Identifying Long Non-coding RNA of Prostate Cancer Associated With Radioresponse by Comprehensive Bioinformatics Analysis

Overcoming chemoresistance and radio resistance in prostate cancer: The emergent role of non-coding RNAs

Prostate cancer (PCa) continues to be a major health concern worldwide, with its resistance to chemotherapy and radiation therapy presenting major hurdles in successful treatment. While patients with localized prostate cancer generally have a good survival rate, those with metastatic prostate cancer often face a grim prognosis, even with aggressive treatments using various methods. The high mortality rate in severe cases is largely due to the lack of treatment options that can offer lasting results, especially considering the significant genetic diversity found in tumors at the genomic level. This comprehensive review examines the intricate molecular mechanisms governing resistance in PCa, emphasising the pivotal contributions of non-coding RNAs (ncRNAs). We delve into the diverse roles of microRNAs, long ncRNAs, and other non-coding elements as critical regulators of key cellular processes involved in CR & RR. The review emphasizes the diagnostic potential of ncRNAs as predictive biomarkers for treatment response, offering insights into patient stratification and personalized therapeutic approaches. Additionally, we explore the therapeutic implications of targeting ncRNAs to overcome CR & RR, highlighting innovative strategies to restore treatment sensitivity. By synthesizing current knowledge, this review not only provides a comprehension of the chemical basis of resistance in PCa but also identifies gaps in knowledge, paving the way for future research directions. Ultimately, this exploration of ncRNA perspectives offers a roadmap for advancing precision medicine in PCa, potentially transforming therapeutic paradigms and improving outcomes for patients facing the challenges of treatment resistance.

Identification of senescence-related lncRNA prognostic index correlating with prognosis and radiosensitivity in prostate cancer patients

BACKGROUND

An increasing number of studies are shown how crucial a role cellular senescence plays in tumor development. In this study, we developed a senescence-related lncRNA prognostic index (SRLPI) to forecast radiosensitivity and the probability of biochemical recurrence (BCR) in patients with prostate cancer (PCa).

METHODS

PCa cohorts in TCGA and GEO databases were downloaded. Senescence-and prognosis-related lncRNA with differential expression in tumor and normal samples were identified and used to establish the SRLPI score. Mutation landscape, function pathway, tumor stemness and heterogeneity and tumor immune microenvironment were also analyzed. We performed the analysis using R 3.6.3 and the appropriate tools.

RESULTS

A SRLPI score was constructed based on SNHG1 and MIAT in the TCGA cohort. Our classification of PCa patients into high- and low-risk groups was based on the median SRLPI score. When compared to the low-SRLPI group, the high-SRLPI group was more vulnerable to BCR (HR: 3.33). In terms of BCR-free survival and metastasis-free survival, the GSE116918 showed similar findings. Surprisingly, the SRLPI score demonstrated a high level of radiosensitivity for diagnosis (AUC: 0.98). Age, Gleason score, T stage, N stage, positive lymph nodes, and residual tumor were all significantly greater in patients with high SRLPI scores. Furthermore, this score was significantly related to markers of senescence. Protein secretion and androgen response were found to be substantially enriched in the low-SRLPI group, whereas E2F targets were found to be strongly enriched in the high-SRLPI group for pathway analysis. For the tumor microenvironment assessment, B cells, CD8+ T cells, immune score and TIDE score were positively related to SRLPI score while endothelial level was negatively associated with SRLPI score with statistical significance.

CONCLUSIONS

We developed a SRLPI score that was related to prognosis and radiosensitivity and might be helpful in clinical practice.

Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC

Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.

Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.

Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.

Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

LncRNAs as Theragnostic Biomarkers for Predicting Radioresistance in Cancer: A Systematic Review and Meta-Analysis

Background

Radioresistance is the major obstacle after cancer radiotherapy. The dysregulation of long non-coding RNAs (lncRNAs) was closely related the radioresistance response. This meta-analysis was aimed to interpret the relationship between lncRNAs and radiotherapy responses in different cancers.

Method

The studies were selected from databases including PubMed, ISI Web of Science, Embase, Google Scholar, PMC, and CNKI (China National Knowledge Infrastructure). The publication time was limited to before March 20, 2021. The hazard ratios (HRs) and 95% confidence interval were calculated with random-effects models. Subgroup analyses, sensitivity analyses, and publication bias were also conducted.

Result

Twenty-seven lncRNAs in 14 cancer types were investigated, in which 23 lncRNAs were upregulated and four lncRNAs were downregulated. Dysregulation of these lncRNAs were found to be related to radioresistance response. The pooled HR and 95% confidence interval for the combined up-regulated lncRNAs was 1.73 (95% CI=1.50-2.00; P< 0.01) and down-regulated lncRNAs was 2.09 (95% CI= 1.60-2.72; P< 0.01). The HR values of the subgroup analysis for glioma (HR= 2.22, 95% CI= 1.79-2.74; p< 0.01), non-small cell lung cancer (HR=1.48, 95% CI=1.18-1.85; P<0.01), nasopharyngeal carcinoma (HR=4.26; 95% CI= 1.58-11.46; P< 0.01), and breast cancer (HR=1.29; 95% CI= 1.08-1.54; P< 0.01) were obtained. Moreover, the expression of lncRNAs was significantly related to overall survival of patients no matter if the sample size was >50 or not. In addition, the HR values of the subgroup analysis for lncRNA H19 (HR=2.68; 95% CI= 1.92-3.74; P <0.01), lncRNA FAM201A (HR=2.15; 95% CI= 1.15-3.99; P <0.01), and lncRNA HOTAIR (HR=1.22; 95% CI= 0.98-1.54; P =0.08) were also obtained.

Conclusion

LncRNAs can induce cancer radioresistance by regulating cell death-related signaling pathways. Results indicated that lncRNAs, especially lncRNA H19, FAM201A, and HOTAIR, could be considered as a predictive theragnostic biomarker to evaluate radiotherapy response.

An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients

Purpose

We developed a strategy of building prognosis gene signature based on clinical treatment responsiveness to predict radiotherapy survival benefit in breast cancer patients.

Methods and Materials

Analyzed data came from the public database. PFS was used as an indicator of clinical treatment responsiveness. WGCNA was used to identify the most relevant modules to radiotherapy response. Based on the module genes, Cox regression model was used to build survival prognosis signature to distinguish the benefit group of radiotherapy. An external validation was also performed.

Results

In the developed dataset, MEbrown module with 534 genes was identified by WGCNA, which was most correlated to the radiotherapy response of patients. A number of 11 hub genes were selected to build the survival prognosis signature. Patients that were divided into radio-sensitivity group and radio-resistant group based on the signature risk score had varied survival benefit. In developed dataset, the 3-, 5-, and 10-year AUC of the signature were 0.814 (CI95%: 0.742–0.905), 0.781 (CI95%: 0.682–0.880), and 0.762 (CI95%: 0.626–0.897), respectively. In validation dataset, the 3- and 5-year AUC of the signature were 0.706 (CI95%: 0.523–0.889) and 0.743 (CI95%: 0.595–0.891). The signature had higher predictive power than clinical factors alone and had more clinical prognosis efficiency. Functional enrichment analysis revealed that the identified genes were mainly enriched in immune-related processes. Further immune estimated analysis showed the difference in distribution of immune micro-environment between radio-sensitivity group and radio-resistant group.

Conclusions

The 11-gene signature may reflect differences in tumor immune micro-environment that underlie the differential response to radiation therapy and could guide clinical-decision making related to radiation in breast cancer patients.

The Influence of miRNAs on Radiotherapy Treatment in Prostate Cancer - A Systematic Review

In the last years, extensive investigation on miRNomics have shown to have great advantages in cancer personalized medicine regarding diagnosis, treatment and even clinical outcomes. Prostate cancer (PCa) is the second most common male cancer and about 50% of all PCa patients received radiotherapy (RT), despite some of them develop radioresistance. Here, we aim to provide an overview on the mechanisms of miRNA biogenesis and to discuss the functional impact of miRNAs on PCa under radiation response. As main findings, 23 miRNAs were already identified as being involved in genetic regulation of PCa cell response to RT. The mechanisms of radioresistance are still poorly understood, despite it has been suggested that miRNAs play an important role in cell signaling pathways. Identification of miRNAs panel can be thus considered an upcoming and potentially useful strategy in PCa diagnosis, given that radioresistance biomarkers, in both prognosis and therapy still remains a challenge.

Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples

Small cell lung cancer (SCLC) is a carcinoma of the lungs with strong invasion, poor prognosis and resistant to multiple chemotherapeutic drugs. It has posed severe challenges for the effective treatment of lung cancer. Therefore, searching for genes related to the development and prognosis of SCLC and uncovering their underlying molecular mechanisms are urgent problems to be resolved. This study is aimed at exploring the potential pathogenic and prognostic crucial genes and key pathways of SCLC via bioinformatic analysis of public datasets. Firstly, 117 SCLC samples and 51 normal lung samples were collected and analyzed from three gene expression datasets. Then, 102 up-regulated and 106 down-regulated differentially expressed genes (DEGs) were observed. And then, functional annotation and pathway enrichment analyzes of DEGs was performed utilizing the FunRich. The protein-protein interaction (PPI) network of the DEGs was constructed through the STRING website, visualized by Cytoscape. Finally, the expression levels of eight hub genes were confirmed in Oncomine database and human samples from SCLC patients. It showed that CDC20, BUB1, TOP2A, RRM2, CCNA2, UBE2C, MAD2L1, and BUB1B were upregulated in SCLC tissues compared to paired adjacent non-cancerous tissues. These suggested that eight hub genes might be viewed as new biomarkers for prognosis of SCLC or to guide individualized medication for the therapy of SCLC.