The prognostic value of hedgehog signaling in bladder cancer by integrated bioinformatics

Abstract

Bladder cancer is the second most prevalent urological malignancy. It's a big contributor to cancer-related deaths throughout the globe. Researchers discovered that the hedgehog signaling (HhS) pathway contributed to the onset and spread of many different kinds of cancer. Nevertheless, the present understanding of the function of HhS in the bladder cancer molecular landscape is incomplete. Raw data were gotten from the IMvigor210, the Gene Expression Omnibus, and The Cancer Genome Atlas databases. Bioinformatics was used to examine the HhS score of each sample, and the enrichment of differentially expressed genes (DEGs), differentiation characteristics, immunological infiltration, and metabolic activity. The HhS prognostic signature was developed with significant assistance from the least absolute shrinkage and selection operator regression and Cox regression. An HhS-related nomogram was developed to assist in the prediction of patients’ survival probability. We found that HhS was linked to poor prognosis in bladder cancer, and its activation was linked to the Basal subtype of bladder cancer. Bladder cancer with high HhS activity has higher glycolysis, nucleotide metabolism, amino acid metabolism, and other cancer-promoting metabolic activities. Furthermore, HhS mediates an immunosuppressive microenvironment in bladder cancer on the basis that HhS negatively correlates with the CD8 + T cells and correlates positively with immune checkpoints and T cell exhaustion scores. Finally, an HhS-related signature was developed for predicting the prognosis of patients with bladder cancer. Targeting HhS may be a potential therapy choice for bladder cancer.

Investigating the prognostic value of mTORC1 signaling in bladder cancer via bioinformatics evaluation

“…We used the Seurat R package to perform unsupervised clustering of individual cells based on the read count matrix as input. Data quality control, cell clustering, and annotation were executed based on the scRNA-seq data as previously described 20 . For intercellular communication network-related analysis, the iTalk R package was employed.…”