Autophagy-related long non-coding RNA signature for ovarian cancer

Exosomal lncRNA USP30-AS1 activates the Wnt/β-catenin signaling pathway to promote cervical cancer progression via stabilization of β-catenin by USP30

Cervical cancer (CC) remains a major cause of cancer-related mortality among women globally. Long noncoding RNAs (lncRNAs) play crucial regulatory roles in various cancers, including CC. This study investigates the function of a novel lncRNA, USP30 antisense RNA 1 (USP30-AS1), in CC tumorigenesis. We analyzed USP30-AS1 expression using RT-qPCR and conducted in vitro loss-of-function assays, as well as in vivo assays, to evaluate the effects of USP30-AS1 silencing on CC cell growth and migration. Additional mechanistic experiments, including RNA pull-down, RNA immunoprecipitation (RIP), and co-immunoprecipitation (Co-IP) assays, were performed to elucidate the regulatory mechanisms influenced by USP30-AS1. We discovered that USP30-AS1 is overexpressed in CC tissues and cells. Silencing USP30-AS1 significantly reduced cell proliferation, migration, invasion, and tumor growth. Moreover, USP30-AS1 was found to modulate the expression of ubiquitin-specific peptidase 30 (USP30) by sponging microRNA-2467-3p (miR-2467-3p) and recruiting the FUS RNA binding protein (FUS), thereby stabilizing β-catenin and activating the Wnt/β-catenin signaling pathway. These findings suggest that USP30-AS1 enhances CC cell growth and migration through the miR-2467-3p/FUS/USP30 axis, highlighting its potential as a biomarker for CC.

Dysregulation of different modes of programmed cell death by epigenetic modifications and their role in cancer

Modifications of epigenetic factors affect our lives and can give important information regarding one's state of health. In cancer, epigenetic modifications play a crucial role, as they influence various programmed cell death types. The purpose of this review is to investigate how epigenetic modifications, such as DNA methylation, histone modifications, and non-coding RNAs, influence various cell death processes in suppressing or promoting cancer development. Autophagy and apoptosis are the most investigated programmed cell death modes, as based on the tumor stage these cell death types can either promote or prevent cancer evolution. Therefore, our discussion focuses on how epigenetic modifications affect autophagy and apoptosis, as well as their diagnostic and therapeutical potential in combination with available chemotherapeutics. Additionally, we summarize the available data regarding the role of epigenetic modifications on other programmed cell death modes, such as ferroptosis, necroptosis, and parthanatos in cancer and discuss current advancements.

Prognostic significance of LINC01132 in lung cancer and its regulatory role in tumor progression

BACKGROUND

The application of long non-coding RNAs (lncRNAs) in cancer has been the focus of research in recent years. This study aimed to discuss the expression and functional mechanism of lncRNA LINC01132 (LINC01132) in lung cancer and explore its prognostic significance in tumors.

METHODS

The expression of LINC01132 in lung cancer patients was verified using GSE98929 screening and real-time quantitative polymerase chain reaction (RT-qPCR) detection. The prognostic potential of LINC01132 was evaluated by performing the chi-square analysis of clinical indicators, Kaplan-Meier analysis, and Cox proportional hazard model. Cell Counting Kit-8 (CCK-8), flow cytometry, and Transwell assay were used to characterize the biological functions of the lung cancer cells. The targeting relationship between LINC01132 and microRNA-125a-3p (miR-125a-3p), miR-125a-3p and SMAD2 was predicted by bioinformatics and verified by luciferase activity assay.

RESULTS

LINC01132 was upregulated in lung cancer tissues and cells, which was an independent risk factor for survival and prognostic outcomes of lung cancer patients. Silencing LINC01132 suppressed the proliferation and migration of lung cancer cells and accelerated cell death. The target of LINC01132 was miR-125a-3p, and miR-125a-3p inhibitor could eliminate the inhibitory effect of LINC01132 knockdown on the cells. Additionally, SMAD2 is a downstream target of miR-125a-3p, and knockdown of SMAD2 reversed the effects of miR-125a-3p inhibitor on cell migration and invasion.

CONCLUSION

LINC01132 may regulate the progression of lung cancer by targeting the miR-125a-3p /SMAD2 axis and serve as a prognostic biomarker for lung cancer.

Fatty acid metabolism-related lncRNAs are potential biomarkers for survival prediction in clear cell renal cell carcinoma

Metabolic reprogramming of energy is a newly recognized characteristic of cancer. In our current investigation, we examined the possible predictive importance of long noncoding RNAs (lncRNAs) associated to fatty acid metabolism in clear cell renal cell carcinoma (ccRCC). We conducted an analysis of the gene expression data obtained from patients diagnosed with ccRCC using the Cancer Genome Atlas (TCGA) database and the ArrayExpress database. We performed a screening to identify lncRNAs that are differentially expressed in fatty acid metabolism. Based on these findings, we developed a prognostic risk score model using these fatty acid metabolism-related lncRNAs. We then validated this model using Cox regression analysis, Kaplan-Meier survival analysis, and principal-component analysis (PCA). Furthermore, the prognostic risk score model was successfully validated using both the TCGA cohort and the E-MTAB-1980 cohort. We utilized gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to determine the correlation between fatty acid metabolism and the PPAR signaling pathway in patients with ccRCC at various clinical stages and prognoses. We have discovered compelling evidence of the interaction between immune cells in the tumor microenvironment and tumor cells, which leads to immune evasion and resistance to drugs. This was achieved by the utilization of advanced techniques such as the CIBERSORT method, ESTIMATE R package, ssGSEA algorithm, and TIMER database exploration. Ultimately, we have established a network of competing endogenous RNA (ceRNA) that is related to fatty acid metabolism. The findings of our study suggest that medicines focused on fatty acid metabolism could be clinically significant for individuals with ccRCC. The utilization of this risk model, which is centered around the lncRNAs associated with fatty acid metabolism, could potentially provide valuable prognostic information and hold immunotherapeutic implications for patients with ccRCC.

LncRNA LBX2-AS1 promotes proliferation and migratory capacity of clear cell renal cell carcinoma through mitophagy

BACKGROUND

Long non-coding RNAs (lncRNAs) have been extensively investigated in the field of cancer, among which, lncRNA ladybird homeobox 2-antisense RNA 1 (LBX2-AS1) has been demonstrated to exert carcinogenic effects on a variety of malignancies. However, the biological functions of LBX2-AS1 in clear cell renal cell carcinoma (ccRCC) have not been explicitly elucidated.

METHODS

Arraystar lncRNA chip and qRT-PCR verify the expression of LncRNA LBX2-AS1 in ccRCC. CCK-8 assay and cell cloning assay were used to assess the proliferative capacity of ccRCC cells. Migration abilities were quantified by scratch assay and transwell assay. Potential molecular signaling pathways were determined by high-throughput whole transcriptomics analysis. WB analysis was performed to validate the relationship between LBX2-AS1 and key molecules of mitophagy pathway. The effect of LBX2-AS1 on mitophagy was observed by laser confocal microscopy. Rescue experiments further validated the role of downstream gene FOXO3A in the LBX2-AS1 signaling pathway. Finally, the authentic effect of LBX2-AS1 was verified in vivo.

RESULTS

LncRNA LBX2-AS1 was over expressed in ccRCC tissues and could enhance the proliferation and migration of ccRCC cells. Autophagic pathway was identified as a possible mechanism involved in the oncogenic effect of LBX2-AS1. Mitophagy levels were observed in LBX2-AS1 low-expressing cells through laser confocal microscopy. Knockdown of LBX2-AS1 significantly elevated mitophagy levels as observed using laser confocal microscopy and led to FOXOA3 decreasing in and BNIP3L and LC3 enrichment. Meanwhile, LBX2-AS1 knocking down dampened the proliferation of ccRCC cells in vivo.

Comprehensive analyses of mitophagy-related genes and mitophagy-related lncRNAs for patients with ovarian cancer

BACKGROUND

Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC.

METHODS

The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on.

RESULTS

We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (P < 0.05) with independent value (P < 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (P = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs.

CONCLUSIONS

Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.

Trans-ancestry epigenome-wide association meta-analysis of DNA methylation with lifetime cannabis use

Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9436 participants (7795 European and 1641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 ( p < 5.85 × 10 - 7 ) : cg22572071 near gene ADGRF1, cg15280358 in ADAM12, cg00813162 in ACTN1, and cg01101459 near LINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated to APOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes.

The role and function of autophagy through signaling and pathogenetic pathways and lncRNAs in ovarian cancer

Lysosomal-driven autophagy is a tightly controlled cellular catabolic process that breaks down and recycles broken or superfluous cell parts. It is involved in several illnesses, including cancer, and is essential in preserving cellular homeostasis. Autophagy prevents DNA mutation and cancer development by actively eliminating pro-oxidative mitochondria and protein aggregates from healthy cells. Oncosuppressor and oncogene gene mutations cause dysregulation of autophagy. Increased autophagy may offer cancer cells a pro-survival advantage when oxygen and nutrients are scarce and resistance to chemotherapy and radiation. This finding justifies the use of autophagy inhibitors in addition to anti-neoplastic treatments. Excessive autophagy levels can potentially kill cells. The diagnosis and treatment of ovarian cancer present many difficulties due to its complexity and heterogeneity. Understanding the role of autophagy, a cellular process involved in the breakdown and recycling of cellular components, in ovarian cancer has garnered increasing attention in recent years. Of particular note is the increasing amount of data indicating a close relationship between autophagy and ovarian cancer. Autophagy either promotes or restricts tumor growth in ovarian cancer. Dysregulation of autophagy signaling pathways in ovarian cancers can affect the development, metastasis, and response to tumor treatment. The precise mechanism underlying autophagy concerning ovarian cancer remains unclear, as does the role autophagy plays in ovarian carcinoma. In this review, we tried to encapsulate and evaluate current findings in investigating autophagy in ovarian cancer.

New algorithms based on autophagy-related lncRNAs pairs to predict the prognosis of skin cutaneous melanoma patients

Skin cutaneous melanoma (SKCM) is the most malignant skin tumor for it is enormously easy to develop invasion and metastasis. Autophagy is a process by which cellular material is degraded by lysosomes or vacuoles and recycled. Autophagy-related long non-coding RNAs (lncRNAs) have been thought to correlate with SKCM. This study aims to explore the prognostic significance of autophagy-related lncRNAs and establish a prognostic model of autophagy-related lncRNA pairs in SKCM. Firstly, the RNA-seq data and related clinical information were downloaded from the TCGA database. 446 qualified samples were enrolled. 222 autophagy-related genes were obtained from the HADb database. Pearson correlation analysis was conducted to identify autophagy-related lncRNAs (ARLs). After that, we obtained prognosis-related ARLs and autophagy-related lncRNA pairs (ARLPs). Using Lasso-Cox regression analysis, an autophagy-related lncRNA-pair prognostic signature was established. The accuracy of the signature were confirmed through a series of validations in terms of mutation profiles, immunity infiltration, and cellular pathways. And we used the random forest method to find USP30-AS1 as a key mediating factor in SKCM.

Transcriptome analysis identification of A-to-I RNA editing in granulosa cells associated with PCOS

Background

Polycystic ovary syndrome (PCOS) is a complex, multifactor disorder in women of reproductive age worldwide. Although RNA editing may contribute to a variety of diseases, its role in PCOS remains unclear.

Methods

A discovery RNA-Seq dataset was obtained from the NCBI Gene Expression Omnibus database of granulosa cells from women with PCOS and women without PCOS (controls). A validation RNA-Seq dataset downloaded from the European Nucleotide Archive Databank was used to validate differential editing. Transcriptome-wide investigation was conducted to analyze adenosine-to-inosine (A-to-I) RNA editing in PCOS and control samples.

Results

A total of 17,395 high-confidence A-to-I RNA editing sites were identified in 3,644 genes in all GC samples. As for differential RNA editing, there were 545 differential RNA editing (DRE) sites in 259 genes with Nucleoporin 43 (NUP43), Retinoblastoma Binding Protein 4 (RBBP4), and leckstrin homology-like domain family A member 1 (PHLDA) showing the most significant three 3'-untranslated region (3'UTR) editing. Furthermore, we identified 20 DRE sites that demonstrated a significant correlation between editing levels and gene expression levels. Notably, MIR193b-365a Host Gene (MIR193BHG) and Hook Microtubule Tethering Protein 3 (HOOK3) exhibited significant differential expression between PCOS and controls. Functional enrichment analysis showed that these 259 differentially edited genes were mainly related to apoptosis and necroptosis pathways. RNA binding protein (RBP) analysis revealed that RNA Binding Motif Protein 45 (RBM45) was predicted as the most frequent RBP binding with RNA editing sites. Additionally, we observed a correlation between editing levels of differential editing sites and the expression level of the RNA editing enzyme Adenosine Deaminase RNA Specific B1 (ADARB1). Moreover, the existence of 55 common differentially edited genes and nine differential editing sites were confirmed in the validation dataset.

Conclusion

Our current study highlighted the potential role of RNA editing in the pathophysiology of PCOS as an epigenetic process. These findings could provide valuable insights into the development of more targeted and effective treatment options for PCOS.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes

Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1, using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.

lncRNA GAS5 suppression of the malignant phenotype of ovarian cancer via the miR-23a-WT1 axis

Background

Growth arrest-specific 5 (GAS5) is a long noncoding RNA (lncRNA) that regulates cell viability. GAS5 lncRNA has been shown to decrease colorectal and breast cancer carcinogenesis. Although the function and mechanisms related to lncRNA GAS5 in the development of ovarian cancer (OC) remains unclear. The goal of this study was to clarify the essential functions of lncRNA GAS5 in regulating OC progression and its underlying mechanism.

Methods

Relative levels of lncRNA GAS5 in OC and normal ovarian tissues were identified by quantitative real-time polymerase chain reaction (qRT-PCR). The regulatory effects of lncRNA GAS5 on the proliferation and apoptosis of SK-OV-3 cells were evaluated. Moreover, bioinformatics tools were used to predict a novel target [microRNA (miRNA)] of lncRNA GAS5. To explore the key functions of the lncRNA GAS5/miRNA-23a/WT1 regulatory loop in mediating OC progression, we performed functional experiments and a dual-luciferase reporter (DLR) gene assessment.

Results

Downregulation of lncRNA GAS5 was found in tissues of OC, which was positively correlated with a poor prognosis. In addition, SK-OV-3 cells with a lower expression of lncRNA GAS5 and accelerated cancer cell migration demonstrated a lower percentage of apoptosis in in vitro experiments. It was demonstrated that lncRNA GAS5 acts as a molecular sponge for miR-23a in OC cells. Additionally, WT1 was detected as a miR-23a target gene in OC cells, and through sponging miR-23a, lncRNA GAS5 positively regulated WT1 expression. Rescue tests demonstrated that enhancing the outputs of the miR-23a-WT1 axis reversed the impacts of lncRNA GAS5 silencing on cell proliferation and apoptosis in OC.

Conclusions

The lncRNA GAS5/miR-23a/WT1 cascade was found participate in the progression of OC. lncRNA GAS5 also decreases OC progression by upregulating WT1 and attenuating miR-23a, suggesting that it could be an advantageous therapeutic target for OC intervention.

The role of LncRNA LBX2-AS1 in cancers: functions, mechanisms and potential clinical utility

Increasingly advanced biology technique has revealed that long non-coding RNAs (lncRNA) as critical factors that exert significant regulatory effects on biological functions by modulating gene transcription, epigenetic modifications and protein translation. A newly emerging lncRNA, ladybird homeobox 2 (LBX2)-antisense RNA 1 (LBX2-AS1), was found to be highly expressed in various tumors. Moreover, it is functionally linked to the regulation of essential tumor-related biological processes, such as cell proliferation and apoptosis, through interactions with multiple signaling molecules/pathways. The important roles played by LBX2-AS1 in cancer initiation and progression suggest that this lncRNA has enormous clinical potential for use as a novel biomarker or therapeutic target. In this article, we retrospectively review the latest advances in research exploring the roles of the lncRNA LBX2-AS1 in oncology field, highlighting its involvement in a comprehensive network of molecular mechanisms underlying diverse cancers and examining its potential applications in clinical practice.

A prognostic risk prediction model based on ferroptosis-related long non-coding RNAs in bladder cancer: A bulk RNA-seq research and scRNA-seq validation

BACKGROUND

To construct a prognostic risk model of bladder cancer (BC) from the perspective of long non-coding RNAs (lncRNAs) and ferroptosis, in order to guide clinical prognosis and identify potential therapeutic targets.

METHODS

In-hours BC samples were collected from 4 patients diagnosed with BC, who underwent radical cystectomy. Single cell transcriptome sequencing was performed and Seurat package were used for quality control and secondary analysis. LncRNAs expression profiles of BC samples were extracted from The Cancer Genome Atlas database. And sex, age, tumor, node, metastasis stage and other clinical data was downloaded at the same time. Ferroptosis-related lncRNAs were identified by co-expression analysis. We constructed a risk model by Cox regression and least absolute shrinkage and selection operator regression analyses. The predictive strength of the risk model for overall survival (OS) of patients with BC was evaluated by the log-rank test and Kaplan-Meier method. Finally, the enrichment analysis was performed and visualized.

RESULTS

We identified and included 15 prognostic ferroptosis-related lncRNAs (AL356740.1, FOXC2AS1, ZNF528AS1, LINC02535, PSMB8AS1, AL590428.1, AP000347.2, OCIAD1-AS1, AP001347.1, AC104986.2, AC018926.2, LINC00867, AC099518.4, USP30-AS1, and ARHGAP5-AS1), to build our ferroptosis-related lncRNAs risk model. Using this risk model, BC patients were divided into high and low-risk groups, and their respective survival lengths were calculated. The results showed that the OS of the low-risk group was significantly longer than that of the high-risk group. A nomogram was utilized to predict the survival rate of BC patients. As indicated in the nomogram, risk score was the most important indicator of OS in patients with BC. The ferroptosis-related lncRNAs risk model is an independent tool for prognostic risk assessment in patients with BC. Single cell transcriptome sequencing suggests that ferroptosis-related lncRNAs express specifically in BC tumor microenvironment. AL356740.1, LINC02535 and LINC00867 were mainly expressed in tumor cells.

CONCLUSION

The risk model based on the ferroptosis-related lncRNAs and the genomic clinico-pathological nomogram could be used to accurately predict the prognosis of patients with BC. The lncRNAs used to build this model might become potential therapeutic targets in the future.

Comprehensive analysis of cuproptosis-related lncRNAs in the prognosis and therapy response of patients with bladder cancer

Background

Cuproptosis is the recently defined regulatory cell death (RCD) that plays essential roles in tumorigenesis and progression. Long noncoding RNAs (lncRNAs) regulate the gene expression through various means. However, the clinical value of cuproptosis-related lncRNAs in bladder cancer (BLCA) remains poorly described.

Methods

We downloaded the transcriptome sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and lasso Cox regression analyses were performed to construct the prognostic risk signature, the predictive accuracy of which was validated in the subsequent independence and stratification analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the underlying molecular mechanisms involved in the signature to explore therapeutic vulnerabilities and potential targets in BLCA. Tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) were used to estimate the response to immune checkpoint inhibitors (ICIs). We further explored the potential new drug-target candidates based on the half maximal inhibitory concentration for this patient population.

Results

Fifteen cuproptosis-related lncRNAs significantly associated with survival were identified to construct the risk signature based on the normalized expression level and regression coefficient of each gene. The patients with BLCA and high-risk scores defined by the signature were associated with worse survival outcomes. The differentially expressed genes (DEGs) between the 2 risk groups had different biological activity. Furthermore, the patients in the low-risk group exhibited a higher TMB index and a lower TIDE score. The sensitivity of multiple antitumor drugs was negatively related to risk score, including AR-42, AS605240, FK866, TAK-715, and tubastatin A, while the sensitivity of some antitumor drugs, such as AMG-706, BX-795, and RO-3306, were positively correlated with risk score.

Conclusions

Our study established and verified a novel clinical risk signature with cuproptosis-related lncRNAs that may predict therapy response and prognosis with robust and stable accuracy in patients with BLCA and enhance the personalized management of this patient population.

Construction and validation of a histone acetylation-related lncRNA prognosis signature for ovarian cancer

Ovarian cancer (OC) leads to the most deaths among gynecological malignancies. The various epigenetic regulatory mechanisms of histone acetylation in cancer have attracted increasing attention from scientists. Long non-coding RNA (lncRNA) also plays an important role in multiple biology processes linked to OC. This study aimed to identify the histone acetylation-related lncRNAs (HARlncRNAs) with respect to the prognosis in OC. We obtained the transcriptome data from Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA); HARlncRNAs were first identified by co-expression and differential expression analyses, and then univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to construct the HARlncRNAs risk signature. Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analysis (GSEA) in risk groups were conducted to explore the tightly correlated pathways with the risk group. A risk signature with 14 HARlncRNAs in OC was finally established and further validated in the International Cancer Genome Consortium (ICGC) cohort; the 1-, 3-, and 5-year ROC value, nomogram, and calibration results confirmed the good prediction power of this model. The patients were grouped into high- and low-risk subgroups according to the risk score by the median value. The low-risk group patients exhibited a higher homologous recombination deficiency (HRD) score, LOH_frac_altered, and mutLoad_nonsilent. Furthermore, consensus clustering analysis was employed to divide OC patients into three clusters based on the expression of the 14 HARlncRNAs, which presented different survival probabilities. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were also performed to evaluate the three clusters. In conclusion, the risk signature composed of 14 HARlncRNAs might function as biomarkers and prognostic indicators with respect to predicting the response to the anti-cancer drugs in OC.

Construction of a prognostic model of colon cancer patients based on metabolism-related lncRNAs

Background

Many studies have shown that metabolism-related lncRNAs may play an important role in the pathogenesis of colon cancer. In this study, a prognostic model for colon cancer patients was constructed based on metabolism-related lncRNAs.

Methods

Both transcriptome data and clinical data of colon cancer patients were downloaded from the TCGA database, and metabolism-related genes were downloaded from the GSEA database. Through differential expression analysis and Pearson correlation analysis, long non-coding RNAs (lncRNAs) related to colon cancer metabolism were obtained. CRC patients were divided into training set and verification set at the ratio of 2:1. Based on the training set, univariate Cox regression analysis was utilized to determine the prognostic differential expression of metabolic-related lncRNAs. The Optimal lncRNAs were obtain by Lasso regression analysis, and a risk model was built to predict the prognosis of CRC patients. Meanwhile, patients were divided into high-risk and low-risk groups and a survival curve was drawn accordingly to determine whether the survival rate differs between the two groups. At the same time, subgroup analysis evaluated the predictive performance of the model. We combined clinical indicators with independent prognostic significance and risk scores to construct a nomogram. C index and the calibration curve, DCA clinical decision curve and ROC curve were obtained as well. The above results were all verified using the validation set. Finally, based on the CIBERSORT analysis method, the correlation between lncRNAs and 22 tumor-infiltrated lymphocytes was explored.

Results

By difference analysis, 2491 differential lncRNAs were obtained, of which 226 were metabolic-related lncRNAs. Based on Cox regression analysis and Lasso results, a multi-factor prognostic risk prediction model with 13 lncRNAs was constructed. Survival curve results suggested that patients with high scores and have a poorer prognosis than patients with low scores (P<0.05). The area under the ROC curve (AUC) for the 3-year survival and 5-year survival were 0.768 and 0.735, respectively. Cox regression analysis showed that age, distant metastasis and risk scores can be used as independent prognostic factors. Then, a nomogram including age, distant metastasis and risk scores was built. The C index was 0.743, and the ROC curve was drawn to obtain the AUC of the 3-year survival and the 5-year survival, which were 0.802 and 0.832, respectively. The above results indicated that the nomogram has a good predictive effect. Enrichment analysis of KEGG pathway revealed that differential lncRNAs may be related to chemokines, amino acid and sugar metabolism, NOD-like receptor and Toll-like receptor activation as well as other pathways. Finally, the analysis results based on the CIBERSORT algorithm showed that the lncRNAs used to construct the model had a strong polarized correlation with B cells, CD8+T cells and M0 macrophages.

Conclusion

13 metabolic-related lncRNAs affecting the prognosis of CRC were screened by bioinformatics methods, and a prognostic risk model was constructed, laying a solid foundation for the research of metabolic-related lncRNAs in CRC.

Enabling factor for cancer hallmark acquisition: Small nucleolar RNA host gene 17

The role of long non-coding RNA (lncRNA) in human tumors has gradually received increasing attention in recent years. Particularly, the different functions of lncRNAs in different subcellular localizations have been widely investigated. The upregulation of lncRNA small nucleolar RNA host gene 17 (SNHG17) has been observed in various human tumors. Growing evidence has proved that SNHG17 plays a tumor-promoting role in tumorigenesis and development. This paper describes the molecular mechanisms by which SNHG17 contributes to tumor formation and development. The different functions of SNHG17 in various subcellular localizations are also emphasized: its function in the cytoplasm as a competing endogenous RNA (ceRNA), its action in the nucleus as a transcriptional coactivator, and its function through the polycomb repressive complex 2 (PRC2)-dependent epigenetic modifications that regulate transcriptional processes. Finally, the correlation between SNHG17 and human tumors is summarized. Its potential as a novel prognostic and diagnostic biomarker for cancer is explored especially.

The m6A-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumor Immune Infiltration in Ovarian Cancer

Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

An Immune-Related lncRNA Pairing Model for Predicting the Prognosis and Immune-Infiltrating Cell Condition in Human Ovarian Cancer

Ovarian cancer is the second common cancer among the gynecological tumors. It is difficult to be found and diagnosed in the early stage and easy to relapse due to chemoresistance and deficiency in choices of treatment. Therefore, future exploring the biomarkers for diagnosis, treatment, and prognosis prediction of ovarian cancer is significant to women in the world. We downloaded data from TCGA and GTEx and used R “limma” package for analyzing the differentially expressed immune-related lncRNA in ovarian cancer and finally got 7 downregulated and 171 upregulated lncRNA. Then, we paired the differentially expressed immune-related lncRNA and constructed a novel lncRNA pairing model containing 7 lncRNA pairs. Based on the cut-off point with the highest AUC value, 102 patients were selected in high-risk group and 272 in low-risk group. The KM analysis suggested that the patients in the low-risk group had a longer overall survival. Future analysis showed the correlations between risk scores and clinicopathological parameters and infiltrating immune cells. In conclusion, we identified an immune-related lncRNA pairing model for predicting the prognosis and immune-infiltrating cell condition in human ovarian cancer, which thus further can instruct immunotherapy.

Effects of Autophagy-Related Genes on the Prognosis and Immune Microenvironment of Ovarian Cancer

Ovarian cancer (OC) is among the most malignant tumors of the female reproductive system. The role of autophagy in cancer is complex, and the functional relationship between autophagy-related genes and OC remains unclear. Here, the prognostic value of autophagy-related genes in OC and relationships between autophagy and immune function were evaluated. OC data from The Cancer Genome Atlas and the Human Autophagy Database were obtained to identify autophagy-related genes. Univariate and multivariate Cox analyses were used to construct a prognostic model based on autophagy-related genes. Relationships between risk scores and clinical traits were evaluated. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Cytoscape were used to analyze gene functions and their effects on the immune microenvironment. Relationships between autophagy genes and long noncoding RNAs (lncRNAs) were evaluated by Pearson's correlation coefficients, and lncRNAs corresponding to the autophagy-related genes associated with OC prognosis were used to construct a model. Relationships between risk scores and survival and prognosis were evaluated. Finally, a gene set enrichment analysis was performed. Seven autophagy-related genes (CAPN1, CDKN1B, DNAJB1, GNAI3, MTMR14, RHEB, and SIRT2) were identified as independent predictors of prognosis. Three lncRNAs corresponding to autophagy genes independently influenced prognosis. Autophagy genes are closely related to immunity. Fifteen immune cell types showed different levels of infiltration between the high- and low-risk groups. Moreover, immune cell infiltration differed between the high- and low-risk groups based on the model. Our analysis of genes and lncRNAs related to prognosis clarifies the role of autophagy in OC and provides a theoretical basis for further research.

Necroptosis-associated long noncoding RNAs can predict prognosis and differentiate between cold and hot tumors in ovarian cancer

Objective

The mortality rate of ovarian cancer (OC) is the highest among all gynecologic cancers. To predict the prognosis and the efficacy of immunotherapy, we identified new biomarkers.

Methods

The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx) databases were used to extract ovarian cancer transcriptomes. By performing the co-expression analysis, we identified necroptosis-associated long noncoding RNAs (lncRNAs). We used the least absolute shrinkage and selection operator (LASSO) to build the risk model. The qRT-PCR assay was conducted to confirm the differential expression of lncRNAs in the ovarian cancer cell line SK-OV-3. Gene Set Enrichment Analysis, Kaplan-Meier analysis, and the nomogram were used to determine the lncRNAs model. Additionally, the risk model was estimated to evaluate the efficacy of immunotherapy and chemotherapy. We classified necroptosis-associated IncRNAs into two clusters to distinguish between cold and hot tumors.

Results

The model was constructed using six necroptosis-associated lncRNAs. The calibration plots from the model showed good consistency with the prognostic predictions. The overall survival of one, three, and five-year areas under the ROC curve (AUC) was 0.691, 0.678, and 0.691, respectively. There were significant differences in the IC50 between the risk groups, which could serve as a guide to systemic treatment. The results of the qRT-PCR assay showed that AL928654.1, AL133371.2, AC007991.4, and LINC00996 were significantly higher in the SK-OV-3 cell line than in the Iose-80 cell line (P < 0.05). The clusters could be applied to differentiate between cold and hot tumors more accurately and assist in accurate mediation. Cluster 2 was more vulnerable to immunotherapies and was identified as the hot tumor.

Conclusion

Necroptosis-associated lncRNAs are reliable predictors of prognosis and can provide a treatment strategy by screening for hot tumors.

A Necroptosis-Related lncRNA to Develop a Signature to Predict the Outcome, Immune Landscape, and Chemotherapeutic Responses in Bladder Urothelial Carcinoma

Objective

Many studies have drawn their attention to the immunotherapy of bladder urothelial carcinoma in terms of immunologic mechanisms of human body. These include immunogenicity of the tumor cells and involvement of long non-coding RNA (lncRNA). We constructed a necroptosis-related long noncoding RNA (nrlncRNA) risk factor model to predict BLCA outcomes and calculate correlations with chemosensitivity and immune infiltration.

Methods

Transcriptomic data from BLCA specimens were accessed from The Cancer Genome Atlas, and nrlncRNAs were identified by performing co-expression analysis. Univariate analysis was performed to identify differentially expressed nrlncRNA pairs. We constructed least absolute contraction and selector operation regression models and drew receiver operating characteristic curves for 1-, 3-, and 5-year survival rates. Akaike information criterion (AIC) values for survival over 1 year were determined as cutoff values in high- and low-risk subgroups. We reassessed the differences between subgroups in terms of survival, clinicopathological characteristics, chemotherapy efficacy, tumor-infiltrating immune cells, and markers of immunosuppression.

Results

We identified a total of 260 necroptosis-related lncRNA pairs, of which we incorporated 13 into the prognostic model. Areas under the curve of 1-, 3-, and 5- year survival time were 0.763, 0.836, and 0.842, respectively. We confirmed the excellent predictive performance of the risk model. Based on AIC values, we confirmed that the high-risk group was susceptible to unfavorable outcomes. The risk scores correlated with survival were age, clinical stage, grade, and tumor node metastases. The risk model was an independent predictor and demonstrated higher predictive power. The risk model can also be utilized to determine immune cell infiltration status, expression levels of immune checkpoint genes, and the sensitivity to cisplatin, doxorubicin, and methotrexate.

Conclusion

We constructed a novel necroptosis-related signature that predicts BLCA outcomes and performs satisfactorily in the immune landscape and chemotherapeutic responses.

The Biological and Molecular Function of LINC00665 in Human Cancers

Long non-coding RNAs (lncRNAs) are more than 200 nucleotides in length and are implicated in the development of human cancers, without protein-coding function. Mounting evidence indicates that cancer initiation and progression are triggered by lncRNA dysregulation. Recently, a growing number of studies have found that LINC00665, a long intergenic non-protein coding RNA, may be associated with various cancers, including gastrointestinal tumors, gynecological tumors, and respiratory neoplasms. LINC00665 was reported to be significantly dysregulated in cancers and has an important clinical association. It participates in cell proliferation, migration, invasion, and apoptosis through different biological pathways. In this review, we summarize the current findings on LINC00665, including its biological roles and molecular mechanisms in various cancers. LINC00665 may be a potential prognostic biomarker and novel therapeutic target for cancers.

Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma

Background. Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods. The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results. Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions. Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.

LINC00665: An Emerging Biomarker for Cancer Diagnostics and Therapeutics

Long intergenic noncoding RNA 00665 (LINC00665) is located on human chromosome 19q13.12. LINC00665 was upregulated in eighteen cancers and downregulated in two cancers. LINC00665 not only inhibits 25 miRNAs but also directly affects the stability of ten protein-coding genes. Notably, LINC00665 also encodes a micro-peptide CIP2A-BP that promotes triple-negative breast cancer progression. LINC00665 can participate in five signaling pathways to regulate cancer progression, including the Wnt/β-catenin signaling pathway, TGF-β signaling pathway, NF-κB signaling pathway, PI3K/AKT signaling pathway, and MAPK signaling pathway. Aberrant expression of LINC00665 in breast cancer, gastric cancer, and hepatocellular carcinoma can be used for disease diagnosis. In addition, aberrant expression of LINC00665 is closely associated with clinicopathological features and poor prognosis of various cancers. LINC00665 is closely associated with the effects of anticancer drugs, including gefitinib and cisplatin in non-small cell lung cancer, gemcitabine in cholangiocarcinoma, and cisplatin-paclitaxel in breast cancer. This work systematically summarizes the diagnostic and prognostic values of LINC00665 in various tumors, and comprehensively analyzes the molecular regulatory mechanism related to LINC00665, which is expected to provide clear guidance for future research.

Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs

Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

Background

Pyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction.

Methods

Based on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan-Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and BRCA1/2 mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and in-vitro assays on alternative lncRNAs.

Results

A total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the immune response and BRCA1 mutations were identified in different groups distinguished by the risk signature (p < 0.05). Interestingly, in-vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote OC cell proliferation.

Conclusion

The PRL risk signature could independently predict overall survival and guide treatment in patients with OC.

The Emerging Roles of LINC00665 in Human Cancers

Long non-coding RNAs (lncRNAs) are non-coding RNAs that have more than 200 nucleotides and can participate in the regulation of gene expression in various ways. An increasing number of studies have shown that the dysregulated expression of lncRNAs is related to the occurrence and progression of human cancers. LINC00665 is a novel lncRNA, which is abnormally expressed in various human cancers, such as lung cancer, breast cancer, prostate cancer, and glioma. LINC00665 functions in many biological processes of tumor cells, such as cell proliferation, migration, invasion, angiogenesis, and metabolism, and is related to the clinicopathological characteristics of cancer patients. LINC00665 can play biological functions as a ceRNA, directly binding and interacting with proteins, and as an upstream molecule regulating multiple signaling pathways. In this review, we comprehensively summarize the expression level, function, and molecular mechanisms of LINC00665 in different human cancers and emphasize that LINC00665 is a promising new diagnostic, prognostic biomarker, and therapeutic target.

lncRNA USP30-AS1 sponges miR-765 and modulates the progression of colon cancer

Background

The incidence and mortality of colon cancer is increasing recently. It is necessary to identify effective biomarkers for the progression and prognosis of colon cancer. To assess the potential of lncRNA USP30-AS1 (USP30-AS1) in serving as the biomarker of colon cancer and unearth the underlying mechanism.

Methods

There were 123 colon cancer patients enrolled. The expression of USP30-AS1 was evaluated with PCR in tissue and cell samples. The clinical significance of USP30-AS1 was assessed with a series of statistical methods, while the CCK8 and Transwell assay were conducted to estimate its biological effect on the colon cancer cellular processes. In mechanism, the interaction of USP30-AS1 with miR-765 was evaluated with the dual-luciferase reporter assay.

Results

In colon cancer tissues, the USP30-AS1 downregulation and the miR-765 upregulation were observed, and there was a negative correlation between the USP30-AS1 expression level and the miR-765 expression level. The downregulation of USP30-AS1 related to the malignant progression and served as an adverse prognostic indicator of colon cancer. The overexpression of USP30-AS1 dramatically suppressed colon cancer cellular processes, which was alleviated by miR-765.

Conclusions

USP30-AS1 predicts the malignancy and prognosis of colon cancer patients. USP30-AS1 suppressed the progression of colon cancer through modulating miR-765.

Long noncoding RNA SNHG17: a novel molecule in human cancers

Many studies in recent years have found that dysregulation of long non-coding RNAs (lncRNAs) can contribute to disease. Small nucleolar RNA host gene 17 (SNHG17) is a novel cancer-related lncRNA of the SNHG family which is highly expressed in various tumors and may exert oncogenic functions. Several studies have demonstrated that SNHG17 is closely related to the proliferation, migration, invasion, apoptosis, and chemical drug resistance of tumor cells, and clinical studies have found an association between high SNHG17 expression and poor prognosis. In this review, we summarize relevant studies investigating SNHG17, focusing on its biological function as well as its potential value for clinical applications.

Prognostication of Pancreatic Cancer Using The Cancer Genome Atlas Based Ferroptosis-Related Long Non-Coding RNAs

Background: Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical.

Methods: In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed.

Results: Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selection operator and the multivariate Cox analysis were used to assess the prognostic value of these ferroptosis-related lncRNAs. A prognostic risk model, of six lncRNAs, including LINC01705, AC068620.2, TRAF3IP2-AS1, AC092171.2, AC099850.3, and MIR193BHG was constructed. The Kaplan Meier (KM) and time-related receiver operating characteristic (ROC) curve analysis were performed to calculate overall survival and compare high- and low-risk groups. There was also a significant difference in survival time between the high-risk and low-risk groups for the testing cohort and the entire cohort, with AUCs of .723, .753, respectively. Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for pancreatic adenocarcinoma through univariate and multivariate Cox regression. Moreover, a nomogram showed good prediction.

Conclusion: The signature of six FRLs had significant prognostic value for pancreatic adenocarcinoma. They may be a promising therapeutic target in clinical practice.

Bioinformatic Analyses of the Ferroptosis-Related lncRNAs Signature for Ovarian Cancer

Both ferroptosis and lncRNAs are significant for ovarian cancer (OC). Whereas, the study of ferroptosis-related lncRNAs (FRLs) still few in ovarian cancer. We first constructed an FRL-signature for patients with OC in the study. A total of 548 FRLs were identified for univariate Cox regression analysis, and 21 FRLs with significant prognosis were identified. The prognostic characteristics of nine FRLs was constructed and validated, showing opposite prognosis in two subgroups based on risk scores. The multivariate Cox regression analysis and nomogram further verified the prognostic value of the risk model. By calculating ferroptosis score through ssGSEA, we found that patients with higher risk scores exhibited higher ferroptosis scores, and high ferroptosis score was a risk factor. There were 40 microenvironment cells with significant differences in the two groups, and the difference of Stromal score between the two groups was statistically significant. Six immune checkpoint genes were expressed at different levels in the two groups. In addition, five m6A regulators (FMR1, HNRNPC, METTL16, METTL3, and METTL5) were higher expressed in the low-risk group. GSEA revealed that the risk model was associated with tumor-related pathways and immune-associated pathway. We compared the sensitivity of chemotherapy drugs between the two risk groups. We also explored the co-expression, ceRNA relation, cis and trans interaction of ferroptosis-related genes and lncRNAs, providing a new idea for the regulatory mechanisms of FRLs. Moreover, the nine FRLs were selected for detecting their expression levels in OC cells and tissues.

A ferroptosis-related lncRNA signature predicts prognosis in ovarian cancer patients

Background

Ovarian cancer (OC) is a common gynecological malignant tumor with poor prognosis. Ferroptosis is an iron-dependent modality of regulated cell death. The purpose of this study was to determine the prognostic ability of ferroptosis-related long non-coding RNAs (lncRNAs) in OC patients and construct a ferroptosis-related lncRNA prognostic model.

Methods

The Cancer Genome Atlas (TCGA) and FerrDb databases were used to collect RNA sequencing data of OC patients and ferroptosis-related genes, respectively. OC patients were randomly assigned to the training or testing set. Pearson correlation analysis was used to identify ferroptosis-related lncRNAs. Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate regression analyses were performed in the training set to develop a predictive model. The model was validated in the testing set and entire set. Survival analysis, receiver operating characteristic curves, independent prognostic factor analysis, and correlation analysis with clinical features were performed to evaluate the predictive value of the model. A nomogram was established to predict the survivability of OC patients over 1, 3, and 5 years. The distribution of distinct groups was investigated using principal component analysis, and the underlying the biological functions were explored using gene set enrichment analysis.

Results

Eleven ferroptosis-related lncRNAs were determined to establish the prognostic model. Patients in the high-risk group had poor prognosis compared with the low-risk group in the training, testing and entire sets. The area under the receiver operating characteristic curve corresponding to 1-, 3-, and 5-year survival were 0.731, 0.796, and 0.805 in the training set; 0.704, 0.597, and 0.655 in the testing set; and 0.715, 0.691, and 0.736, in the entire set, respectively. The risk score correlated with age and grade. The risk score was also an independent prognostic factor in OC. A nomogram with high C-index (0.68) was constructed. An intuitive observation of the principal component analysis revealed a distinction between high- and low-risk groups, and gene set enrichment analysis indicated that cancer-related pathways were enriched in the high-risk group.

Conclusions

The signature composed of 11 ferroptosis-related lncRNAs accurately predicted the prognosis of OC patients.

Using Immune-Related lncRNA Signature for Prognosis and Response to Immunotherapy in Cutaneous Melanoma

Background

Cutaneous melanoma is a highly malignant skin tumor, and most patients have a poor prognosis. In recent years, immunotherapy has assumed an important role in the treatment of advanced cutaneous melanoma, but only a small percentage of patients benefit from immunotherapy. A growing number of studies have demonstrated that the prognosis of patients with cutaneous melanoma is closely related to long non-coding RNA and the tumor immune microenvironment.

Methods

We downloaded RNA expression data and immune-related gene lists of cutaneous melanoma patients separately from The Cancer Genome Atlas database and ImmPort website and identified immune-related lncRNAs by co-expression analysis. The prognostic model was constructed by applying least absolute shrinkage and selection operator regression, and all patients were classified into high- and low-risk groups according to the risk score of the model. We evaluated the differences between the two groups in terms of survival outcomes, immune infiltration, pathway enrichment, chemotherapeutic drug sensitivity and immune checkpoint gene expression to verify the impact of lncRNA signature on clinical prognosis and immunotherapy efficacy.

Results

By correlation analysis and LASSO regression analysis, we constructed an immune-related lncRNA prognostic model based on five lncRNA: HLA-DQB1-AS1, MIR205HG, RP11-643G5.6, USP30-AS1 and RP11-415F23.4. Based on this model, we plotted Kaplan-Meier survival curves and time-dependent ROC curves and analyzed its ability as an independent prognostic factor for cutaneous melanoma in combination with clinicopathological features. The results showed that these lncRNA signature was an independent prognostic factor of cutaneous melanoma with favorable prognostic ability. Our results also show a higher degree of immune infiltration, higher expression of immune checkpoint-associated genes, and better outcome of immunotherapy in the low-risk group of the lncRNA signature.

Conclusion

The 5 immune-related lncRNA signatures constructed in our study can predict the prognosis of cutaneous melanoma and contribute to the selection of immunotherapy.

Development and Verification of an Autophagy-Related lncRNA Signature to Predict Clinical Outcomes and Therapeutic Responses in Ovarian Cancer

Objective: Long noncoding RNAs (lncRNAs) are key regulators during ovarian cancer initiation and progression and are involved in mediating autophagy. In this study, we aimed to develop a prognostic autophagy-related lncRNA signature for ovarian cancer.

Methods: Autophagy-related abnormally expressed lncRNAs were screened in ovarian cancer with the criteria values of |correlation coefficient| > 0.4 and p < 0.001. Based on them, a prognostic lncRNA signature was established. The Kaplan–Meier overall survival analysis was conducted in high- and low-risk samples in the training, verification, and entire sets, followed by receiver operating characteristics (ROCs) of 7-year survival. Multivariate Cox regression analysis was used for assessing the predictive independency of this signature after adjusting other clinical features. The associations between the risk scores and immune cell infiltration, PD-L1 expression, and sensitivity of chemotherapy drugs were assessed in ovarian cancer.

Results: A total of 66 autophagy-related abnormally expressed lncRNAs were identified in ovarian cancer. An autophagy-related lncRNA signature was constructed for ovarian cancer. High-risk scores were indicative of poorer prognosis compared with the low-risk scores in the training, verification, and entire sets. ROCs of 7-year survival confirmed the well-predictive efficacy of this model. Following multivariate Cox regression analysis, this model was an independent prognostic factor. There were distinct differences in infiltrations of immune cells, PD-L1 expression, and sensitivity of chemotherapy drugs between high- and low-risk samples.

Conclusions: This study constructed an autophagy-related lncRNA signature that was capable of predicting clinical outcomes and also therapeutic responses for ovarian cancer.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

A Potential Immune-Related Long Non-coding RNA Prognostic Signature for Ovarian Cancer

Ovarian cancer (OC), the most lethal gynecologic malignancy, ranks fifth in cancer deaths among women, largely because of late diagnosis. Recent studies suggest that the expression levels of immune-related long non-coding RNAs (lncRNAs) play a significant role in the prognosis of OC; however, the potential of immune-related lncRNAs as prognostic factors in OC remains unexplored. In this study, we aimed to identify a potential immune-related lncRNA prognostic signature for OC patients. We used RNA sequencing and clinical data from The Cancer Genome Atlas and the Gene Expression Omnibus database to identify immune-related lncRNAs that could serve as useful biomarkers for OC diagnosis and prognosis. Univariate Cox regression analysis was used to identify the immune-related lncRNAs with prognostic value. Functional annotation of the data was performed through the GenCLiP310 website. Seven differentially expressed lncRNAs (AC007406.4, AC008750.1, AL022341.2, AL133351.1, FAM74A7, LINC02229, and HOXB-AS2) were found to be independent prognostic factors for OC patients. The Kaplan-Meier curve indicated that patients in the high-risk group had a poorer survival outcome than those in the low-risk group. The receiver operating characteristic curve revealed that the predictive potential of the immune-related lncRNA signature for OC was robust. The prognostic signature of the seven lncRNAs was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the signature of the seven lncRNAs was an independent prognostic factor for OC patients. Finally, we constructed a nomogram model and a competing endogenous RNA network related to the lncRNA prognostic signature. In conclusion, our study reveals novel immune-related lncRNAs that may serve as independent prognostic factors in OC.

The Long Noncoding RNA LINC00665 Facilitates c-Myc Transcriptional Activity via the miR-195-5p MYCBP Axis to Promote Progression of Lung Adenocarcinoma

Long noncoding RNAs (lncRNAs) have recently received growing substantial attention in cancer research due to their important roles in various cancer types. However, the underlying mechanisms and functions of lncRNAs, especially in lung adenocarcinoma (LUAD), remain elusive. Based on pan-cancer screening analyses, we identified that the noncoding RNA LINC00665 was up-regulated in lung adenocarcinoma, which was subsequently confirmed in clinical samples and cell lines. Higher expression of LINC00665 was positively associated with poor prognosis and advanced T stage. Next, using gain- and loss- of function approaches, we revealed that LINC00665 promotes cell proliferation, cell migration, invasion, and suppresses cell apoptosis in LUAD through in vitro and in vivo experiments. Additionally, our findings showed that LINC00665 was predominately localized in the cytoplasm so as to interact with Ago2 protein, which could function as miRNA sponges. The results of bioinformatics prediction and RNA pull-down assay indicated that LINC00665 directly interacted with miR-195-5p. This was also confirmed by fluorescence colocalization. Furthermore, luciferase reporter assay demonstrated that Myc binding protein (MYCBP, also called AMY-1), which enhanced c-Myc transcriptional activity, was the target gene of LINC00665 dependent on miR-195-5p. Finally, rescue functional assay results uncovered that the oncogenic capability of LINC00665 was dependent on miR-195-5p and c-Myc transcriptional activity. In summary, this work elucidates that LINC00665 accelerates LUAD progression via the miR-195-5p/MYCBP axis by acting as a competing endogenous RNA (ceRNA), suggesting that LINC00665 may represent a potential therapeutic target for clinical intervention of LUAD.

A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

Background

Some studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.

Method

A Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.

Results

A four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.

Conclusion

The four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

Unraveling Immune-Related lncRNAs in Breast Cancer Molecular Subtypes

Breast cancer (BRCA) is the most leading cause of cancer worldwide. It is a heterogeneous disease with at least five molecular subtypes including luminal A, luminal B, basal-like, HER2-enriched, and normal-like. These five molecular subtypes are usually stratified according to their mRNA profile patterns; however, ncRNAs are increasingly being used for this purpose. Among the ncRNAs class, the long non-coding RNAs (lncRNAs) are molecules with more than 200 nucleotides with versatile regulatory roles; and high tissue-specific expression profiles. The heterogeneity of BRCA can also be reflected regarding tumor microenvironment immune cells composition, which can directly impact a patient's prognosis and therapy response. Using BRCA immunogenomics data from a previous study, we propose here a bioinformatics approach to include lncRNAs complexity in BRCA molecular and immune subtype. RNA-seq data from The Cancer Genome Atlas (TCGA) BRCA cohort was analyzed, and signal-to-noise ratio metrics were applied to create these subtype-specific signatures. Five immune-related signatures were generated with approximately ten specific lncRNAs, which were then functionally analyzed using GSEA enrichment and survival analysis. We highlighted here some lncRNAs in each subtype. LINC01871 is related to immune response activation and favorable overall survival in basal-like samples; EBLN3P is related to immune response suppression and progression in luminal B, MEG3, XXYLT1-AS2, and LINC02613 were related with immune response activation in luminal A, HER2-enriched and normal-like subtypes, respectively. In this way, we emphasize the need to know better the role of lncRNAs as regulators of immune response to provide new perspectives regarding diagnosis, prognosis and therapeutical targets in BRCA molecular subtypes.