Aug 2026· International Journal of Molecular Sciences· 0 citations· 65 references
TL;DR
This computational study identifies several candidate lncRNAs associated with clinical outcomes in breast cancer, which should be interpreted as preliminary candidates, which require future validation and functional studies to determine their biological roles and evaluate their potential as prognostic biomarkers.
Abstract
Functional studies have shown that long noncoding RNAs (lncRNAs) play different roles in gene expression regulation, acting as epigenetic factors. Accumulating evidence indicates that lncRNAs modulate diverse biological processes, and their altered expression is associated with the development and progression of cancer, including BC. Here, aiming to identify lncRNAs associated with breast cancer, we performed a re-analysis of the expression data from 1071 tumors of eligible patients in the LACRN-MPBC Study. Using normalized data, we compared the expression profiles of triple-negative breast cancer (TNBC; 170 cases) and a heterogeneous group of non-TNBC tumors (782 cases). A total of 59 differentially expressed lncRNAs (DELncRNAs) could be identified based on the criteria of |log2FC| ≥ 1 and FDR < 0.05, using the DESeq2 R package. In the Kaplan–Meier survival analysis, we identified DELncRNAs that affect disease-free survival and/or overall survival, with eight of them in TNBC and 27 in non-TNBC patients. Multivariate Cox proportional-hazards models were used to evaluate independent predictors of survival. Two DELncRNAs, VLDLR-AS1 and PART1, were identified as independent prognostic markers for TNBC patients, while seven (LINC00239, FAM30A, LINC00842, LINC01315, EGOT, LY6E-DT, and SLC25A21-AS1) were independent prognostic markers for non-TNBC patients. Our computational study identifies several candidate lncRNAs associated with clinical outcomes in breast cancer. However, the DELncRNAs identified here should be interpreted as preliminary candidates, which require future validation and functional studies to determine their biological roles and evaluate their potential as prognostic biomarkers.
This study found that LINC02257 and LINC00659 are upregulated in BC and are associated with malignancy-related genes and patient survival, suggesting their potential as therapeutic targets and prognostic biomarkers.
Fatemeh Badzohre, Azin Azadfekr, Farnoosh Akbari et al.· Current Genetic Medicine Rep...· 0 citations
Abstract Motivation The Motivation: Long non-coding RNAs (lncRNAs) are important regulators of gene expression and are increasingly implicated in cancer. However, the extent to which aberrant DNA methylation is associated with lncRNA dysregulation in lung adenocarcinoma (LUAD) remains underexplored. Results By integrating RNA-seq and Illumina 450K methylation data from 473 LUAD and 32 normal lung samples in TCGA, we identified 2,668 differentially expressed lncRNAs and 20,843 differentially methylated CpG sites. Mapping CpGs to lncRNA promoters (−1.5 kb from the annotated TSS) and Spearman correlation analysis highlighted seven lncRNAs with inverse promoter methylation–expression patterns; FAM83A-AS2 and AC012213.1 showed the most consistent associations and were linked to poorer overall survival in Kaplan–Meier and Cox models (adjusted for age, sex, and tumor stage). A two-lncRNA prognostic score derived using LASSO Cox regression showed moderate time-dependent discrimination at 590 days (AUC = 0.72) within the TCGA cohort. An exploratory lncRNA–miRNA–mRNA network suggested miRNA-mediated regulation of LUAD genes, with enrichment analyses linking targets to chromatin modification and epigenetic regulation. These findings nominate FAM83A-AS2 and AC012213.1 as candidate epigenetically associated prognostic lncRNAs in LUAD, warranting validation in independent cohorts and functional studies.
S. M. Al Sium, Mahafujul Islam Quadery Tonmoy, Dhirendra Nath Barman et al.· Bioinformatics Advances· 0 citations
Anoikis resistance is a critical mechanism driving glioma invasion, recurrence, and treatment failure. While long non-coding RNAs (lncRNAs) play vital regulatory roles in cancer biology, the functions of anoikis-related lncRNAs (ARlncRNAs) in glioma remain largely uncharacterized.
A total of 1719 glioma samples from The Cancer Genome Atlas (TCGA,
n
= 701) and Chinese Glioma Genome Atlas (CGGA,
n
= 1018) were analyzed to identify prognostic anoikis-related lncRNAs via co-expression and differential screening. A risk signature was constructed using LASSO-Cox regression and validated by survival and ROC analyses. Additionally, immune infiltration, molecular subtypes, and drug sensitivity were evaluated to explore therapeutic potential.
The prognostic model incorporated eight ARlncRNAs (LINC00519, AC140481.1, LINC00928, HOXA-AS2, CRNDE, ACAP2-IT1, USP30-AS1, and TMPO-AS1). The risk score was an independent predictor of overall survival and showed high predictive accuracy in both TCGA and CGGA cohorts (AUC > 0.78). High-risk patients displayed activation of focal adhesion, ECM–receptor interaction, and immune-related pathways, with elevated immune/stromal scores and increased immune checkpoint expression. Consensus clustering identified two molecular subtypes with distinct survival outcomes, immune landscapes, and drug sensitivities.
The established eight-ARlncRNA signature serves as a robust prognostic tool, effectively predicting patient survival and mirroring the heterogeneity of the tumor immune landscape. Furthermore, it offers potential guidance for personalizing therapeutic strategies in glioma.
Yuting Ouyang, Rui Han, Anqi Song et al.· Discover Oncology· 0 citations
Hepatocellular carcinoma (HCC) is a lethal malignancy with limited prognostic biomarkers. This study aimed to develop an RNA methylation-related long non-coding RNA (lncRNA) signature to predict survival and guide therapy in HCC. Transcriptomic and clinical data from 374 HCC tissues (TCGA) were analyzed. RNA methylation-related lncRNAs were identified (Pearson correlation, |cor|> 0.4, p < 0.001). A prognostic signature was constructed using univariate Cox and LASSO regression. Patients were stratified into high-/low-risk groups, validated for survival, immune infiltration, and drug response. A robust three-lncRNA signature (SNHG30, AL049840.5, NRAV) was established. High-risk patients showed significantly worse overall survival (p < 0.001) and progression-free survival (p < 0.001) compared to low-risk patients. The risk score emerged as an independent prognostic factor (multivariate Cox, p < 0.001) with strong predictive accuracy (1-/3-/5-year AUCs: 0.717/0.686/0.682). Functional analysis revealed distinct biological pathways between risk groups, with high-risk tumors associated with cell cycle dysregulation and immune suppression (higher TIDE scores), while low-risk tumors exhibited metabolic pathway activation. Drug sensitivity analysis suggested differential responses to chemotherapy and targeted therapies between risk groups. Our study developed and validated a clinically applicable RNA methylation-related lncRNAs signature that effectively predicts HCC prognosis and therapeutic response. This signature provides valuable insights for risk stratification and may guide personalized treatment decisions in HCC management.
Yunchuan Yang, Xiang Ma, Chi Zhou et al.· Egyptian Liver Journal· 0 citations
Cervical cancer, the third most common cancer worldwide with 50% increase in mortality, is a growing public health concern. Recent research on non-coding RNAs including long coding RNAs (lncRNAs) and circular RNAs (circRNAs) highlights their key roles in carcinogenesis across different cancers; but the studies on lncRNA-circRNA-mRNA regulatory networks (tripartite network) are still lacking in cervical cancer. This study integrates transcriptomic data to analyse circRNA-lncRNA-mRNA interactions, focusing on immune signaling pathways. To further understand the biological relevance of these interactions, differentially expressed genes were identified using DESeq2/limma, followed by functional enrichment. A competing endogenous RNA (ceRNA) network was constructed in Cytoscape, with miRNA binding serving as the central connecting factor. The immune-focused subnetwork comprised 7 mRNAs, 14 circRNAs, and 80 lncRNAs. Different machine learning prognostic models based on the ncRNA network and individual RNAs including differentially expressed coding and non-coding RNAs were developed to assess prognostic performance. Among these, the LASSO model based on the circRNA-lncRNA-mRNA network showed the best overall performance, with AUC values of 0.77 in the training set and 0.89 in the test set. Model performance was also assessed using bootstrap resampling for internal validation to ensure robustness. DLEU1, ITPR1, hsa_circRNA_101206, and miR-210 were identified as key prognostic biomarkers. Docking, HPA validation, and immune infiltration analyses confirmed the miR-210:DLEU1:ITPR1 axis as a key immune regulator in cervical cancer. This network modelling approach establishes that combinatorial modelling of coding and non-coding elements outperforms individual approaches, providing a robust framework for prognostic stratification and therapeutic targeting in cervical cancer.
V. Navya, Geethu Muraleedharan, P. Fasna et al.· Biochemical and Biophysical...· 0 citations
Long non-coding RNAs (lncRNAs) and single nucleotide polymorphisms (SNPs) within them play crucial role in cancer susceptibility and disease outcomes. Breast and ovarian cancers, characterized by genetic heterogeneity, present significant challenges for precise diagnosis and treatment. Despite recent advancements in personalized medicine, inclusion of lncRNA-SNP (LSNP) markers into cancer risk detection panels remains limited. In this work, we put forward LSNP regulated gene expression based breast and ovarian cancer risk prediction model LsGCRPred (LSNP-Gene Interaction Based Cancer Risk Prediction Model). Notably, our approach accounts for the tissue-specificity of lncRNAs as well the benefit for individuals with predisposing conditions. Additionally, pathway analysis revealed the involvement of the LSNP interacting genes in key cancer regulating pathways. TaqMan genotyping and qPCR were performed to confirm the presence of selected LSNPs in ovarian and breast cancer cell lines along with the significant expression of the lncRNA and associated gene transcripts. These findings highlight previously overlooked genetic variants within lncRNA loci and their regulatory impact on disease outcomes, providing insights into personalized cancer diagnosis and treatment strategies. The tool LsGCRPred can be accessed as a standalone version on GitHub. Github Link: https://github.com/zglabDIB/LsGCRPred