclusion

The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.

Integrative exploration of the mutual gene signatures and immune microenvironment between benign prostate hyperplasia and castration-resistant prostate cancer

BACKGROUND

Benign prostate hyperplasia (BPH) and prostate cancer (CaP) are among the most frequently occurring prostatic diseases. When CaP progressed to castration-resistant CaP (CRPC), the prognosis is poor. Although CaP/CRPC and BPH frequently coexist in prostate, the inter-relational mechanism between them is largely unknown.

METHODS

Single-cell RNA sequencing, bulk-RNA sequencing, and microarray data of BPH, CaP in the Gene Expression Omnibus database were obtained and comprehensively analyzed. Weighted Gene Co-Expression Network Analysis (WGCNA) and lasso regression analysis were performed to explore the potential biomarkers.

RESULTS

With WGCNA, five modules in BPH, two in CaP, and three in CRPC were identified as significant modules. Pathway enrichment analysis found that the epigenetics and chromosomal-related signaling were dominantly clustered in the CaP group but not in BPH and CRPC. Lasso regression analysis was used to analyze further the mutual genes between the BPH module and the CRPC module. As a result, DDA1, ERG28, OGFOD1, and OXA1L were significantly correlated with the transcriptomic features in both BPH and CRPC. More importantly, the role of the four gene signatures was validated in two independent anti-PD-1 immunotherapy cohort.

CONCLUSION

This study revealed the shared gene signatures and immune microenvironment between BPH and CRPC. The identified hub genes, including DDA1, ERG28, OGFOD1, and OXA1L, might be potential therapeutic targets for facilitating immunotherapy in prostate cancer.

Identification and verification of a novel anoikis-related gene signature with prognostic significance in clear cell renal cell carcinoma

PURPOSE

Clear cell renal cell carcinomas (ccRCCs) are the most common form of renal cancer in the world. The loss of extracellular matrix (ECM) stimulates cell apoptosis, known as anoikis. A resistance to anoikis in cancer cells is believed to contribute to tumor malignancy, particularly metastasis; however, the potential influence of anoikis on the prognosis of ccRCC patients is not fully understood.

METHODS

In this study, anoikis-related genes (ARGs) with discrepant expression were selected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The anoikis-related gene signature (ARS) was built using a combination of the univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. ARS was also evaluated for their prognostic value. We explored the tumor microenvironment and enrichment pathways between different clusters of ccRCC. We also examined differences in clinical characteristics, immune cell infiltration and drug sensitivity between the high- and low-risk sets. In addition, we utilized three external databases and quantitative real-time polymerase chain reaction (qRT-PCR) to validate the expression and prognosis of ARGs.

RESULTS

Eight ARGs (PLAUR, HMCN1, CDKN2A, BID, GLI2, PLG, PRKCQ and IRF6) were identified as anoikis-related prognostic factors. According to Kaplan-Meier (KM) analysis, ccRCC patients with high-risk ARGs have a worse prognosis. The risk score was found to be a significant independent prognostic indicator. According to tumor microenvironment (TME) scores, stromal score, immune score, and estimated score of the high-risk group were superior to those of the low-risk group. There were significant differences between the two groups regarding the amount of infiltrated immune cells, immune checkpoint expression as well as drug sensitivity. A nomogram was constructed using ccRCC clinical features and risk scores. The signature and the nomogram both performed well in predicting overall survival (OS) for ccRCC patients. According to a decision curve analysis (DCA), clinical treatment options for patients with ccRCC could be improved using this model.

CONCLUSION

The results of validation from external databases and qRT-PCR were basically agreement with findings in TCGA and GEO databases. The ARS serving as biomarkers may provide an important reference for individual therapy of ccRCC patients.

Identification of ATP6V0A4 as a potential biomarker in renal cell carcinoma using integrated bioinformatics analysis

Clear cell renal cell carcinoma (ccRCC) is the most common pathological type of renal cancer, and is associated with a high mortality rate, which is related to high rates of tumor recurrence and metastasis. The aim of the present study was to identify reliable molecular biomarkers with high specificity and sensitivity for ccRCC. A total of eight ccRCC-related expression profiles were downloaded from Gene Expression Omnibus for integrated bioinformatics analysis to screen for significantly differentially expressed genes (DEGs). Reverse transcription-quantitative (RT-q)PCR, western blotting and immunohistochemistry staining assays were performed to evaluate the expression levels of candidate biomarkers in ccRCC tissues and cell lines. In total, 255 ccRCC specimens and 165 adjacent normal kidney specimens were analyzed, and 344 significant DEGs, consisting of 115 upregulated DEGs and 229 downregulated DEGs, were identified. The results of Gene Ontology analysis suggested a significant enrichment of DEGs in 'organic anion transport' and 'small molecule catabolic process' in biological processes, in 'apical plasma membrane' and 'apical part of the cell' in cell components, and in 'anion transmembrane transporter activity' and 'active transmembrane transporter activity' in molecular functions. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that the DEGs were significantly enriched in the 'phagosome', the 'PPAR signaling pathway', 'complement and coagulation cascades', the 'HIF-1 signaling pathway' and 'carbon metabolism'. Next, 7 hub genes (SUCNR1, CXCR4, VCAN, CASR, ATP6V0A4, VEGFA and SERPINE1) were identified and validated using The Cancer Genome Atlas database. Survival analysis showed that low expression of ATP6V0A4 was associated with a poor prognosis in patients with ccRCC. Additionally, received operating characteristic curves indicated that ATP6V0A4 could distinguish ccRCC samples from normal kidney samples. Furthermore, RT-qPCR, western blotting and immunohistochemistry staining results showed that ATP6V0A4 was significantly downregulated in ccRCC tissues and cell lines. In conclusion, ATP6V0A4 may be involved in tumor progression and regarded as a potential therapeutic target for the recurrence and metastasis of ccRCC.

Comprehensive analysis of the prognosis and immune infiltration of TMC family members in renal clear cell carcinoma

Renal cancer is a common malignancy of the urinary system, and renal clear cell carcinoma (RCCC) is the most common pathological type. Transmembrane channel-like (TMC) protein is an evolutionarily conserved gene family containing 8 members, however there is still a lack of comprehensive analysis about TMC family members in RCCC. In this study, we analyzed the expression of TMC family members in RCCC from TCGA and investigated the prognosis values and immune infiltration of TMC family members in RCCC. We found that TMC2, TMC3, TMC5, TMC7 and TMC8 were significantly related with overall survival (OS) of RCCC patients. TMC3, TMC6, and TMC8 was positively correlated with the degree of immune infiltration in RCCC. TMC2, TMC6, TMC7, and TMC8 were positively correlated with immune checkpoint genes, whereas TMC4 was negative. According to KEGG and GO analysis, almost all TMCs except TMC4 were involved in the immune response. Thus, we may regard the TMC family members as novel biomarkers to predict potential prognosis and immunotherapeutic response in RCCC patients.

A meta-validated immune infiltration-related gene model predicts prognosis and immunotherapy sensitivity in HNSCC

BACKGROUND

Tumor microenvironment (TME) is of great importance to regulate the initiation and advance of cancer. The immune infiltration patterns of TME have been considered to impact the prognosis and immunotherapy sensitivity in Head and Neck squamous cell carcinoma (HNSCC). Whereas, specific molecular targets and cell components involved in the HNSCC tumor microenvironment remain a twilight zone.

METHODS

Immune scores of TCGA-HNSCC patients were calculated via ESTIMATE algorithm, followed by weighted gene co-expression network analysis (WGCNA) to filter immune infiltration-related gene modules. Univariate, the least absolute shrinkage and selection operator (LASSO), and multivariate cox regression were applied to construct the prognostic model. The predictive capacity was validated by meta-analysis including external dataset GSE65858, GSE41613 and GSE686. Model candidate genes were verified at mRNA and protein levels using public database and independent specimens of immunohistochemistry. Immunotherapy-treated cohort GSE159067, TIDE and CIBERSORT were used to evaluate the features of immunotherapy responsiveness and immune infiltration in HNSCC.

RESULTS

Immune microenvironment was significantly associated with the prognosis of HNSCC patients. Total 277 immune infiltration-related genes were filtered by WGCNA and involved in various immune processes. Cox regression identified nine prognostic immune infiltration-related genes (MORF4L2, CTSL1, TBC1D2, C5orf15, LIPA, WIPF1, CXCL13, TMEM173, ISG20) to build a risk score. Most candidate genes were highly expressed in HNSCC tissues at mRNA and protein levels. Survival meta-analysis illustrated high prognostic accuracy of the model in the discovery cohort and validation cohort. Higher proportion of progression-free outcomes, lower TIDE scores and higher expression levels of immune checkpoint genes indicated enhanced immunotherapy responsiveness in low-risk patients. Decreased memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells, and increased activated dendritic cells and activated mast cells were identified as crucial immune cells in the TME of high-risk patients.

CONCLUSIONS

The immune infiltration-related gene model was well-qualified and provided novel biomarkers for the prognosis of HNSCC.