Long non-coding RNA PRRT3-AS1 silencing inhibits prostate cancer cell proliferation and promotes apoptosis and autophagy

Critical roles of lncRNA-mediated autophagy in urologic malignancies

Urologic oncology is a significant public health concern on a global scale. Recent research indicates that long chain non-coding RNAs (lncRNAs) and autophagy play crucial roles in various cancers, including urologic malignancies. This article provides a summary of the latest research findings, suggesting that lncRNA-mediated autophagy could either suppress or promote tumors in prostate, kidney, and bladder cancers. The intricate network involving different lncRNAs, target genes, and mediated signaling pathways plays a crucial role in urological malignancies by modulating the autophagic process. Dysregulated expression of lncRNAs can disrupt autophagy, leading to tumorigenesis, progression, and enhanced resistance to therapy. Consequently, targeting particular lncRNAs that control autophagy could serve as a dependable diagnostic tool and a promising prognostic biomarker in urologic oncology, while also holding potential as an effective therapeutic approach.

Comprehensive data mining reveals RTK/RAS signaling pathway as a promoter of prostate cancer lineage plasticity through transcription factors and CNV

Prostate cancer lineage plasticity is a key driver in the transition to neuroendocrine prostate cancer (NEPC), and the RTK/RAS signaling pathway is a well-established cancer pathway. Nevertheless, the comprehensive link between the RTK/RAS signaling pathway and lineage plasticity has received limited investigation. In particular, the intricate regulatory network governing the interplay between RTK/RAS and lineage plasticity remains largely unexplored. The multi-omics data were clustered with the coefficient of argument and neighbor joining algorithm. Subsequently, the clustered results were analyzed utilizing the GSEA, gene sets related to stemness, multi-lineage state datasets, and canonical cancer pathway gene sets. Finally, a comprehensive exploration of the data based on the ssGSEA, WGCNA, GSEA, VIPER, prostate cancer scRNA-seq data, and the GPSAdb database was conducted. Among the six modules in the clustering results, there are 300 overlapping genes, including 3 previously unreported prostate cancer genes that were validated to be upregulated in prostate cancer through RT-qPCR. Function Module 6 shows a positive correlation with prostate cancer cell stemness, multi-lineage states, and the RTK/RAS signaling pathway. Additionally, the 19 leading-edge genes of the RTK/RAS signaling pathway promote prostate cancer lineage plasticity through a complex network of transcriptional regulation and copy number variations. In the transcriptional regulation network, TP63 and FOXO1 act as suppressors of prostate cancer lineage plasticity, whereas RORC exerts a promoting effect. This study provides a comprehensive perspective on the role of the RTK/RAS pathway in prostate cancer lineage plasticity and offers new clues for the treatment of NEPC.

Autophagy-related lncRNAs in tumor progression and drug resistance: A double-edged sword

The incidence and mortality rates of cancer are increasing every year worldwide but the survival rate of cancer patients is still unsatisfactory. Therefore, it is necessary to further elucidate the molecular mechanisms involved in tumor development and drug resistance to improve cancer cure or survival rates. In recent years, autophagy has become a hot topic in the field of oncology research, which plays a double-edged role in tumorigenesis, progression, and drug resistance. Meanwhile, long non-coding RNA (lncRNA) has also been shown to regulate autophagy, and the two-sided nature of autophagy determines the dual regulatory role of autophagy-related lncRNAs (ARlncRNAs). Therefore, ARlncRNAs can be effective therapeutic targets for various cancers. Furthermore, the high abundance and stability of ARlncRNAs in tumor tissues make them promising biomarkers. In this review, we summarized the roles and mechanisms of ARlncRNAs in tumor cell proliferation, apoptosis, migration, invasion, drug resistance, angiogenesis, radiation resistance, and immune regulation. In addition, we described the clinical significance of these ARlncRNAs, including as biomarkers/therapeutic targets and their association with clinical drugs.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

Autophagy, a critical element in the aging male reproductive disorders and prostate cancer: a therapeutic point of view

Autophagy is a highly conserved, lysosome-dependent biological mechanism involved in the degradation and recycling of cellular components. There is growing evidence that autophagy is related to male reproductive biology, particularly spermatogenic and endocrinologic processes closely associated with male sexual and reproductive health. In recent decades, problems such as decreasing sperm count, erectile dysfunction, and infertility have worsened. In addition, reproductive health is closely related to overall health and comorbidity in aging men. In this review, we will outline the role of autophagy as a new player in aging male reproductive dysfunction and prostate cancer. We first provide an overview of the mechanisms of autophagy and its role in regulating male reproductive cells. We then focus on the link between autophagy and aging-related diseases. This is followed by a discussion of therapeutic strategies targeting autophagy before we end with limitations of current studies and suggestions for future developments in the field.

Co-expression network analysis for the identification of potential prostate cancer genes and in vitro confirmation of their expression in cell model in the presence of Staphylococcal tsst-1 gene

Prostate cancer has arisen as an important life-threatening malignancy in males worldwide. Therefore, it is important to study underlying molecular pathways to be able to proposed appropriate a novel pathway of apoptosis in prostate cancer. This study aimed to explore the molecular effects of Staphylococcal tsst-1 gene on PC3 cell line apoptosis by in silico and in vitro studies. In this work, the differential expression of genes in prostate cancer was predicted by analyzing the public dataset GSE132063. Then, the pcDNA3.1 (+) vector was used to transfer tsst-1 gene to the PC3 cells and its effects was investigated using flow cytometry and qPCR. Co-expression network analysis indicated that lncRNAs had strong relationship with apoptosis genes in prostate cancer. Results of protein-protein docking indicated that BCL2L11, GRAMD3 and EGR1 interacted with tsst-1. Finally, the flow cytometry results showed that transfection by pcDNA3.1 (+)- tsst-1 could increase cellular death rates (48.15%) compared with the pcDNA3.1 (+) groups (6.35%); and the expression levels of GRAMD3, EGR1, BCL2L11 and PLAC4 were dysregulated in tsst-1 -transfected PC3 compared with empty-transfected PC3 (p < .05). In conclusion, our data will provide a novel landscape to understanding the mechanism of Staphylococcal tsst-1 gene on the PC3 cells apoptosis pathways.

A thorough understanding of the role of lncRNA in prostate cancer pathogenesis; Current knowledge and future research directions

In the entire world, prostate cancer (PCa) is one of the most common and deadly cancers. Treatment failure is still common among patients, despite PCa diagnosis and treatment improvements. Inadequate early diagnostic markers and the emergence of resistance to conventional therapeutic approaches, particularly androgen-deprivation therapy, are the causes of this. Long non-coding RNAs (lncRNAs), as an essential group of regulatory molecules, have been reported to be dysregulated through prostate tumorigenesis and hold great promise as diagnostic targets. Besides, lncRNAs regulate the malignant features of PCa cells, such as proliferation, invasion, metastasis, and drug resistance. These multifunctional RNA molecules interact with other molecular effectors like miRNAs and transcription factors to modulate various signaling pathways, including AR signaling. This study aimed to compile new knowledge regarding the role of lncRNA through prostate tumorigenesis in terms of their effects on the various malignant characteristics of PCa cells; in light of these characteristics and the significant potential of lncRNAs as diagnostic and therapeutic targets for PCa. AVAILABILITY OF DATA AND MATERIALS: Not applicable.

Importance of long non-coding RNAs in the pathogenesis, diagnosis, and treatment of prostate cancer

Long non-coding RNAs (lncRNAs) are regulatory transcripts with essential roles in the pathogenesis of almost all types of cancers, including prostate cancer. They can act as either oncogenic lncRNAs or tumor suppressor ones in prostate cancer. Small nucleolar RNA host genes are among the mostly assessed oncogenic lncRNAs in this cancer. PCA3 is an example of oncogenic lncRNAs that has been approved as a diagnostic marker in prostate cancer. A number of well-known oncogenic lncRNAs in other cancers such as DANCR, MALAT1, CCAT1, PVT1, TUG1 and NEAT1 have also been shown to act as oncogenes in prostate cancer. On the other hand, LINC00893, LINC01679, MIR22HG, RP1-59D14.5, MAGI2-AS3, NXTAR, FGF14-AS2 and ADAMTS9-AS1 are among lncRNAs that act as tumor suppressors in prostate cancer. LncRNAs can contribute to the pathogenesis of prostate cancer via modulation of androgen receptor (AR) signaling, ubiquitin-proteasome degradation process of AR or other important signaling pathways. The current review summarizes the role of lncRNAs in the evolution of prostate cancer with an especial focus on their importance in design of novel biomarker panels and therapeutic targets.

Targeting Autophagy Using Long Non-Coding RNAs (LncRNAs): New Landscapes in the Arena of Cancer Therapeutics

Cancer has become a global health hazard accounting for 10 million deaths in the year 2020. Although different treatment approaches have increased patient overall survival, treatment for advanced stages still suffers from poor clinical outcomes. The ever-increasing prevalence of cancer has led to a reanalysis of cellular and molecular events in the hope to identify and develop a cure for this multigenic disease. Autophagy, an evolutionary conserved catabolic process, eliminates protein aggregates and damaged organelles to maintain cellular homeostasis. Accumulating evidence has implicated the deregulation of autophagic pathways to be associated with various hallmarks of cancer. Autophagy exhibits both tumor-promoting and suppressive effects based on the tumor stage and grades. Majorly, it maintains the cancer microenvironment homeostasis by promoting viability and nutrient recycling under hypoxic and nutrient-deprived conditions. Recent investigations have discovered long non-coding RNAs (lncRNAs) as master regulators of autophagic gene expression. lncRNAs, by sequestering autophagy-related microRNAs, have been known to modulate various hallmarks of cancer, such as survival, proliferation, EMT, migration, invasion, angiogenesis, and metastasis. This review delineates the mechanistic role of various lncRNAs involved in modulating autophagy and their related proteins in different cancers.

Aggrephagy-related LncRNAs index: A predictor for HCC prognosis, immunotherapy efficacy, and chemosensitivity

BACKGROUND

Due to the complexity and heterogeneity of hepatocellular carcinoma, the existing clinical staging criterias are insufficient to accurately reflect the tumor microenvironment and predict the prognosis of HCC patients. Aggrephagy, as a type of selective autophagy, is associated with various phenotypes of malignant tumors.

OBJECTIVE

This study aimed to identify and validate a prognostic model based on aggrephagy-related LncRNAs to assess the prognosis and immunotherapeutic response of HCC patients.

METHODS

Based on the TCGA-LIHC cohort, aggrephagy-related LncRNAs were identified. Univariate Cox regression analysis and lasso and multivariate Cox regression were used to construct a risk-scoring system based on eight ARLs. CIBERSORT, ssGSEA, and other algorithms were used to evaluate and present the immune landscape of tumor microenvironment.

RESULTS

The high-risk group had a worse overall survival (OS) than the low-risk group. Patients in the high-risk group are more likely to benefit from immunotherapy because of their high infiltration level and high immune checkpoint expression.

CONCLUSION

The ARLs signature is a powerful predictor of prognosis for HCC patients, and the nomogram based on this model can help clinicians accurately determine the prognosis of HCC patients and screen for specific subgroups of patients who are more sensitive to immunotherapy and chemotherapy.

Construction and verification of a novel circadian clock related long non-coding RNA model and prediction of treatment for survival prognosis in patients with hepatocellular carcinoma

Circadian clock genes are significant in the occurrence and development of HCC and long-non coding RNAs (lncRNAs) are closely related to HCC progression. In this study, we aimed to establish a prognostic risk model for HCC. Circadian clock-related lncRNAs expressed in HCC were extracted from The Cancer Genome Atlas. A nomogram was established to predict individual survival rate. Biological processes enriched for risk model transcripts were investigated by Gene Set Enrichment Analysis. Further, we evaluated the relationship between risk score and immune-checkpoint inhibitor-related gene expression level. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to assess the sensitivity of tumors in high- and low-risk score groups to different drugs. A total of 11 circadian clock-related lncRNAs were included in multi-Cox proportional hazards model analysis to establish a risk model. Univariate and multivariate Cox regression analysis showed that the risk model was an independent risk factor in HCC. The risk model was a significantly associated with the immune signature. Further GDSC analysis indicated that patients in each risk score group may be sensitive to different anti-cancer drugs. QRT-PCR analysis results showed that C012073.1, PRRT3-AS1, TMCC1-AS1, LINC01138, MKLN1-AS, KDM4A-AS1, AL031985.3, POLH-AS1, LINC01224, and AC099850.3 were more highly expressed in Huh-7 and HepG2, compared to LO2, while AC008549.1 were lower expressed. Our work established a prognostic model for HCC. Risk score analysis indicated that the model is significantly associated with modulation tumor immunity and could be used to guide more effective therapeutic strategies in the future.

The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM

INTRODUCTION

Skin cutaneous melanoma (SKCM) is the world's fourth deadliest cancer, and advanced SKCM leads to a poor prognosis. Novel biomarkers for SKCM diagnosis and prognosis are urgently needed. Long non-coding RNAs (lncRNAs) provide various biological functions and have been proved to play a significant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell level. This study explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data.

MATERIALS AND METHODS

The TCGA cohort and melanoma samples in the GEO database (GSE72056, GSE19234, GSE15605, GSE7553, and GSE81383) were included in this study. Marker genes were filtered, and ensemble lncRNAs were annotated. The clinical significance of selected lncRNAs was verified through TCGA and GEO dataset analysis. SiRNA transfection, wound-healing and transwell assays were performed to evaluate the effect of PRRT3-AS1 on cellular function. Immune infiltration of the selected lncRNAs was also exhibited.

RESULTS

A 5-marker-lncRNAs model of significant prognostic value was constructed based on GSE72056 and the TCGA cohort. PRRT3-AS1 combined with DANCR was then found to provide significant prognostic value in SKCM. PRRT3-AS1 was filtered for its higher expression in more advanced melanoma and significant prognosis value. Cellular function experiments in vitro revealed that PRRT3-AS1 may be required for cancer cell migration in SKCM. PRRT3-AS1 was found to be related to epithelial-mesenchymal transition (EMT) signaling pathways. DNA methylation of PRRT3-AS1 was negatively related to PRRT3-AS1 expression and showed significant prognosis value. In addition, PRRT3-AS1 may suppress immune infiltration and be involved in immunotherapy resistance.

CONCLUSION

PRRT3-AS1 may be a diagnostic and prognostic biomarker of SKCM.

Identification of lncRNA biomarkers in hepatocellular carcinoma by comprehensive analysis of the lncRNA-mediated ceRNA network

Growing evidence implicates that miRNAs can interact with long non-coding RNAs (lncRNAs) to regulate target mRNAs through competitive interactions. However, this mechanism that regulate tumorigenesis and cancer progression remains largely unexplored. Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs), which play a significant role in regulating gene expression. The purpose of our study was to determine potential lncRNA biomarkers to predict the prognosis of HCC by comprehensive analysis of a ceRNA network. The edgeR package was used to obtain the differentially expressed RNA datasets by analyzing 370 HCC tissues and 50 adjacent non-HCC tissues from The Cancer Genome Atlas (TCGA). Through investigating the differentially expressed between HCC tissues and adjacent non-HCC tissues, a total of 947 lncRNAs, 52 miRNAs, and 1,650 mRNAs were obtained. The novel constructed ceRNA network incorporated 99 HCC-specific lncRNAs, four miRNAs, and 55 mRNAs. Survival analysis identified 22 differentially expressed mRNAs, four miRNAs, and nine lncRNAs which were associated with overall survival (OS) time in HCC (p &lt; 0.05), and further exploration was performed to assess the correlation of these differentially expressed genes with tumor stage. The Interpretation of the potential functions of these differentially expressed genes in HCC was realized by Gene ontology (GO) and KEGG pathway enrichment analyses. Seven lncRNAs were confirmed based on univariate Cox regression analysis, lasso COX regression analysis and multivariate Cox regression analysis to construct a predictive model in HCC patients which were related to the prognosis of OS. In summary, ceRNAs contributed to explore the mechanism of tumorigenesis and development, and a model with seven lncRNAs might be potential biomarker to predict the prognosis of HCC. These findings supported the need to studies on the mechanisms involved in the regulation of HCC by ceRNAs.

Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma

The present study was aimed at identifying the potential prognostic biomarkers of the immune-related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA-sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were determined with regard to the coexpression of immune-related genes and differentially expressed lncRNAs. The survival IRLs were obtained using the univariate Cox analysis. Subsequently, the prognosis model was constructed via the multivariate Cox analysis. Subsequently, functional annotation was conducted using Gene Set Enrichment Analysis (GSEA) and principal component analysis (PCA). In total, 341 IRLs were identified, and 6 IRLs were found to have a highly significant association with the prognosis of patients with HCC. The immune prognosis model was constructed with these 6 IRLs (AC099850.4, negative regulator of antiviral response, AL031985.3, PRRT3-antisense RNA1, AL365203.2, and long intergenic nonprotein coding RNA 1224) using the multivariate Cox regression analysis. In addition, immune-related prognosis signatures were confirmed as an independent prognostic factor. The association between prognostic signatures and immune infiltration indicated that the 6 lncRNAs could reflect the immune status of the tumor. Collectively, the present study demonstrates that six-lncRNA signatures may be potential biomarkers to predict the prognosis of patients with HCC.

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

BACKGROUND

LncRNAs involve in the autophagy to regulate Prostate cancer (PCa) initiation and progression. Therefore, it urges to explore more significant AR-lncRNAs in PCa.

METHODS

mRNA data and clinical information of PCa were achieved from TCGA database, and ARGs were obtained from the HADb. AR-lncRNAs were identified by correlation analysis of DE ARGs and lncRNAs. Univariate Cox regression, LASSO regression, and multivariate Cox regression were used to identify the prognostic AR-lncRNA signature and constructed a risk model. GESA was used to biological function analysis between high- and low-risk score group. A nomogram was constructed and used to predicate the survival of PCa patients. A calibration curve was used to determines accuracy of the predication model. AR-related ceRNA network was constructed by correlation analysis. Expression of six AR-related lncRNAs were detected by qRT-PCR.

RESULTS

222 ARGs and 385 AR-lncRNAs were screened from PCa and normal tissues, and 17 AR-lncRNAs were identified as prognostic signature for PCa. Based on the expression of prognostic signature, a risk score was calculated, and PCa samples were distributed into high- and low-risk score groups. The biological function and predicated value of the prognostic signature were also examined. Finally, based on the correlation between each ARG and its prognostic signature, three modules of AR-lncRNA-miRNA-mRNA regulatory networks were constructed based on 6 AR-lncRNAs, 17 miRNAs, and 12 ARGs. And we found that AC012085.2, UBXN10-AS1, LINC00261 downregulated, whereas AP004608.1, AC104667.2, AC008610.1 upregulated in PCa compared with BPH tissues.

CONCLUSION

Our finding supplied the potential AR-lncRNAs prognostic signature for PCa.

Long non-coding RNA PCDRlnc1 confers docetaxel resistance in prostate cancer by promoting autophagy

Docetaxel resistance seriously affects its clinical application in prostate cancer (PCa). Long noncoding RNAs (lncRNAs) influence the chemosensitivity of various cancers. However, the potential involvement of lncRNAs in docetaxel sensitivity remains largely unknown in PCa. In the present study, we used RNA sequencing to compare the expression profiles of lncRNAs in docetaxel-resistant PCa cells and their parental cells and identified a novel lncRNA, ENSG00000234147, termed as PCa docetaxel resistance-associated lncRNA1 (PCDRlnc1). Our results indicated that PCDRlnc1 is closely associated with docetaxel resistance in PCa, and PCDRlnc1 knockout markedly sensitized the resistant cells to docetaxel in vitro and in vivo. In addition, PCDRlnc1 inhibition markedly suppressed docetaxel-induced autophagy. Conversely, PCDRlnc1 overexpression promoted autophagy. Mechanistically, PCDRlnc1 interacted with UHRF1 (ubiquitin-like with plant homeodomain and ring finger domains 1) and promoted its transcription level in PCa cells, leading to the activation of autophagic Beclin-1 signaling. Together, our data demonstrate that PCDRlnc1 is a novel key regulator of PCa docetaxel resistance, suggesting that it may be used as a potential biomarker of docetaxel resistance and therapeutic target in PCa.

Skin cutaneous melanoma properties of immune-related lncRNAs identifying potential prognostic biomarkers

Skin cutaneous melanoma (SKCM) is one of the most aggressive and life-threatening cancers with high incidence rate, metastasis rate and mortality. Early detection and stratification of risk assessment are essential to treat SKCM and to improve survival rate. The aim of this study is to construct an immune-related lncRNAs (immlncRNAs) prognosis risk model to identify immune biomarkers for early diagnosis, prognosis assessment and target immunotherapy of SKCM. For this purpose, we identified 46 immlncRNAs significantly correlated with SKCM prognosis to construct the prognostic risk model and patients were stratified into the high- and low-risk subgroups according to the developed model. The predictive efficiency of this model has been proved by K-M survival analysis and receiver operating characteristic curve. Moreover, CIBERSORT algorithms confirmed that there were differences in immune cell infiltration between the high- and low-risk groups. Functional enrichment analysis further indicated that immlncRNAs were related to a variety of immune response process signaling pathways, suggesting that relevant immlncRNAs could play an important role in the immune regulation of SKCM. Finally, subgroup analysis and multiple Cox regression analysis further proved the stability of the model. In summary, we successfully constructed a 46 immlncRNA-related prognostic risk score model with excellent predictive efficacy and provided more possibilities to investigate the immune regulation mechanisms and to develop immunotherapy of SKCM.

Inflammatory Response-Related Long Non-Coding RNA Signature Predicts the Prognosis of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a high mortality malignant tumor with genetic and phenotypic heterogeneity, making predicting prognosis challenging. Meanwhile, the inflammatory response is an indispensable player in the tumorigenesis process and regulates the tumor microenvironment, which can affect the prognosis of tumor patients.

Methods

Using HCC samples in the TCGA-LIHC dataset, we explored lncRNA expression profiles associated with the inflammatory response. The inflammatory response-related lncRNA signature was constructed by univariate Cox regression, LASSO regression, and multivariate Cox regression methods based on inflammatory response-related differentially expressed lncRNAs in HCC.

Results

Seven inflammatory response-related lncRNA signatures were identified in predicting HCC prognosis. Kaplan–Meier (K-M) survival analysis indicated that high-risk group HCC patients were associated with poor prognosis. The utility of the inflammatory response-related lncRNA signatures was proved by the AUC and DCA analysis. The nomogram further confirmed the accuracy of the novel signature in predicting HCC patients' prognoses. In validation, our novel signature is more accurate than traditional clinicopathological performance for prognosis prediction of HCC patients. GSEA analysis further elucidated the underlying mechanisms and pathways of HCC progression in the low- and high-risk groups. Moreover, immune cells infiltration responses and immune function analyses revealed a significant difference between high- and low-risk groups in cytolytic activity, MHC class I, type I INF response, type II INF response, inflammation-promoting, and T cell coinhibition. Finally, HHLA2, NRP1, CD276, TNFRSF9, TNFSF4, CD80, and VTCN1 were expressed higher in high-risk groups in the immune checkpoint analysis.

Conclusions

A novel inflammatory response-related lncRNA signature (AC145207.5, POLHAS1, AL928654.1, MKLN1AS, AL031985.3, PRRT3AS1, and AC023157.2) is capable of predicting the prognosis of HCC patients and providing new immune targeted therapies insight.

Targeting autophagy in prostate cancer: preclinical and clinical evidence for therapeutic response

Prostate cancer is a leading cause of death worldwide and new estimates revealed prostate cancer as the leading cause of death in men in 2021. Therefore, new strategies are pertinent in the treatment of this malignant disease. Macroautophagy/autophagy is a “self-degradation” mechanism capable of facilitating the turnover of long-lived and toxic macromolecules and organelles. Recently, attention has been drawn towards the role of autophagy in cancer and how its modulation provides effective cancer therapy. In the present review, we provide a mechanistic discussion of autophagy in prostate cancer. Autophagy can promote/inhibit proliferation and survival of prostate cancer cells. Besides, metastasis of prostate cancer cells is affected (via induction and inhibition) by autophagy. Autophagy can affect the response of prostate cancer cells to therapy such as chemotherapy and radiotherapy, given the close association between autophagy and apoptosis. Increasing evidence has demonstrated that upstream mediators such as AMPK, non-coding RNAs, KLF5, MTOR and others regulate autophagy in prostate cancer. Anti-tumor compounds, for instance phytochemicals, dually inhibit or induce autophagy in prostate cancer therapy. For improving prostate cancer therapy, nanotherapeutics such as chitosan nanoparticles have been developed. With respect to the context-dependent role of autophagy in prostate cancer, genetic tools such as siRNA and CRISPR-Cas9 can be utilized for targeting autophagic genes. Finally, these findings can be translated into preclinical and clinical studies to improve survival and prognosis of prostate cancer patients.

• Prostate cancer is among the leading causes of death in men where targeting autophagy is of importance in treatment;

• Autophagy governs proliferation and metastasis capacity of prostate cancer cells;

• Autophagy modulation is of interest in improving the therapeutic response of prostate cancer cells;

• Molecular pathways, especially involving non-coding RNAs, regulate autophagy in prostate cancer;

• Autophagy possesses both diagnostic and prognostic roles in prostate cancer, with promises for clinical application.

Molecular and cellular functions of long non-coding RNAs in prostate and breast cancer

Long noncoding RNAs (lncRNAs) are defined as noncoding RNA transcripts with a length greater than 200 nucleotides. Research over the last decade has made great strides in our understanding of lncRNAs, especially in the biology of their role in cancer. In this article, we will briefly discuss the biogenesis and characteristics of lncRNAs, then review their molecular and cellular functions in cancer by using prostate and breast cancer as examples. LncRNAs are abundant, diverse, and evolutionarily, less conserved than protein-coding genes. They are often expressed in a tumor and cell-specific manner. As a key epigenetic factor, lncRNAs can use a wide variety of molecular mechanisms to regulate gene expression at each step of the genetic information flow pathway. LncRNAs display widespread effects on cell behavior, tumor growth, and metastasis. They act intracellularly and extracellularly in an autocrine, paracrine and endocrine fashion. Increased understanding of lncRNA's role in cancer has facilitated the development of novel biomarkers for cancer diagnosis, led to greater understanding of cancer prognosis, enabled better prediction of therapeutic responses, and promoted identification of potential targets for cancer therapy.

Identification of Mutator-Derived lncRNA Signatures of Genomic Instability for Promoting the Clinical Outcome in Hepatocellular Carcinoma

Background

Accumulating evidence proves that long noncoding RNA (lncRNA) plays a crucial role in maintaining genomic instability. However, it is significantly absent from exploring genomic instability-associated lncRNAs and discovering their clinical significance.

Objective

To identify crucial mutator-derived lncRNAs and construct a predictive model for prognosis and genomic instability in hepatocellular carcinoma.

Methods

First, we constructed a mutator hypothesis-derived calculative framework through uniting the lncRNA expression level and somatic mutation number to screen for genomic instability-associated lncRNA in hepatocellular carcinoma. We then selected mutator-derived lncRNA from the genome instability-associated lncRNA by univariate Cox analysis and Lasso regression analysis. Next, we created a prognosis model with the mutator-derived lncRNA signature. Furthermore, we verified the vital role of the model in the prognosis and genomic instability of hepatocellular carcinoma patients. Finally, we examined the potential relationship between the model and the mutation status of TP53.

Results

In this study, we screened 88 genome instability-associated lncRNAs and built a prognosis model with four mutator-derived lncRNAs. Moreover, the model was an independent predictor of prognosis and an accurate indicator of genomic instability in hepatocellular carcinoma. Finally, the model could catch the TP53 mutation status, and the model was a more effective indicator than the mutation status of TP53 for hepatocellular carcinoma patients.

Conclusion

This research adopted a reliable method to analyze the role of lncRNA in genomic instability. Besides, the prognostic model with four mutator-derived lncRNAs is an excellent new indicator of prognosis and genomic instability in hepatocellular carcinoma. In addition, this finding may help clinicians develop therapeutic systems.

CDKN2B-AS1 Promotes Malignancy as a Novel Prognosis-Related Molecular Marker in the Endometrial Cancer Immune Microenvironment

The prognosis of patients with endometrial cancer (EC) is closely associated with immune cell infiltration. Although abnormal long non-coding RNA (lncRNA) expression is also linked to poor prognosis in patients with EC, the function and action mechanism of immune infiltration-related lncRNAs underlying the occurrence and development of EC remains unclear. In this study, we analyzed lncRNA expression using The Cancer Genome Atlas and clinical data and identified six lncRNAs as prognostic markers for EC, all of which are associated with the infiltration of immune cell subtypes, as illustrated by ImmLnc database and ssGSEA analysis. Real-time quantitative polymerase chain reaction showed that CDKN2B-AS1 was significantly overexpressed in EC, whereas its knockdown inhibited the proliferation and invasion of EC cells and the in vivo growth of transplanted tumors in nude mice. Finally, we constructed a competing endogenous RNA regulatory network and conducted Gene Ontology enrichment analysis to elucidate the potential molecular mechanism underlying CDKN2B-AS1 function. Overall, we identified molecular targets associated with immune infiltration and prognosis and provide new insights into the development of molecular therapies and treatment strategies against EC.

PPAR-γ Modulators as Current and Potential Cancer Treatments

Worldwide, cancer has become one of the leading causes of mortality. Peroxisome Proliferator-Activated Receptors (PPARs) is a family of critical sensors of lipids as well as regulators of diverse metabolic pathways. They are also equipped with the capability to promote eNOS activation, regulate immunity and inflammation response. Aside from the established properties, emerging discoveries are also made in PPAR's functions in the cancer field. All considerations are given, there exists great potential in PPAR modulators which may hold in the management of cancers. In particular, PPAR-γ, the most expressed subtype in adipose tissues with two isoforms of different tissue distribution, has been proven to be able to inhibit cell proliferation, induce cell cycle termination and apoptosis of multiple cancer cells, promote intercellular adhesion, and cripple the inflamed state of tumor microenvironment, both on transcriptional and protein level. However, despite the multi-functionalities, the safety of PPAR-γ modulators is still of clinical concern in terms of dosage, drug interactions, cancer types and stages, etc. This review aims to consolidate the functions of PPAR-γ, the current and potential applications of PPAR-γ modulators, and the challenges in applying PPAR-γ modulators to cancer treatment, in both laboratory and clinical settings. We sincerely hope to provide a comprehensive perspective on the prospect of PPAR-γ applicability in the field of cancer treatment.

A Novel Nine-lncRNA Risk Signature Correlates With Immunotherapy in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma is one of the most common malignant tumors with a very high mortality rate. The emergence of immunotherapy has brought hope for the cure of hepatocellular carcinoma. Only a small number of patients respond to immune checkpoint inhibitors, and ferroptosis and tertiary lymphoid structure contribute to the increased response rate of immune checkpoint inhibitors; thus, we first need to identify those who are sensitive to immunotherapy and then develop different methods to improve sensitivity for different groups.

Methods

The sequencing data of hepatocellular carcinoma from The Cancer Genome Atlas and Gene Expression Omnibus was downloaded to identify the immune-related long non-coding RNAs (lncRNAs). LncRNAs related to survival data were screened out, and a risk signature was established using Cox proportional hazard regression model. R software was used to calculate the riskScore of each patient, and the patients were divided into high- and low-risk groups. The prognostic value of riskScore and its application in clinical chemotherapeutic drugs were confirmed. The relationship between riskScore and immune checkpoint genes, ferroptosis genes, and genes related to tertiary lymphoid structure formation was analyzed by Spearman method. TIMER, CIBERSORT, ssGSEA, and ImmuCellAI were used to evaluate the relative number of lymphocytes in tumor. The Wilcoxon signed-rank test confirmed differences in immunophenoscore between the high- and low-risk groups.

Results

Data analysis revealed that our signature could well predict the 1-, 2-, 3-, and 5-year survival rates of hepatocellular carcinoma and to predict susceptible populations with Sorafenib. The risk signature were significantly correlated with immune checkpoint genes, ferroptosis genes, and tertiary lymphoid structure-forming genes, and predicted tumor-infiltrating lymphocyte status. There was a significant difference in IPS scores between the low-risk group and the high-risk group, while the low-risk group had higher scores.

Conclusion

The riskScore obtained from an immune-related lncRNA signature could successfully predict the survival time and reflect the efficacy of immune checkpoint inhibitors. More importantly, it is possible to select different treatments for different hepatocellular carcinoma patients that increase the response rate of immune checkpoint inhibitors and will help improve the individualized treatment of hepatocellular carcinoma.

Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis

Background and objective

Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of prostate cancer (PCa). Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis.

Methods

Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa based on TCGA data. Then we used the data of TCGA and ICGC to verify the performance of our prediction model. The receiver operating characteristic curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, Cox regression analysis was conducted to examine whether the signature’s prediction ability was independent of additional clinicopathological variables. Possible biological functions for those prognostic lncRNAs were predicted on those 4 protein-coding genes (PCGs) related to lncRNAs.

Results

Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane.

Conclusion

Our current study successfully identifies a novel candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.

The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma

Background

lncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs’ clinical significance, and find new targeted treatment strategies.

Methods

The corresponding data of HCC patients and autophagy genes were obtained from the TCGA database, and the human autophagy database respectively. Based on the co-expression and Cox regression analysis to construct prognostic prediction signature.

Results

Finally, a signature containing seven autophagy-related lncRNAs (PRRT3-AS1, RP11-479G22.8, RP11-73M18.8, LINC01138, CTD-2510F5.4, CTC-297N7.9, RP11-324I22.4) was constructed. Based on the risk score of signature, Overall survival (OS) curves show that the OS of high-risk patients is significantly lower than that of low-risk patients (P = 2.292e−10), and the prognostic prediction accuracy of risk score (AUC = 0.786) is significantly higher than that of ALBI (0.532), child_pugh (0.573), AFP (0.5751), and AJCC_stage (0.631). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are obviously accuracy by the combined analysis of the risk score, child_pugh, age, M_stage, and Grade (The AUC of 1- and 3-years are 0.87, and 0.855). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing.

Conclusion

In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04123-6.

Autophagy-Related Long Non-coding RNA Is a Prognostic Indicator for Bladder Cancer

Bladder cancer (BC) is one of the most common malignant urinary system tumors, and its prognosis is poor. In recent years, autophagy has been closely linked to the development of BC. Therefore, we investigated the potential prognostic role of autophagy-related long non-coding RNA (lncRNA) in patients with BC. We obtained the lncRNA information and autophagy genes, respectively, from The Cancer Genome Atlas (TCGA) data set and the human autophagy database (HADb) and performed a co-expression analysis to identify autophagy gene-associated lncRNAs. Then, we divided the data into training group and testing group. In the training group, 15 autophagy-related lncRNAs were found to have a prognostic value (AC026369.3, USP30-as1, AC007991.2, AC104785.1, AC010503.4, AC037198.1, AC010331.1, AF131215.6, AC084357.2, THUMPD3-AS1, U62317.4, MAN1B1-DTt, AC024060.1, AL662844.4, and AC005229.4). The patients were divided into low-risk group and high-risk group based on the prognostic lncRNAs. The overall survival (OS) time for the high-risk group was shorter than that for the low-risk group [risk ratio (hazard ratio, HR) = 1.08, 95% CI: 1.06-1.10; p < 0.0001]. Using our model, the defined risk value can predict the prognosis of a patient. Next, the model was assessed in the TCGA testing group to further validate these results. A total of 203 patients with BC were recruited to verify the lncRNA characteristics. We divided these patients into high-risk group and low-risk group. The results of testing data set show that the survival time of high-risk patients is shorter than that of low-risk patients. In the training group, the area under the curve (AUC) was more than 0.7, indicating a high level of accuracy. The AUC for a risk model was greater than that for each clinical feature alone, indicating that the risk value of a model was the best indicator for predicting the prognosis. Further training data analysis showed that the gene set was significantly enriched in cancer-related pathways, including actin cytoskeleton regulation and gap junctions. In conclusion, our 15 autophagy-related lncRNAs have a prognostic potential for BC, and may play key roles in the biology of BC.

Development and Validation of an Immune-Related lncRNA Signature for Predicting the Prognosis of Hepatocellular Carcinoma

Aim

Immunotherapy is currently being explored as a potential treatment for hepatocellular carcinoma (HCC). This study investigated the prognostic value of immune-related long non-coding RNAs (lncRNAs) in patients with HCC.

Methods

The Wilcoxon test was used to compare differentially expressed lncRNAs between HCC tissue and non-tumor tissue. Moreover, co-expression analysis was used to determine immune-related lncRNA. Univariate cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were used to identify immune-related prognostic lncRNA. The immune risk score was calculated by the sum of the product from each lncRNA expression and its coefficient. Furthermore, the prognostic significance of the lncRNA signature was determined in the training group, testing group, and the entire group. A prognostic nomogram was established by integrating immune risk score and clinicopathological features.

Results

PRRT3-AS1 and AL031985.3 were identified as immune-related prognostic lncRNAs in HCC patients. HCC patients were divided into high and low-risk groups based on the optimal cutoff value of risk score in the training group. The prognosis of HCC patients in the high-risk group was worse compared with the low-risk group. Besides, the immune-related lncRNA score was regarded as an independent risk factor for the prognosis of HCC patients. The predictive nomogram showed satisfactory discrimination and consistency. Gene enrichment analysis results indicated that the high-risk group was associated with immune-related signaling pathways.

Conclusion

This study screened a 2-lncRNA signature and constructed a nomogram to predict the survival of HCC patients, thereby provided guidelines for undertaking medical decisions.

Immune-Related lncRNA Risk Signatures Predict Survival of IDH Wild-Type and MGMT Promoter Unmethylated Glioblastoma

Introduction

Glioblastoma is the most malignant grade of glioma, and it is also the most common primary tumor in the brain. Immunotherapy is a kind of precise tumor treatment. However, there are limited studies about immune-related lncRNA. This study is aimed at analyzing immune-related lncRNAs in glioblastoma and screening out prognostic factors, providing new potential targets for glioblastoma immunology research. Material and Methods. Gene expression data and clinical data of IDH wild-type with MGMT promoter unmethylated glioblastoma were acquired from the TCGA and CGGA databases. Immune-related lncRNAs were identified with the help of data from the InnateDB database. Immune prognostic factors were recognized by Cox regression analysis. GSEA analysis pursued their potential functions.

Results

We found 318 immune-related lncRNAs. Among them, there were 137 immune-related lncRNAs that were upregulated and 181 that were downregulated. 15 prognostic lncRNAs were identified by Cox regression, and a total of 6 molecules were included in the following risk scoring model. GSEA showed that these lncRNAs participated in functions such as protein digestion and absorption and the PPAR signaling pathway.

Conclusion

There are limited studies about immune regulation mechanisms of lncRNA in IDH wild-type with MGMT promoter unmethylated glioblastoma. The identified immune-related lncRNAs in glioblastoma might contribute new targets and research directions for immunological molecular studies of glioblastoma.

Versatile role of curcumin and its derivatives in lung cancer therapy

Lung cancer is a main cause of death all over the world with a high incidence rate. Metastasis into neighboring and distant tissues as well as resistance of cancer cells to chemotherapy demand novel strategies in lung cancer therapy. Curcumin is a naturally occurring nutraceutical compound derived from Curcuma longa (turmeric) that has great pharmacological effects, such as anti-inflammatory, neuroprotective, and antidiabetic. The excellent antitumor activity of curcumin has led to its extensive application in the treatment of various cancers. In the present review, we describe the antitumor activity of curcumin against lung cancer. Curcumin affects different molecular pathways such as vascular endothelial growth factors, nuclear factor-κB (NF-κB), mammalian target of rapamycin, PI3/Akt, microRNAs, and long noncoding RNAs in treatment of lung cancer. Curcumin also can induce autophagy, apoptosis, and cell cycle arrest to reduce the viability and proliferation of lung cancer cells. Notably, curcumin supplementation sensitizes cancer cells to chemotherapy and enhances chemotherapy-mediated apoptosis. Curcumin can elevate the efficacy of radiotherapy in lung cancer therapy by targeting various signaling pathways, such as epidermal growth factor receptor and NF-κB. Curcumin-loaded nanocarriers enhance the bioavailability, cellular uptake, and antitumor activity of curcumin. The aforementioned effects are comprehensively discussed in the current review to further direct studies for applying curcumin in lung cancer therapy.