The novel role and function of LINC01235 in metastasis of gastric cancer cells by inducing epithelial-mesenchymal transition

Prognostic risk model under the immune-associated long chain non-coding ribonucleic acid and its application in survival prognosis assessment of patients with breast cancer

This study aimed to develop a prognostic risk model based on immune-related long non-coding RNAs (lncRNAs). By analyzing the expression profiles of specific long non-coding RNAs, the objective was to construct a predictive model to accurately assess the survival prognosis of breast cancer (BC) patients. This effort seeks to provide personalized treatment strategies for patients and improve clinical outcomes. Based on the median risk value, 300 samples of triple-negative BC (TNBC) patients were rolled into a high-risk group (HR group, n = 140) and a low-risk group (LR group, n = 160). Multivariate Cox (MVC) analysis was performed by combining the patient risk score and clinical information to evaluate the prognostic value of the prognostic risk (PR) model. A total of 371 immune-related lncRNAs associated with the prognosis of TNBC were obtained from 300 TNBC samples. Nine associated with prognosis were obtained by univariate Cox (UVC) analysis, and 3 (AC090181.2, LINC01235, and LINC01943) were selected by MVC analysis for the construction of TNBC PR model. Survival analysis showed a great difference in TNBC patients in different groups (P < 0.001). The receiver operator characteristic (ROC) curve showed the model possessed a good area under ROC curve (AUC), which was 0.928. The patient RS jointing with clinical information as well as the MVC analysis revealed that RS was an independent risk factor (IRF) for prognosis of TNBC (P < 0.05, HR = 1.033286). Therefore, the lncRNAs associated with TNBC immunity can be screened by bioinformatics analysis, and the established PR model of TNBC could better predict the prognosis of patients with TNBC, exhibiting a high application value in clinic.

Identification of CAF-related lncRNAs at the pan-cancer level represents a potential carcinogenic risk

Cancer-associated fibroblasts (CAFs) are increasingly recognized as playing a crucial role in regulating cancer progression and metastasis. These cells can be activated by long non-coding RNAs (lncRNAs), promoting the malignant biological processes of tumor cells. Therefore, it is essential to understand the regulatory relationship between CAFs and lncRNAs in cancers. Here, we identified CAF-related lncRNAs at the pan-cancer level to systematically predict their potential regulatory functions. The identified lncRNAs were also validated using various external data at both tissue and cellular levels. This study has revealed that these CAF-related lncRNAs exhibit expression perturbations in cancers and are highly correlated with the infiltration of stromal cells, particularly fibroblasts and endothelial cells. By prioritizing a list of CAF-related lncRNAs, we can further distinguish patient subtypes that show survival and molecular differences. In addition, we have developed a web server, CAFLnc (https://46906u5t63.zicp.fun/CAFLnc/), to visualize our results. In conclusion, CAF-related lncRNAs hold great potential as a valuable resource for comprehending lncRNA functions and advancing the identification of biomarkers for cancer progression and therapeutic targets in cancer treatment.

Decoding the regulatory landscape of lncRNAs as potential diagnostic and prognostic biomarkers for gastric and colorectal cancers

Colorectal cancer (CRC) and gastric cancer (GC) are major contributors to cancer-related mortality worldwide. Despite advancements in understanding molecular mechanisms and improved drug treatments, the overall survival rate for patients remains unsatisfactory. Metastasis and drug resistance are major challenges contributing to the high mortality rate in both CRC and GC. Recent research has shed light on the role of long noncoding RNAs (lncRNAs) in the development and progression of these cancers. LncRNAs regulate gene expression through various mechanisms, including epigenetic modifications and interactions with microRNAs (miRNAs) and proteins. They can serve as miRNA precursors or pseudogenes, modulating gene expression at transcriptional and post-transcriptional levels. Additionally, circulating lncRNAs have emerged as non-invasive biomarkers for the diagnosis, prognosis, and prediction of drug therapy response in CRC and GC. This review explores the intricate relationship between lncRNAs and CRC/GC, encompassing their roles in cancer development, progression, and chemoresistance. Furthermore, it discusses the potential of lncRNAs as therapeutic targets in these malignancies. The interplay between lncRNAs, miRNAs, and tumor microenvironment is also highlighted, emphasizing their impact on the complexity of cancer biology. Understanding the regulatory landscape and molecular mechanisms governed by lncRNAs in CRC and GC is crucial for the development of effective diagnostic and prognostic biomarkers, as well as novel therapeutic strategies. This review provides a comprehensive overview of the current knowledge and paves the way for further exploration of lncRNAs as key players in the management of CRC and GC.

Detection of cell-type-enriched long noncoding RNAs in atherosclerosis using single-cell techniques: A brief review

Cardiovascular diseases are the leading causes of mortality and morbidity worldwide. Atherosclerotic plaque underlies the predominant factors and is composed of various cell types, including structure cells, such as endothelial and smooth muscle cells, and immune cells, such as macrophages and T cells. Single-cell RNA sequencing (scRNA-seq) has been extensively applied to decipher these cellular heterogeneities to expand our understanding on the mechanisms of atherosclerosis (AS) and to facilitate identifying cell-type-specific long noncoding RNAs (LncRNAs). LncRNAs have been demonstrated to deeply regulate biological activities at the transcriptional and post-transcriptional levels. A group of well-documented functional lncRNAs in AS have been studied. In our review, we selectively described several lncRNAs involved in the critical process of AS. We highlighted four novel lncRNAs (lncRNA CARMN, LINC00607, PCAT19, LINC01235) detected in scRNA-seq datasets and their functions in AS. We also reviewed open web source and bioinformatic tools, as well as the latest methods to perform an in-depth study of lncRNAs. It is fundamental to annotate functional lncRNAs in the various biological activities of AS, as lncRNAs may represent promising targets in the future for treatment and diagnosis in clinical practice.

Exploration of epithelial-mesenchymal transition-related lncRNA signature and drug sensitivity in breast cancer

Background

Breast cancer (BRCA) has become the most diagnosed cancer worldwide for female and seriously endanger female health. The epithelial-mesenchymal transition (EMT) process is associated with metastasis and drug resistance in BRCA patients. However, the prognostic value of EMT-related lncRNA in BRCA still needs to be revealed. The aim of this study is to construct an EMT-related lncRNA (ERL) signature with accuracy predictive ability for the prognosis of BRCA patients.

Methods

RNA-seq expression data and Clinical characteristics obtained from the TCGA (The Cancer Genome Atlas) were used in the study. First, we identified the EMT-related lncRNA by the Pearson correlation analysis. An EMT-related lncRNAs prognostic risk signature was constructed using univariate Cox regression and Lasso-penalized Cox regression analyses. The model's performance was validated using Kaplan-Meier (KM) survival analysis, ROC curve and C-index. Finally, a nomogram was constructed for clinical practice in evaluating the patients with BRCA and validated by calibration curve and decision curve analysis (DCA). We also evaluated the drug sensitivity of signature lncRNA and the tumor immune cell infiltration in breast cancer.

Results

We constructed a 10-lncRNA risk score signature based on the lncRNAs associated with the EMT process. We could assign BRCA patients to the high- and low-risk group according to the median risk score. The prognostic risk signature showed excellent accuracy and demonstrated sufficient independence from other clinical characteristics. The immune cell infiltration analysis showed that the prognostic risk signature was related to the infiltration of the immune cell subtype. Drug sensitivity analysis proved ERLs signature could effectively predict the sensitivity of patients to common chemotherapy drugs in BRCA and provide guidance for chemotherapy drugs for high-risk and low-risk patients.

Conclusion

Our ERL signature and nomogram have excellent prognostic value and could become reliable tools for clinical guidance.

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.

Neutrophil extracellular traps (NETs)-related lncRNAs signature for predicting prognosis and the immune microenvironment in breast cancer

Background: Neutrophil extracellular traps (NETs) are closely associated to tumorigenesis and development. However, the relationship between NETs-related long non-coding RNAs (lncRNAs) and the characteristics of breast tumor remains an enigma. This study aimed to explore the clinical prognostic value of NETs-related lncRNAs, their correlation with the tumor microenvironment (TME) and their predictive ability of drug sensitivity in patients with breast cancer (BC). Methods: The expression profiles of RNA-sequencing and relevant clinical data of BC patients were extracted from TCGA database. The co-expression network analysis, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were employed to construct the NETs-related lncRNAs signature. A nomogram was established and validated to explore the clinical application. Furthermore, the immune microenvironment and drug sensitivity for BC with different prognostic risks were explored. Finally, the expression pattern of lncRNAs was validated using qRT-PCR in BC tissues and their adjacent non-cancerous tissues. Results: Based on NETs-related lncRNAs, a prognostic risk model consisted of 10 lncRNAs (SFTA1P, ACTA2-AS1, AC004816.2, AC000067.1, LINC01235, LINC01010, AL133467.1, AC092919.1, AL591468.1, and MIR200CHG) was established. The Kaplan-Meier analysis showed that the overall survival (OS) was significantly better in low-risk BC patients than in high-risk BC patients (P _{training cohort} < 0.001, P _{validation cohort} = 0.009). The nomogram also showed good predictive accuracy for OS of BC individuals in both training and validation cohorts. The function enrichment analysis revealed that high-risk group was mainly enriched in immune-related functions and pathways, and the tumor mutation burden in this group was markedly higher than that in the low-risk group (p = 0.022). Moreover, significant differences were observed in immune cells, immune functions and immune checkpoint genes among BC patients at different risks (p < 0.05). The response to chemotherapeutic agents and immunotherapy were also closely related with the expression of NETs-related lncRNAs (p < 0.001). The expression of lncRNAs from experimental validation were generally consistent with the bioinformatics analysis results. Conclusion: Our study provided a novel prognostic model for BC and yielded strong scientific rationale for individualized treatment strategies, elucidating immunotherapy in BC patients.

Epigenetic signatures in gastric cancer: current knowledge and future perspectives

INTRODUCTION

Gastric cancer (GC) is the fifth most common malignancy in the world and accounts for 7.7% of all cancer-related deaths. Early diagnosis of GC is critical in terms of prognosis, and aberrations at the molecular level, especially epigenetic alterations, manifest much earlier than histological findings. In recent years, there has been a great deal of research on the epigenomic profile of GC, and epigenetic alterations seem to play a more important role than genetic factors. With the introduction of epigenetic drugs into clinical use in the last decade, the importance of the epigenetic background of GC has increased considerably.

AREAS COVERED

In this review, we summarize the role of methylation changes, histone modifications, and non-coding RNAs in the pathogenesis of GC and how these signatures can be used as diagnostic and therapeutic targets in clinical management.

EXPERT OPINION

Epigenetic alterations take place before most genetic aberrations observed in GC and may have an initiating role in the pathogenesis of GC. They can be used as biomarkers in risk calculation, early diagnosis, and evaluation of prognosis of GC, as well as treatment targets.

A Novel Oxidative Stress-Related lncRNA Signature That Predicts the Prognosis and Tumor Immune Microenvironment of Breast Cancer

Background

The association between oxidative stress and lncRNAs within the cancer-related researching field has been a controversial subject. At present, the exact function of oxidative stress as well as lncRNAs exert in breast cancer (BC) are still unclear. Therefore, the present study examined the lncRNAs oxidative stress-related in BC.

Methods

Transcriptome data of BC obtained from TCGA (The Cancer Genome Atlas) database were used to generate synthetic matrices. Patients with breast cancer were randomly assigned to training, testing, or combined groups. The prognostic signature of oxidative stress was created using the selection operator Cox regression method, and the difference in prognosis between groups was examined using Kaplan-Meier curves, the accuracy of which was calculated using a receiver-operating characteristic-area through the curve (ROC-AUC) analysis with internal validation. Also, the Gene Set Enrichment Analyses (GSEA) was applied for the analysis of the risk groups. To conclude, the half-maximal inhibitory concentration (IC50) of these groups were investigated by immunoassay assay.

Results

A model based on 7 lncRNAs related to oxidative stress was proposed, and the calibration plots and projected prognosis matched well. For prognosis at 5, 3, and 1 year, the area under the ROC curve (AUC) values were 0.777, 0.777, and 0.759. The functions of target genes identified by GSEA appear to be mainly expressed in metabolism, signal transduction, tumorigenesis, and also the progression. The remarkable differences in IC50 and gene expression between risk groups in this study provide a deep insight for further systemic treatment. Higher macrophage scores were acquired in the high-risk group, of which patients showed more response to conventional chemotherapy drugs, such as AKT inhibitor VIII and Lapatinib, as well as immunotherapy strategies including anti-CD80, TNF SF4, CD276, and NRP1.

Conclusion

The prognosis of breast cancer can be independently predicted by the markers, which sheds light on further research of the specific role of lncRNAs which are oxidative stress-related and clinical treatment of breast cancer.

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.

An Immune-Related lncRNA Signature to Predict the Biochemical Recurrence and Immune Landscape in Prostate Cancer

Purpose

This study aims to construct an immune-related signature to provide comprehensive insights into the immune landscape of prostate cancer, which can predict biochemical recurrence (BCR) and clinical treatment.

Methods

Based on The Cancer Genome Atlas (TCGA) dataset, a signature constructed by DEirlncRNAs pairs was determined. The receiver operating characteristic curve analysis, Kaplan-Meier analysis, nomogram, and decision curve analysis were used to analyze it. Then, immunophenoscore (IPS), immune cell infiltration, tumor mutation burden (TMB), and immune function were investigated. Finally, we evaluated the role of the signature in medical treatment.

Results

A signature constructed by 10 valid DEirlncRNAs pairs was identified in the training set and validated well in the testing and entire set. The signature was a reliable and independent prognostic indicator to predict the BCR of prostate cancer, which was better than the clinicopathological characteristics. After dividing the patients into low- and high-risk groups by median value, we found that the high-risk group had shorter BCR-free time and higher TMB levels. Furthermore, the high-risk group was negatively associated with plasma B cells and CD+8 T cells. IPS and immune functions, such as immune checkpoints and human leukocyte antigen, were significantly different between the two groups. Low-risk group was more sensitive to endocrine therapy and immunotherapy, while high-risk group was more inclined to targeted drugs. Both groups had their own sensitive chemotherapy.

Conclusion

We established a novel signature to predict BCR and validated its role in the immune landscape of prostate cancer, which could help patients receive personalized medical treatment.

Non-Coding RNAs in Gastric Cancer: From Malignant Hallmarks to Clinical Applications

Gastric cancer (GC) is one of the most lethal malignancies worldwide. However, the molecular mechanisms underlying gastric carcinogenesis remain largely unknown. Over the past decades, advances in RNA-sequencing techniques have greatly facilitated the identification of various non-coding RNAs (ncRNAs) in cancer cells, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Accumulating evidence has revealed that ncRNAs are essential regulators in GC occurrence and development. However, ncRNAs represent an emerging field of cancer research, and their complex functionality remains to be clarified. Considering the lack of viable biomarkers and therapeutic targets in GC, further studies should focus on elucidating the intricate relationships between ncRNAs and GC, which can be translated into clinical practice. In this review, we summarize recent research progress on how ncRNAs modulate the malignant hallmarks of GC, especially in tumor immune escape, drug resistance, and stemness. We also discuss the promising applications of ncRNAs as diagnostic biomarkers and therapeutic targets in GC, aiming to validate their practical value for clinical treatment.

Identification of Key lncRNA-mRNA Pairs and Functional lncRNAs in Breast Cancer by Integrative Analysis of TCGA Data

Long non-coding RNAs (lncRNAs) play an important role in many diseases and are involved in the post-transcriptional regulatory network of tumors. The purpose of this study is to mine new lncRNA–mRNA regulatory pairs and analyze the new mechanism of lncRNA involvement in breast cancer progression. Using breast cancer miRNA and mRNA expression profiling from The Cancer Genome Atlas (TCGA), we identified 59 differentially expressed lncRNAs, 88 differentially expressed miRNAs, and 1,465 differentially expressed mRNAs between breast cancer tissue and adjacent normal breast cancer. Whereafter, four candidate lncRNAs (FGF14-AS2, LINC01235, AC055854.1, and AC124798.1) were identified by the Kaplan–Meier (K–M) plotter. Furthermore, we screened the hub lncRNA (LINC01235) through univariate Cox analysis, multivariate Cox analysis, and qPCR validation, which was significantly correlated with breast cancer stage, ER status, and pathological N. Subsequently, 107 LINC01235-related mRNAs were obtained by combining differentially expressed miRNAs, differentially expressed mRNAs, and LINC01235 targeting miRNAs and mRNAs. The protein–protein interaction (PPI) network was established by Cytoscape software, and 53 key genes were screened. Function and pathway enrichment showed that LINC01235-related key genes might be involved in the process of cell differentiation, cell proliferation, and p53 signal pathway. In addition, LINC01235 has been confirmed to regulate the proliferation, migration, and invasion of MCF-7 cells in in vitro experiments. Furthermore, we screened three mRNAs (ESR1, ADRA2A, and DTL) associated with breast cancer drug resistance from key genes. Through RNA interference experiments in vitro and correlation analysis, we found that there was a negative feedback mechanism between LINC01235 and ESR1/ADRA2A. In conclusion, our results suggest that LINC01235-ESR1 and LINC01235-ADRA2A could serve as important co-expression pairs in the progression of breast cancer, and LINC01235 plays a key role as an independent prognostic factor in patients with breast cancer. The findings of this work greatly increase our understanding of the molecular regulatory mechanisms of lncRNA in breast cancer.

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.

Identification of prognostic long non-coding RNA signature with potential drugs in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the primary malignancy in the liver with high rate of death and recurrence. Novel prognostic model would be crucial for early diagnosis and improved clinical decision. The study aims to provide an effective lncRNA-based signature to predict survival time and tumor recurrence for HCC. Based on public database, lncRNA-based classifiers for overall survival and tumor recurrence were built with regression analysis and cross validation strategy. According to the risk-score of the classifiers, the whole cohorts were divided into groups with high and low risk. Afterwards, the efficiency of the lncRNA-based classifiers was evaluated and compared with other clinical factors. Finally, candidate small molecules for high risk groups were further screened using drug response databases to explore potential drugs for HCC treatment.