Expression status and prognostic value of autophagy-related lncRNAs in prostate cancer

Autophagy-related long non-coding RNAs act as prognostic biomarkers and associate with tumor microenvironment in prostate cancer

Aberrant autophagy could promote cancer cells to survive and proliferate in prostate cancer (PCa). LncRNAs play key roles in autophagy regulatory network. We established a prognostic model, which autophagy-related lncRNAs (au-lncRNAs) were used as biomarkers to predict prognosis of individuals with PCa. Depending on au-lncRNAs from the Cancer Genome Atlas and the Human Autophagy Database, a risk score model was created. To evaluate the prediction accuracy, the calibration, Kaplan-Meier, and receiver operating characteristic curves were used. To clarify the biological function, gene set enrichment analyses (GSEA) were performed. Quantitative real-time PCR (qRT-PCR) was employed to determine the au-lncRNAs expression in PCa cell lines and healthy prostate cells for further confirmation. We identified five au-lncRNAs with prognostic significance (AC068580.6, AF131215.2, LINC00996, LINC01125 and LINC01547). The development of a risk scoring model required the utilization of multivariate Cox analysis. According to the model, we categorized PCa individuals into low- and high-risk cohorts. PCa subjects in the high-risk group had a worse disease-free survival rate than those in the low-risk group. The 1-, 3-, and 5-year periods had corresponding areas under curves (AUC) of 0.788, 0.794, and 0.818. The prognosis of individuals with PCa could be predicted by the model with accuracy. Further analysis with GSEA showed that the prognostic model was associated with the tumor microenvironment, including immunotherapy, cancer-related inflammation, and metabolic reprogramming. Four lncRNAs expression in PCa cell lines was greater than that in healthy prostate cells. The au-lncRNA prognostic model has significant clinical implications in prognosis of PCa patient.

Construction of lncRNA prognostic model related to cuproptosis in esophageal carcinoma

Background: Esophageal carcinoma (ESCA) is one of the most prevalent malignant tumors in the world. The prognosis of patients has significantly improved with the development of surgery, targeted therapy and immunotherapy. But the 5-year survival rate of ESCA patients is still incredibly low. Cuproptosis is a type of mitochondrial cell death induced by copper. It is unclear how cuproptosis-related lncRNAs (CRLs) affect ESCA prognosis. Methods: In this study, we obtained the clinical data of ESCA patients, the transcriptome data from TCGA and identified CRLs by co-expression analysis, lasso regression, and cox regression analysis, to build a prognostic model. Then we validated the prognostic model using the Kaplan-Meier curve, cox regression analysis, and ROC, to create a nomogram based on risk score to forecast the prognosis of ESCA. Next, the immune escape of the CRLs was examined using the TIDE algorithm to assess its sensitivity to possible ESCA medications. Results: To predict the prognosis of ESCA patients, we created a predictive model using 6 CRLs (AC034199.1, AC125437.1, AC107032.2, CTBP1-DT, AL024508.1, and AC008610.1), validated by the Kaplan-Meier and ROC curves. The model has a higher diagnostic value compared to other clinical features. The 6 CRLs expressed high in TCGA and ESCA specimens. Enrichment analysis revealed CRLs largely contributed to the interaction between cytokines and their receptors as well as complement coagulation cascades. The immunity escape analysis demonstrated that immunotherapy had a worse effect in the low-risk group than in the high-risk group. Additionally, we screened out potential antineoplastic drugs according to the results of the immunoassay and obtained 5 drugs, including CP-466722, crizotinib, MS-275, KIN001-135, and CP-466722. Conclusion: The prognosis of patients with ESCA can be correctly predicted by the 6 CRLs chosen from this investigation. It lays the groundwork for more investigation into the ESCA mechanism and the identification of novel therapeutic targets.

Correlations between serum levels of microRNA-148a-3p and microRNA-485-5p and the progression and recurrence of prostate cancer

BACKGROUND

Unpredicted postoperative recurrence of prostate cancer, one of the most common malignancies among males worldwide, has become a prominent issue affecting patients after treatment. Here, we investigated the correlation between the serum miR-148a-3p and miR-485-5p expression levels and cancer recurrence in PCa patients, aiming to identify new biomarkers for diagnosis and predicting postoperative recurrence of prostate cancer.

METHODS

A total of 198 male PCa cases treated with surgery, postoperative radiotherapy, and chemotherapy were involved in the presented study. Serum levels of miR-148a-3p and miR-485-5p were measured before the initial operation for the involved cases, which were then followed up for two years to monitor the recurrence of cancer and to split the cases into recurrence and non-recurrence groups. Comparison of the relative expressions of serum miR-148a-3p and miR-485-5p were made and related to other clinic pathological features.

RESULTS

Pre-surgery serum levels of miR-148a-3p in patients with TNM stage cT1-2a prostate cancer (Gleason score < 7) were significantly lower (P < 0.05) than levels in patients with TNM Classification of Malignant Tumors (TNM) stage cT2b and higher prostate cancer (Gleason score ≥ 7). pre-surgery serum levels of miR-485-5p in patients with TNM stage cT1-2a prostate cancer (Gleason score < 7) were significantly higher (P < 0.05) than in patients with TNM stage cT2b and higher cancer (Gleason score ≥ 7). Serum miR-148a-3p level in recurrence group is higher than the non-recurrence group (P < 0.05) while serum miR-485-5p level in recurrence group is lower than non-recurrence group (P < 0.05). ROC curve analysis showed the AUCs of using miR-148a-3p, miR-485-5p, and combined detection for predicting recurrence of prostate cancer were 0.825 (95% CI 0.765-0.875, P < 0.0001), 0.790 (95% CI 0.726-0.844, P < 0.0001), and 0.913 (95% CI 0.865-0.948, P < 0.0001).

CONCLUSION

Pre-surgery serum miR-148a-3p level positively correlates while miR-485-5p level negatively correlates with prostate cancer's progressing and postoperative recurrence. Both molecules show potential to be used for predicting postoperative recurrence individually or combined.

SLC25A25-AS1 over-expression could be predicted the dismal prognosis and was related to the immune microenvironment in prostate cancer

It has been established that long-chain coding RNA (lncRNA) SLC25A25-AS1 is associated with cancer progression. However, the roles and mechanisms of SLC25A25-AS1 in prostate cancer (PC) have not been reported in the literature. The present study explored the relationship between SLC25A25-AS1 expression and PC progression via comprehensive analysis. The pan-cancer expression of SLC25A25-AS1 was identified using data from The Cancer Genome Atlas (TCGA) database and tissue specimens from our hospital. The expression levels of SLC25A25-AS1 in various subgroups based on the clinical features were identified. The prognostic value of SLC25A25-AS1 and SLC25A25-AS1 co-expressed lncRNAs in PC patients was assessed by survival analysis and ROC analysis, and prognosis-related risk models of SLC25A25-AS1 were constructed. The relationship between SLC25A25-AS1 and the PC immune microenvironment was investigated using correlation analysis. SLC25A25-AS1 expression in PC was significantly increased and correlated with the T stage, clinical stage, Gleason score (GS), and dismal prognosis. SLC25A25-AS1 overexpression exhibited good performance in evaluating the prognosis of PC patients. The area under the curves (AUCs) of the 1-, 3-, and 5-year overall survival (OS) for SLC25A25-AS1 was 1, 0.876, and 0.749. Moreover, the AUCs for the 1-, 3-, and 5-year progress free interval (PFI) for SLC25A25-AS1 were 0.731, 0.701, and 0.718. SLC25A25-AS1 overexpression correlated with the infiltration of CD8 T cells, interstitial dendritic cells (IDC), macrophages and other cells. AC020558.2, ZNF32-AS2, AP4B1-AS1, AL355488.1, AC109460.3, SNHG1, C3orf35, LMNTD2-AS1, and AL365330.1 were significantly associated with SLC25A25-AS1 expression, and short OS and PFI in PC patients. The risk models of the SLC25A25-AS1-related lncRNAs were associated with a dismal prognosis in PC. Overall, SLC25A25-AS1 expression was increased in PC and related to the prognosis and PC immune microenvironment. The risk model of SLC25A25-AS1 have huge prospect for application as prognostic tools in PC.

Prognostic significance of lncRNA AP004608.1 in prostate cancer

This study aimed to screen and determine the value of AP004608.1 expression as a biomarker for Prostate cancer (PCa) survival. We investigated the expression and prognosis of AP004608.1 through bioinformatics analysis. Low AP004608.1 expression predicted favorable Overall survival (OS) and Progression-free survival (PFS) in PCa patients, according to the Cancer Genome Atlas (TCGA) database. Cox regression demonstrated that low AP004608.1 expression were in-dependent biomarkers for OS. Moreover, Gene Expression Omnibus (GEO) database was utilized to verify the prognostic role of AP004608.1 in PCa, and the similar results were reached. A meta-analysis revealed that low AP004608.1 expression was closely relevant to better OS. AP004608.1 could constitute a promising prognostic biomarker, and probably plays an important role in PCa.

Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer

Background

Increasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood.

Methods

Patients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (GILncSig) was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan–Meier (K-M) analysis and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF–lncRNA interactions.

Results

A total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the low-risk (n = 121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value.

Conclusion

In summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.