Identification of Hypoxia-Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma

Cuproptosis in ccRCC: key player in therapeutic and prognostic targets

Background

Classical biomarkers have been used to classify clear cell renal cell carcinoma (ccRCC) patients in a variety of ways, and emerging evidences have indicated that cuproptosis is closely related to mitochondrial metabolism, thereby accelerating the development and progression of ccRCC. Nevertheless, the specific relationship between cuproptosis and the prognosis and treatment of ccRCC remains unclear.

Methods

We comprehensively integrated several ccRCC patient datasets into a large cohort. Following that, we systematically analyzed multi-omics data to demonstrate the differences between two cuproptosis clusters.

Results

We identified two cuproptosis clusters in ccRCC patients. Among the two clusters, cluster 1 patients showed favorable prognosis. We then confirmed the significant differences between the two clusters, including more typical cancer hallmarks were enriched in cluster 2 patients; cluster 2 patients were more susceptible to develop mutations and had a lower level of gistic score and mRNAsi. Importantly, both Tumor Immune Dysfunction and Exclusion analysis and subclass mapping algorithm showed that cuproptosis 1 patients were more susceptible to be responded to immunotherapy. In addition, a prognostic signature was successfully developed and also showed prominent predictive power in response to immunotherapy.

Conclusion

As a result of our findings, we were able to classify ccRCC patients according to cuproptosis in a novel way. By constructing the cuproptosis clusters and developing the signature, patients with ccRCC could have a more accurate prognosis prediction and better immunotherapy options.

NFE2L3 drives hepatocellular carcinoma cell proliferation by regulating the proteasome-dependent degradation of ISGylated p53

Nuclear factor erythroid 2-like 3 (NFE2L3) is a member of the cap 'n' collar basic-region leucine zipper (CNC-bZIP) transcription factor family that plays a vital role in modulating oxidation-reduction steady-state and proteolysis. Accumulating evidence suggests that NFE2L3 participates in cancer development; however, little is known about the mechanism by which NFE2L3 regulates hepatocellular carcinoma (HCC) cell growth. Here, we confirmed that NFE2L3 promotes HCC cell proliferation by acting as a transcription factor, which directly induces the expression of proteasome and interferon-stimulated gene 15 (ISG15) to enhance the proteasome-dependent degradation of ISGylated p53. Post-translational ISGylation abated the stability of p53 and facilitated HCC cell growth. In summary, we uncovered the pivotal role of NFE2L3 in promoting HCC cell proliferation during proteostasis. This finding may provide a new target for the clinical treatment of HCC.

Construction of an endoplasmic reticulum stress-related gene model for predicting prognosis and immune features in kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is one of the most lethal malignant tumors with a propensity for poor prognosis and difficult treatment. Endoplasmic reticulum (ER) stress served as a pivotal role in the progression of the tumor. However, the implications of ER stress on the clinical outcome and immune features of KIRC patients still need elucidation.

Methods: We identified differentially expressed ER stress-related genes between KIRC specimens and normal specimens with TCGA dataset. Then, we explored the biological function and genetic mutation of ER stress-related differentially expressed genes (DEGs) by multiple bioinformatics analysis. Subsequently, LASSO analysis and univariate Cox regression analysis were applied to construct a novel prognostic model based on ER stress-related DEGs. Next, we confirmed the predictive performance of this model with the GEO dataset and explored the potential biological functions by functional enrichment analysis. Finally, KIRC patients stratified by the prognostic model were assessed for tumor microenvironment (TME), immune infiltration, and immune checkpoints through single-sample Gene Set Enrichment Analysis (ssGSEA) and ESTIMATE analysis.

Results: We constructed a novel prognostic model, including eight ER stress-related DEGs, which could stratify two risk groups in KIRC. The prognostic model and a model-based nomogram could accurately predict the prognosis of KIRC patients. Functional enrichment analysis indicated several biological functions related to the progression of KIRC. The high-risk group showed higher levels of tumor infiltration by immune cells and higher immune scores.

Conclusion: In this study, we constructed a novel prognostic model based on eight ER stress-related genes for KIRC patients, which would help predict the prognosis of KIRC and provide a new orientation to further research studies on personalized immunotherapy in KIRC.

FMR1 is identified as an immune-related novel prognostic biomarker for renal clear cell carcinoma: A bioinformatics analysis of TAZ/YAP

WW domain-containing transcription regulator 1 (TAZ, or WWTR1) and Yes-associated protein 1 (YAP) are both important effectors of the Hippo pathway and exhibit different functions. However, few studies have explored their co-regulatory mechanisms in kidney renal clear cell carcinoma (KIRC). Here, we used bioinformatics approaches to evaluate the co-regulatory roles of TAZ/YAP and screen novel biomarkers in KIRC. GSE121689 and GSE146354 were downloaded from the GEO. The limma was applied to identify the differential expression genes (DEGs) and the Venn diagram was utilized to screen co-expressed DEGs. Co-expressed DEGs obtained the corresponding pathways through GO and KEGG analysis. The protein-protein interaction (PPI) network was constructed using STRING. The hub genes were selected applying MCODE and CytoHubba. GSEA was further applied to identify the hub gene-related signaling pathways. The expression, survival, receiver operating character (ROC), and immune infiltration of the hub genes were analyzed by HPA, UALCAN, GEPIA, pROC, and TIMER. A total of 51 DEGs were co-expressed in the two datasets. The KEGG results showed that the enriched pathways were concentrated in the TGF-β signaling pathway and endocytosis. In the PPI network, the hub genes (STAU2, AGO2, FMR1) were identified by the MCODE and CytoHubba. The GSEA results revealed that the hub genes were correlated with the signaling pathways of metabolism and immunomodulation. We found that STAU2 and FMR1 were weakly expressed in tumors and were negatively associated with the tumor stages. The overall survival (OS) and disease-free survival (DFS) rate of the high-expressed group of FMR1 was greater than that of the low-expressed group. The ROC result exhibited that FMR1 had certainly a predictive ability. The TIMER results indicated that FMR1 was positively correlated to immune cell infiltration. The abovementioned results indicated that TAZ/YAP was involved in the TGF-β signaling pathway and endocytosis. FMR1 possibly served as an immune-related novel prognostic gene in KIRC.