Elucidating the role of MICAL1 in pan-cancer using integrated bioinformatics and experimental approaches.

Molecule interacting with CasL 1 (MICAL1) is a crucial protein involved in cell motility, axon guidance, cytoskeletal dynamics, and gene transcription. This pan-cancer study analyzed MICAL1 across 33 cancer types using bioinformatics and experiments. Dysregulated expression, diagnostic potential, and prognostic value were assessed. Associations with tumor characteristics, immune factors, and drug sensitivity were explored. Enrichment analysis revealed MICAL1's involvement in metastasis, angiogenesis, metabolism, and immune pathways. Functional experiments demonstrated its impact on renal carcinoma cells. These findings position MICAL1 as a potential biomarker and therapeutic target in specific cancers, warranting further investigation into its role in cancer pathogenesis.

Identification and validation of tumor-infiltrating lymphocyte-related prognosis signature for predicting prognosis and immunotherapeutic response in bladder cancer.

BACKGROUND: It has been discovered that tumor-infiltrating lymphocytes (TILs) are essential for the emergence of bladder cancer (BCa). This study aimed to research TIL-related genes (TILRGs) and create a gene model to predict BCa patients' overall survival. METHODS: The RNA sequencing and clinical data were downloaded from the TGCA and GEO databases. Using Pearson correlation analysis, TILRGs were evaluated. Moreover, hub TILRGs were chosen using a comprehensive analysis. By dividing the TCGA-BCa patients into different clusters based on hub TILRGs, we were able to explore the immune landscape between different clusters. RESULTS: Here, we constructed a model with five hub TILRGs and split all of the patients into two groups, each of which had a different prognosis and clinical characteristics, TME, immune cell infiltration, drug sensitivity, and immunotherapy responses. Better clinical results and greater immunotherapy sensitivity were seen in the low-risk group. Based on five hub TILRGs, unsupervised clustering analysis identify two molecular subtypes in BCa. The prognosis, clinical outcomes, and immune landscape differed in different subtypes. CONCLUSIONS: The study identifies a new prediction signature based on genes connected to tumor-infiltrating lymphocytes, providing BCa patients with a new theoretical target.

Promising Therapeutic Targets in Kidney Renal Clear Cell Carcinoma: PLXNA1 and PLXNB3.

Aims: This study sets out to identify dysregulated plexins and investigate their roles in KIRC through an integrated bioinformatics approach. Methods: RNA-sequencing data and clinicopathological information of KIRC, extracted from The Cancer Genome Atlas (TCGA) database, were used to perform comprehensive bioinformatics analysis. Results: Almost all plexin gene family members were dysregulated in KIRC. Univariate and multivariate Cox regression analyses revealed that PLXNA1/B3 were independent prognostic factors of overall survival in patients with KIRC. Mechanically, PLXNA1/B3 may promote ccRCC progression through several cancer-related signaling pathways, tumor immunity, and angiogenesis. Drug sensitivity analysis suggested that vemurafenib was the potential drug for PLXNA1/B3. Conclusion: Herein, we found that PLXNA1/B3 were independent prognostic factors, making them attractive new targets for KIRC treatment.

CARS as a Prognosis Factor in Clear Cell Renal Cell Carcinoma and Its Association with Tumor Immunity.

AIM: The aim of this study was to investigate the expression of CARS (cysteinyl-tRNA synthetase) in clear cell renal cell carcinoma (ccRCC) and its biological action mechanisms. METHODS: Expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA) to estimate the CARS expression patterns in ccRCC, its relationship with clinicopathological variables, and prognosis of ccRCC and potential biological mechanisms in ccRCC. RESULTS: CARS was significantly elevated in ccRCC. Overexpression of CARS indicated disease progression. Univariate and multivariate Cox regression analyses identified CARS as an independent prognostic factor for overall survival (OS) in renal clear cell carcinoma. Mechanically, CARS influenced the progression of ccRCC through several tumor-related pathways. Additionally, we found that CARS was significantly associated with tumor mutational burden, tumor-infiltrating immune cells, immunosuppressive molecules, methyltransferases, and mismatch repair proteins. CONCLUSION: CARS could serve as a promising prognostic biomarker and therapeutic target for ccRCC.

IQGAP3 May Serve as a Promising Biomarker in Clear Cell Renal Cell Carcinoma.

AIM: The study was designed to mine the expression and roles of IQGAP3 in clear cell renal cell carcinoma (ccRCC). METHODS: Expression profiles and clinical information were obtained from the Cancer Genome Atlas (TCGA) to estimate IQGAP3 expression in ccRCC, its relationship with patients' clinicopathological variables and prognosis, and the potential biological mechanisms. RESULTS: IQGAP3 was highly expressed in ccRCC and indicated advanced clinical outcome and poor prognosis. IQGAP3 affected the progression of ccRCC through several cancer-related pathways. IQGAP3 might play a vital role in the ccRCC tumor microenvironment. CONCLUSION: IQGAP3 could serve as a promising prognostic biomarker and therapeutic target.

KDELC1 and TRMT1 Serve as Prognosis-Related SARS-CoV-2 Proteins Binding Human mRNAs and Promising Biomarkers in Clear Cell Renal Cell Carcinoma.

BACKGROUND: SARS-CoV-2 proteins binding human mRNAs (SPBRs) have been proven to regulate a variety of tumor-related functions in different types of cancer. However, their biological roles and potential mechanisms in clear cell renal cell carcinoma (ccRCC) are still elusive. Herein, we investigate the expression and prognostic value of SPBRs in ccRCC through bioinformatics methods. METHODS: Data downloaded from the Cancer Genome Atlas (TCGA) database was used to screen differentially expressed SPBRs (DE-SPBRs) between ccRCC samples and noncancerous samples. Metascape was utilized to perform function and pathway enrichment analyses of these DE-SPBRs. Kaplan-Meier method of overall survival (OS) was used to assess the prognostic value of DE-SPBRs in ccRCC patients. Univariate and multivariate Cox regression analyses were applied to identify candidate SPBRs, which were independently associated with overall survival of ccRCC patients. Subsequently, several internationally renowned databases were employed to conduct a comprehensive analysis of candidate SPBRs to further investigate their roles and mechanisms in ccRCC. RESULTS: A total of 33 DE-SPBRs, including 18 upregulated SPBRs and 17 downregulated SPBRs, were screened between ccRCC samples and noncancerous samples. Among them, two candidate SPBRs, KDELC1 and TRMT1, were identified. Additionally, we observed that upregulated KDELC1/TRMT1 expression in ccRCC at both gene and protein levels was significantly associated with clinicopathological features. Furthermore, we found that KDELC1/TRMT1 genetic mutation has an unfavorable influence on prognosis of patients with ccRCC. Functional enrichment analysis revealed that KDELC1/TRMT1 was closely enriched in several vital biological processes and pathways. Finally, we noticed that KDELC1/TRMT1 was remarkably associated with immune infiltrates. CONCLUSION: In summary, we screened DE-SPBRs of ccRCC, which were enriched mainly in various biological and signaling pathways with tumor progression. Furthermore, we identified two candidate DE-SPBRs (KDELC1 and TRMT1), which could serve as promising biomarkers and therapeutic targets of patients with ccRCC.

KIF4A is a promising prognostic marker and correlates with immune infiltration in clear cell renal cell carcinoma.

BACKGROUND: Kinesin family member 4A (KIF4A) belongs to the kinesin family. It has been found to promote the proliferation and invasion of tumor cells and correlates with the poor prognosis of different types of human cancers. However, the expression and prognostic value of KIF4A in clear cell renal cell carcinoma (ccRCC) and its correlation with tumor-infiltrating immune cells are currently unclear. METHODS: Here, we analyzed the expression data of KIF4A in different types of tumors on the TIMER database. The Kaplan-Meier curve was utilized to reveal the correlation between KIF4A expression and the clinical prognosis of ccRCC patients. UALCAN database was utilized to evaluate the relationship between KIF4A expression and the clinicopathological features of ccRCC patients. In addition, the correlation between KIF4A expression and the abundance of immune infiltrates, as well as gene markers of tumor-infiltrating immune cells was determined on the TIMER database. RESULTS: Various types of malignant tumors, including kidney renal clear cell carcinoma (KIRC), show high levels of KIF4A expression. The high expression of KIF4A was correlated with the worse prognosis, advanced clinical stage, poorer differentiation, and higher levels of immune infiltration in KIRC. CONCLUSIONS: In summary, KIF4A is a promising prognostic marker and correlates with immune infiltration in clear cell renal cell carcinoma. However, further molecular and cellular experimental evidence is required to validate these conclusions.

The expression and prognostic value of RNA binding proteins in clear cell renal cell carcinoma.

BACKGROUND: RNA binding proteins (RBPs) have previously been demonstrated to be involved in the initiation and development of human cancers. However, its role in clear cell renal cell carcinoma (ccRCC) is not yet clear. The study was intended to explore the diagnostic and prognostic value of RBPs in ccRCC via bioinformatics methods of public datasets. METHODS: Data download from the Cancer Genome Atlas (TCGA) database was used to identify differentially expressed RBPs between normal renal samples and cancerous samples. Then, we performed the gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of differentially expressed genes (DEGs) using the ClusterProfiler package. Next, the protein-protein interaction (PPI) network was built by the online tool STRING database and Cytoscape software. The significant module and hub genes were screened by MCODE and Cytohubba plugin, respectively. Lastly, we performed a systematical analysis to investigate the diagnostic and prognostic value of candidate RBPs. RESULTS: A total of 133 DEGs, including 39 upregulated RBPs and 94 downregulated RBPs, were screened between ccRCC samples and noncancerous samples. From these data, eight candidate RBPs (RPS2, GAPDH, RPS20, EIF4A1, RPL18, RPL13, RPL18A, and RPS19) were identified. CONCLUSIONS: In summary, we screened differentially expressed RBPs of ccRCC, which were enriched mainly in various biological processes and signaling pathways. Furthermore, we identified eight candidate RBPs, which could serve as potential biomarkers of ccRCC.