Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma.

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

CXCL10 could be a prognostic and immunological biomarker in bladder cancer.

INTRODUCTION: As proteins that promote immune cell differentiation, chemokines have attracted great interest regarding their role in anti-tumor immune responses within the cancer environment. However, the exact role of CXCL10, a chemokine, in bladder cancer (BLCA) is still not fully elucidated. METHOD: In the present study, we employed bioinformatics approaches to examine the expression pattern, prognostic value, and immune infiltration of CXCL10 in BLCA. Furthermore, we focused on examining the impact of CXCL10 on immune therapy in BLCA. Additionally, we validated the expression of CXCL10 in various BLCA cell lines using PCR techniques. RESULTS: We observed an upregulation of CXCL10 in BLCA tissues as well as in different cell lines. Additionally, upregulation of CXCL10 indicates a better prognosis for BLCA patients. ESTIMATE and CIBERSORT algorithms suggest that CXCL10 is closely associated with the immune microenvironment of BLCA. Through multiple immune therapy cohorts, we also identified that CXCL10 has shown promising predictive value for assessing the efficacy of immune therapy in in BLCA. CONCLUSION: Our study indicates that CXCL10 has the potential to serve as a favorable prognostic factor and is strongly associated with immune infiltration in BLCA.

Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation.

Background: Anoikis is a programmed cell death process that was proven to be associated with cancer. Uroepithelial carcinoma of the bladder (BLCA) is a malignant disease of the urinary tract and has a strong metastatic potential. To determine whether anoikis-associated genes can predict the prognosis of BLCA accurately, we evaluated the prognostic value of anoikis-associated genes in BLCA and constructed the best model to predict prognosis. Method: The BLCA transcriptome data were downloaded from TCGA and GEO databases, and genes with differential expression were selected and then clustered using non-negative matrix factorization (NMF). The genes with the most correlation with anoikis were screened and identified using univariate Cox regression, lasso regression, and multivariate Cox regression. The GEO dataset was used for external validation. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with receiver operating characteristic (ROC) curves. The immunotherapeutic significance of this risk score was assessed using the immune phenomenon score (IPS). IC50 values of predictive chemotherapeutic agents were calculated. Finally, we used RT-qPCR to determine the mRNA expression of four genes, CALR, FASN, CASP6, and RAD9A. Result: We screened 406 tumor samples and 19 normal tissue samples from the TCGA database. Based on anoikis-associated genes, we classified patients into two subtypes (C1 and C2) using NMF method. Subsequently, nine core genes were screened by multiple methods after analysis, which were used to construct risk profiles. The design of nomograms based on risk profiles and clinical variables, ROC, and calibration curves confirmed that the model could well have the ability to predict the survival of BLCA patients at 1, 3, and 5 years. By predicting the IC50 values of chemotherapeutic drugs, it was learned that the high-risk group (HRG) was more susceptible to paclitaxel, gemcitabine, and cisplatin, and the low-risk group (LRG) was more susceptible to veriparib and afatinib. Conclusion: In summary, the risk score of anoikis-associated genes can be applied as a predictor to predict the prognosis of BLCA in clinical practice.