A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

Abstract

Background. Prostate cancer (PC) is one of the most critical cancers affecting men’s health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs. Materials and Methods. In this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package “pRRophetic” was applied to drug sensitivity (IC50) value prediction. Results. We screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (EGR1), CACNA2D1, AC005831.1, SLC52A3, TMEM79, IL20RA, CRACR2A, and FAM189A2 expressions in the high-risk score group were decreased, while AC012181.1 and TRAPPC8 expressions were increased. GO and KEGG analyses showed that prognosis was related to various cancer signaling pathways. The proportion of B_cell, T_cell_CD4, and macrophages in the high-risk score group was significantly higher than that in the low-risk score group. A total of 25 classic immune checkpoint genes were screened out to express abnormally high-risk scores, and there were significant differences. Thirty mutant genes were identified; in the high- and low-risk score groups, SPOP, TP53, and TTN had the highest mutation frequency, and their mutations were mainly missense mutations. A total of 36 potential drug candidates for the treatment of PC were screened and identified. Conclusions. Ten genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders. SPOP, TP53, and TTN may be potential targets for the prognosis of PC.

A comprehensive pan-cancer analysis and prediction model revealed that FAM189A2 is an immune-related gene and a prognostic biomarker for esophageal adenocarcinoma

“…A study conducted in China over a 24-year period revealed that ESCC constituted around 95% of esophageal cancer cases in the country, whereas the incidence of EAC was relatively low. However, due to rapid economic growth and the adoption of more Westernized diets and lifestyles, the occurrence of EAC has been increasing annually in both urban and rural areas of China (Rai et al, 2023;Luo et al, 2021;Liang et al, 2017). This trend necessitates the attention of researchers in cancer prevention and treatment.…”

PRLR and CACNA2D1 Impact the Prognosis of Breast Cancer by Regulating Tumor Immunity

“…In line with these findings, this study showed that it was functionally implicated in myofibril formation, adhesive spots, and the development of muscular structure. Prior studies found that CACNA2D1 played a certain role in the carcinogenesis of prostate cancer [45], bladder cancer [46], epithelial ovarian cancer [47], laryngeal squamous cell cancer [48], gastric cancer [49], non-small-cell cancer [50], and other cancers, but very few studies examined the association between CACNA2D1 and BC. Meng Li et al showed that the cells overexpressing α2δ1 were involved in the self-renewal, differentiation, invasion, and metastasis of the tumor.…”

To explore the potential diagnostic and prognostic value of Golgi related genes in prostate cancer

“…treatment of PCa bone metastases [29].The development of many cancers involves dysregulation or mutations in key transcription factors. Ruixiang Luo et al established a risk model based on transcription factors to predict that the prognosis of PCa may be related to immune disorders, and SPOP, TP53 and TTN may be potential targets of PCa prognosis [30].PD-1/PD-L1 checkpoint inhibitors such as pembrolizumab and durvalumab enhance the T cell response by inhibiting receptor and ligand binding, stimulating the body's immune system to attack tumor cells [31].Immunomodulatory drugs, such as lenalidomide and Taquinomide, can improve the tumor microenvironment and enhance the anti-tumor immune response.These results all suggest that the single factor we constructed the model has an important correlation with the occurrence and development of tumors.…”