Predicting prostate adenocarcinoma patients' survival and immune signature: a novel risk model based on telomere-related genes

Abstract

Alterations in telomeres constitute some of the earliest occurrences in the tumourigenesis of prostate adenocarcinoma (PRAD) and persist throughout the progression of the tumour. While the activity of telomerase and the length of telomeres have been demonstrated to correlate with the prognosis of PRAD, the prognostic potential of telomere-related genes (TRGs) in this disease remains unexplored. Utilising mRNA expression data from the Cancer Genome Atlas (TCGA), we devised a risk model and a nomogram to predict the survival outcomes of patients with PRAD. Subsequently, our investigations extended to the relationship between the risk model and immune cell infiltration, sensitivity to chemotherapeutic drugs, and specific signalling pathways. The risk model we developed is predicated on seven key TRGs, and immunohistochemistry results revealed significant differential expression of three TRGs in tumours and paracancerous tissues. Based on the risk scores, PRAD patients were stratified into high-risk and low-risk cohorts. The Receiver operating characteristics (ROC) and Kaplan-Meier survival analyses corroborated the exceptional predictive performance of our novel risk model. Multivariate Cox regression analysis indicated that the risk score was an independent risk factor associated with Overall Survival (OS) and was significantly associated with T and N stages of PRAD patients. Notably, the high-risk group exhibited a greater response to chemotherapy and immunosuppression compared to the low-risk group, offering potential guidance for treatment strategies for high-risk patients. In conclusion, our new risk model, based on TRGs, serves as a reliable prognostic indicator for PRAD. The model holds significant value in guiding the selection of immunotherapy and chemotherapy in the clinical management of PRAD patients.

Keywords: Immune checkpoint inhibitors; Immune microenvironment; Prostate adenocarcinoma; Risk model; Telomere.

Fig. 1

The overview design in this study

Fig. 2

Enrichment analysis of TRGs associated with PRAD prognosis. A, B Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the “Clusterprofiler” R package. C Metascape-based GO-KEGG pathway enrichment analysis. The lower panel shows the cluster network composed of enriched pathways, where nodes sharing the same cluster are usually close to each other

Fig. 3

The risk model was constructed in the Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) cohort and validated in the Memorial Sloan-Kettering Cancer (MSKCC) and Gene Expression Omnibus (GEO) cohorts. A Ten-fold cross-validation was used to adjust the parameter selection for the LASSO model to determine the minimum λ value. B Distribution of LASSO coefficients for TRGs and gene combinations with minimum λ values. C Seven model genes (GTF2H4, THRSP, BUB3, LARP7, SRC, TOP3A, and HELLS) were selected using the LASSO regression algorithm. D–F Survival curves were constructed based on the model genes in the TCGA-PRAD (p = 0.002), MSKCC (p < 0.005), and GEO (p = 0.024) cohorts. G–I Receiver operating characteristic (ROC) analysis

Fig. 4

Expression differences and specific regulatory mechanisms of the model genes. A Differential expression levels of the model genes at different clinical T stages (T2, T3, and T4). B Differential expression levels of the model genes at different clinical N stages (N0 and N1). C, D Comparison of mRNA expression levels for the critical genes between PRAD and normal tissue in TCGA and GEO cohorts. The seven critical genes used to construct the risk model are represented on the horizontal axis, and the gene expression levels calculated using log2 (FPKM + 1) are represented on the vertical axis. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. E Representative immunohistochemical images of the seven model genes in PRAD tissues and adjacent normal tissues (n = 3). Scale bars, 100 μm (left) and 20 μm (right). F Statistical analysis of the average OD value based on stained areas. G Cumulative recovery curves for four base sequences with high AUC values. The red line represents the mean of the recovery curve for each motif, while the green line represents the mean ± standard deviation (SD). The blue line represents the recovery curve of the current motif. The maximum enrichment level is the maximum distance point (mean + SD) between the current motif and the green curve

Fig. 5

The prognostic value and clinical relevance of the risk model. A Single- and multi-factor regression forest plots. Red indicates risk factors, and green indicates protective factors. B Comparison of the ROC curve and area under the curve (AUC) values between the risk score and other clinical factors. C Clinical correlation between risk score and PRAD. Differences between risk score and T, N, age, and Fustat (p < 0.05 was considered statistically significant)

Fig. 6

Construction of the risk score-related nomogram model. A The nomogram model was constructed using risk scores and other clinical characteristics. B 3- and 5-year nomogram calibration curves for OS. C ROC curves for predicting 3- and 5-year survival. D Comparison of the ROC curve and AUC values of 3-year survival between the nomogram and other clinical factors. E Comparison of the ROC curve and AUC values of 5-year survival between the nomogram and other clinical factors

Fig. 7

Differences in signaling pathways between the high- and low-risk groups. A, B GO-KEGG pathways enriched in high- and low-risk groups. C Pathway activity was scored using gene set variance analysis (GSVA), with Hallmark as the background gene set

Fig. 8

Analysis of tumor microenvironment and immune correlation. A Differences in immune cell proportion between patients in the high- and low-risk groups. Blue indicates patients in the low-risk group, and yellow indicates patients in the high-risk group (*p < 0.05, **p < 0.01, and ***p < 0.001). B Correlation between risk score and immune cells. C Pearson's correlation among 22 high- and low-risk immune cell types. D Correlation between seven model genes and 21 immune cell types. E Correlation between immune ssGSEA scores and model gene expression

Fig. 9

Correlation between risk score and immune-related genes. A–D Differences in the expression of immune-related genes in samples from high- and low-risk groups (*p < 0.05, **p < 0.01, and ***p < 0.001). E–G Tumor Immune Dysfunction and Exclusion (TIDE) estimation showing CD274, microsatellite instability (MSI), and TIDE scores. Abbreviations: CD274, the cluster of differentiation 274, also called PD-L1

Fig. 10

Correlation of risk score and sensitivity of common chemotherapeutic agents