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A Novel Ferroptosis-Based Molecular Signature Associated with Biochemical Recurrence-Free Survival and Tumor Immune Microenvironment of Prostate Cancer.

ABSTRACT: Objective: To identify ferroptosis-related molecular clusters, and to develop and validate a ferroptosis-based molecular signature for predicting biochemical recurrence-free survival (BCRFS) and tumor immune microenvironment of prostate cancer (PCa). Materials and Methods: The clinical data and transcriptome data of PCa were downloaded from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained from FerrDb database. We performed consensus clustering analysis to identify ferroptosis-related molecular subtypes for PCa. Univariate and multivariate Cox regression analysis were used to establish a ferroptosis-based signature for predicting BCRFS. Internal verification, external verification and subgroup survival analysis were then successfully performed. Results: There was a total of 40 differentially expressed FRGs in PCa. We then identified three ferroptosis-related molecular clusters of PCa, which have significantly different immune infiltrating cells, tumor immune microenvironment and PD-L1 expression level. More importantly, a novel ferroptosis-based signature for predicting BCRFS of PCa based on four FRGs (including ASNS, GPT2, NFE2L2, RRM2) was developed. Internal and external verifications were then successfully performed. Patients with high-risk score were associated with significant poor BCRFS compared with those with low-risk score in training cohort, testing cohort and validating cohort, respectively. The area under time-dependent Receiver Operating Characteristic (ROC) curve were 0.755, 0.705 and 0.726 in training cohort, testing cohort and validating cohort, respectively, indicating the great performance of this signature. Independent prognostic analysis indicated that this signature was an independent predictor for BCRFS of PCa. Subgroup analysis revealed that this signature was particularly suitable for younger or stage T III-IV or stage N0 or cluster 1-2 PCa patients. Patients with high-risk score have significantly different tumor immune microenvironment in comparison with those with low-risk score. The results of qRT-PCR successfully verified the mRNA expression levels of ASNS, GPT2, RRM2 and NFE2L2 in DU-145 and RWPE-1 cells while the results of IHC staining exactly verified the relative protein expression levels of ASNS, GPT2, RRM2 and NFE2L2 between PCa and BPH tissues. Conclusions: This study successfully identified three ferroptosis-related molecular clusters. Besides, we developed and validated a novel ferroptosis-based molecular signature, which performed well in predicting BCRFS and tumor immune microenvironment of PCa.

SUBMITTER: Ke ZB

PROVIDER: S-EPMC8773967 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Findings from numerous studies have revealed that ferroptosis is closely related to tumorigenesis and immune cell infiltration. Long non-coding RNAs (lncRNAs) are reportedly involved in the progression of various cancers, including prostate cancer (PCa). This study was designed to establish a ferroptosis-related lncRNA (frlncRNA) signature to predict PCa prognosis. Methods The frlncRNAs were identified by studying their expression by Pearson’s correlation analysis. Differentially expressed prognosis related frlncRNAs were identified by the Wilcoxon test and univariate Cox regression analysis. The LASSO Cox regression model was used to build a model to predict biochemical recurrence (BCR) based on frlncRNAs. The GSEA software (version 4.1.0) was used to explore the enriched pathways in high- and low- risk groups. Patients with PCa were clustered into different subgroups by unsupervised clustering based on the frlncRNAs considered in the prognostic model. Real-time PCR and CCK8 assays were performed to verify the expression and function of frlncRNAs. Results We identified 35 differentially expressed prognosis related frlncRNAs based on data on PCa from TCGA. A risk signature based on five frlncRNAs (AP006284.1, AC132938.1, BCRP3, AL360181.4 and AL135999.1), was confirmed to perform well in predicting BCR. The high-risk group had higher disease grades and a greater number of infiltrating immune cells. Besides this, we found that the five frlncRNAs were connected with typical immune checkpoints. With respect to molecular mechanisms, several metabolic pathways were found to enriched in the low-risk group. Furthermore, patients could be classified into different subtypes with different PSA-free times using the five frlncRNAs. Notably, AP006284.1, AC132938.1, BCRP3 and AL135999.1 were upregulated in PCa cells and tissues, whereas AL360181.4 exhibited the opposite trend. The downregulation of BCRP3 and AP006284.1 impaired the proliferation of 22RV1 cells. Conclusion We generated a prognostic model based on five frlncRNAs, with clinical usefulness, and thus provided a novel strategy for predicting the BCR of patients with PCa. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09876-8.

Project description:Prostate cancer (PCa) is one of the malignant tumors of urinary system with a high morbidity. Enhancer RNA is a subclass of long non-coding RNA transcribed from active enhancer regions, which plays a critical role in gene transcriptional regulation. However, the role of enhancer RNA (eRNA) in PCa remains extremely mysterious. This study is aimed at exploring key prognostic eRNAs in PCa. First, we downloaded gene expression data and clinical data of 33 cancer types from UCSC Xena platform. Second, we selected reported putative eRNA-target pairs and performed the Kaplan-Meier survival and correlation analysis to determine the crucial eRNAs most related to biochemical recurrence (BCR)-free survival. Third, we explored the clinical characteristics with the key eRNA GAS1 adjacent regulatory RNA (GAS1RR) and performed a computational difference algorithm and the Cox regression analysis. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the underlying mechanisms. Finally, we used the pan-cancer data from The Cancer Genome Atlas (TCGA) and performed reverse transcription-quantitative polymerase chain reaction (RT-qPCR) of 18 pairs of specimens to prove the results we acquired. Among all 2695 putative eRNAs, 6 pairs of eRNA-target genes were prominently related to BCR-free survival. Growth arrest-specific protein 1 (GAS1) was a target gene of GAS1RR (r = 0.86, P < 0.001). Patients with low GAS1RR expression were likely to have unfavorable clinical characteristics. The result of computational Cox regression analysis demonstrated that GAS1RR may predict the prognosis of PCa independently. RT-qPCR results illuminated that GAS1RR and GAS1 were both downregulated in PCa tissues, and they show a strong positive correlation. GO and KEGG analyses revealed biological processes that GAS1RR was mainly associated with. Immune infiltration analysis indicated that GAS1RR expression is correlated with the infiltration level of six kinds of immune cells. Our results suggest that GAS1RR may be clinically useful in the prediction of PCa prognosis. Moreover, it may also be a prognostic predictor and theoretic target with great promise in PCa.

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A Novel Ferroptosis-Based Molecular Signature Associated with Biochemical Recurrence-Free Survival and Tumor Immune Microenvironment of Prostate Cancer.

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