lncRNAs In Cancer Research Articles

LINC01836 Promotes Colorectal Cancer Progression and Functions as ceRNA to Target SLC17A9 by Sponging miR-1226-3p.

Increasing evidence proves that long non-coding RNAs (lncRNAs) play a key role in the occurrence and development of colorectal cancer. However, the function and molecular mechanism of LINC01836 in CRC are still unknown. The differentially expressed lncRNAs in colorectal cancer were obtained from the RNA sequencing data. The effects of LINC01836 on colorectal cancer cells were tested in in vitro experiments. The mechanism of LINC01836 action was investigated through western blot, RNA immunoprecipitation assay and luciferase reporter assay. Moreover, the xenograft mouse model was conducted to examine the effects of LINC01836 in vivo. In this study, we showed that LINC01836 was significantly elevated in colorectal cancer tissues and cells. Elevated LINC01836 expression significantly correlated with larger tumor size, positive lymph node metastasis, distant metastasis, advanced tumor-node-metastasis (TNM) stage, and poor prognosis. Furthermore, decreased expression of LINC01836 repressed proliferation, migration, and invasion in vitro and vivo, and high LINC01836 expression displayed the opposite effect. Further analysis revealed that LINC01836 could regulate the expression of SLC17A9 by competing with miR---1226-3p. Furthermore, down-regulation of LINC01836 or increased expression of miR-1226-3p markedly reversed the effects of SLC17A9 overexpression on colorectal cancer cells. This study showed that LINC01836 regulated the expression of SLC17A9 through sponge miR-1226-3p by acting as a competitive endogenous RNA (ceRNA), promoted the progression of colorectal cancer, and suggested a new prognostic biomarker and potential cancer treatment target for colorectal cancer.

MIR143HG promotes methylation of transcription factor HOXB7 promoter by recruiting methyltransferase DNMT1 to prevent the progression of colon cancer.

In recent years, accumulating evidence has demonstrated the role of long noncoding RNAs (lncRNAs) in colon cancer. We aim to investigate the role of MIR143HG, also known as CARMN (Cardiac mesoderm enhancer-associated noncoding RNA) in colon cancer and explore the related mechanisms. An RNAseq data analysis was performed to screen differentially expressed lncRNAs associated with colon cancer. Next, MIR143HG expression was quantified in colon cancer cells. Moreover, the contributory roles of MIR143HG in the progression of colon cancer with the involvement of DNMT1 and HOXB7 (Homeobox B7) were evaluated after restored MIR143HG or depleted HOXB7. Finally, the effects of MIR143HG were investigated invivo by measuring tumor formation in nude mice. High-throughput transcriptome sequencing was employed to validate the specific mechanisms by which MIR143HG and HOXB7 affect tumor growth invivo. MIR143HG was found to be poorly expressed, while HOXB7 was highly expressed in colon cancer. MIR143HG could promote HOXB7 methylation by recruiting DNMT1 to reduce HOXB7 expression. Upregulation of MIR143HG or downregulation of HOXB7 inhibited cell proliferation, invasion and migration and facilitated apoptosis in colon cancer cells so as to delay the progression of colon cancer. The same trend was identified invivo. Our study provides evidence that restoration of MIR143HG suppressed the progression of colon cancer via downregulation of HOXB7 through DNMT1-mediated HOXB7 promoter methylation. Thus, MIR143HG may be a potential candidate for the treatment of colon cancer.

Association of MIR4435-2HG Expression with TP53 Mutation, Estrogen Receptor Activity, and Poor Prognosis in Breast Cancer

Background: The roles of many long noncoding RNAs (lncRNAs) in breast cancer (BC) are unclear, and the medications that affect them have attracted less attention. Objectives: This study aimed to identify lncRNAs that have the highest association with poor prognosis in BC. Moreover, the relationship between the expression of identified lncRNA and conventional chemotherapy agents was investigated. Methods: The cancer genome atlas data were used to identify lncRNA with the most significant impact on BC survival. The relation of identified lncRNA levels with different subtypes of BC and mutation was assessed. PharmacoGX data and the GEO database were used to evaluate the association of identified lncRNA levels with drug sensitivity and find appropriate medications to reduce its expression, respectively. To assess the effect of the identified drug on the level of lncRNA expression, MCF7, and MDA-MB-468 cell lines were used, and the expression level of target genes was assessed by quantitative reverse transcription polymerase chain reaction. Results: The expression level of lncRNA MIR4435-2HG was most associated with the poor survival of BC patients, and its expression level can be related to TP53 mutation and estrogen and progesterone receptors. Increased expression of MIR4435-2HG was observed in cancer samples, and MIR4435-2HG level was correlated with metastasis pathway genes. Drug sensitivity to epirubicin, irinotecan, and lestaurtinib correlated with MIR4435-2HG levels. Furthermore, GEO data showed that the expression level of MIR4435-2HG could decrease under estrogen receptor (ER) activity. Finally, in vitro, results showed that MIR4435-2HG expression declined in response to estradiol as an ER-activating ligand in the MCF7 cell line. Conclusions: The expression of MIR4435-2HG can be a powerful biomarker for poor prognosis in BC. The data also suggested that this lncRNA may play an oncogenic role in metastatic pathways, and ER activity could regulate its expression.

Construction and validation of a prognostic signature based on seven endoplasmic reticulum stress-related lncRNAs for patients with head and neck squamous cell carcinoma

Endoplasmic reticulum stress (ERS) occurs when misfolded or unfolded proteins accumulate in the endoplasmic reticulum (ER), and it is often observed in tumors, including head and neck squamous cell carcinoma (HNSCC). Relevant studies have demonstrated the prognostic significance of ERS-related long non-coding RNAs (lncRNAs) in various cancers. However, the relationship between ERS and lncRNAs in HNSCC has received limited attention in previous studies. In this study, we aimed to develop an ERS-related lncRNAs prognostic model using correlation analysis, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis based on data from The Cancer Genome Atlas (TCGA) database. The survival and predictive ability of this model were evaluated using Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC), while nomograms and calibration curves were constructed. Then, functional enrichment analyses, tumor mutation burden (TMB), tumor infiltration of immune cells, single sample Gene Set Enrichment Analysis (ssGSEA), and drug sensitivity analysis were performed. Additionally, we conducted a consensus cluster analysis to compare differences between subtypes of tumors. Finally, we validated the expression of the ERS-related lncRNAs that constructed prognostic risk score model in HNSCC tissues through quantitative real-time PCR (qRT-PCR). We developed a prognostic signature based on seven ERS-related lncRNAs, which showed better predictive performance than other clinicopathological features. The high-risk poor prognosis group had a poorer prognosis in comparison to the low-risk good prognosis. The area under the ROC curve (AUC) predicted by this model for 3-year survival rates of HNSCC patients was 0.805. Enrichment analysis revealed that the differentially expressed genes were primarily enriched in pathways related to immune responses and signal transduction. Low-risk patients had lower TMB, more immune cell infiltrations, and enhanced anti-tumor immunity. Cluster analysis indicated that cluster 3 may have a better prognosis and immunotherapy effect. In addition, the result of qRT-PCR was consistent with our analysis. This prognostic model based on seven ERS-related lncRNAs is a promising tool for risk stratification, survival prediction, and immune cell infiltration status assessment.

Dysregulated of the FAM138E and CLRN1-AS1 lncRNAs could be diagnosis biomarkers in colorectal cancer

BackgroundColorectal cancer (CRC) is a malignant tumor that is prone to metastasis and seriously affects human health and longevity. Therefore, identifying suitable biomarkers for diagnostic and therapeutic purposes in CRC is a pressing requirement. The development of bioinformatics analysis has led to the introduction of an increasing number of long non-coding RNAs (lncRNAs) as potential biomarkers. MethodsThe Cancer Genome Atlas Program (TCGA) data for CRC were computationally analyzed to identify new and potential lncRNAs that are dysregulated in CRC. Real-time qPCR was conducted to quantitatively measure the expression levels of FAM138E and CLRN1-AS1 lncRNAs. ResultThis approach led us to select FAM138E and CLRN1-AS1 lncRNAs as dysregulated lncRNAs in the TCGA database. We then quantified the expression levels of these lncRNAs, which interestingly showed overexpression of CLRN1-AS1 (P-value<0.0001, Fold change = 1.55) and downregulation of FAM138E (P-value = 0.012, Fold change = 0.028) in tumors compared to normal adjacent tissues. ConclusionThe evaluated associations with prognosis in CRC using data from TCGA database showed that these lncRNAs could be used as diagnostic factors to detect tumors from normal samples. These findings may have implications for the deeper characterization of FAM138E and CLRN1-AS1 lncRNAs in colorectal cancer.

The complex nature of lncRNA-mediated chromatin dynamics in multiple myeloma.

Extensive genome-wide sequencing efforts have unveiled the intricate regulatory potential of long non-protein coding RNAs (lncRNAs) within the domain of haematological malignancies. Notably, lncRNAs have been found to directly modulate chromatin architecture, thereby impacting gene expression and disease progression by interacting with DNA, RNA, and proteins in a tissue- or condition-specific manner. Furthermore, recent studies have highlighted the intricate epigenetic control of lncRNAs in cancer. Consequently, this provides a rationale to explore the possibility of therapeutically targeting lncRNAs themselves or the epigenetic mechanisms that govern their activity. Within the scope of this review, we will assess the current state of knowledge regarding the epigenetic regulation of lncRNAs and how, in turn, lncRNAs contribute to chromatin remodelling in the context of multiple myeloma.

Expression of Pivotal Long Non-coding RNAs Implicated in Gastric Cancer: A Bioinformatic and Clinical Study.

Gastric cancer (GC) is a prominent public health issue and ranks as the third most prevalent cause of cancer-related mortality on a global scale. The role of long non-coding RNAs (lncRNAs) in cancer is not yet fully understood, particularly in relation to GC development. The objective of this study was to examine the expression levels of lncRNAs in GC tissues using a bioinformatics-based ranking approach. A bioinformatics methodology was employed to prioritize lncRNAs that are hypothesized to play a role in GC tumorigenesis. Moreover, a selection was made for experimental validation of the highest-ranked lncRNAs, which include HCG18, OIP5-AS1, FGD5-AS1, and NORAD. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) was employed to confirm the results obtained from bioinformatics analysis in a total of 35 GC samples and their corresponding adjacent non-tumoral samples. Receiver operating characteristic (ROC) curves and the corresponding area under the ROC curve (AUC) were utilized to evaluate the diagnostic efficacy of the lncRNAs. The bioinformatics analysis revealed that the lncRNA HCG18 is the highest-ranked lncRNA associated with GC. Furthermore, the expression levels of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were found to be significantly elevated in GC samples when compared to adjacent non-tumoral samples. The calculated values for the AUC of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were 0.80, 0.74, 0.73, and 0.71, respectively. The results of the study indicate that the lncRNAs HCG18, OIP5-AS1, FGD5-AS1, and NORAD may play a role in the development of GC. Additionally, the present study revealed that utilizing bioinformatic techniques can prove to be a highly effective strategy in identifying potential lncRNAs pertinent to the progression of GC.

The value of emerging prostate cancer-associated lncRNA PCGEM1 in various tumors.

LncRNA not only plays an auxiliary role as an intermediate carrier of genetic information, but also mediates various regulatory functions. The complex and precise regulatory function of lncRNA in development and gene expression has greatly explained the complexity of the genome, and also opened a new avenue for people to understand the complexity of life from the dimension of gene expression. Long non-coding RNA (lncRNA) prostate cancer gene expression marker 1 (PCGEM1) is a characterized oncogenic lncRNA in different cancers. This review is dedicated to summarizing the various molecular mechanisms of PCGEM1 and demonstrates that the biological functions exerted by PCGEM1 are good starting points for therapy. The molecular mechanism of PCGEM1 acting on tumors has been summarized by reviewing the literature available on PubMed. The expression of lncRNA PCGEM1 is abnormally increased in all kinds of tumors, including cervical cancer, prostate cancer, renal carcinoma, gastric cancer, lung cancer, ovarian carcinoma, cough variant asthma, and castration tolerant prostate cancer. Besides, PCGEM1 mediates multiple processes involved in carcinogenesis, including proliferation, invasion, anti-apoptosis, and migration. The potential of PCGEM1 as a therapeutic target for cancer is revealed.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

Prognostic potential of m7G-associated lncRNA signature in predicting bladder cancer response to immunotherapy and chemotherapy

Abstract Objectives The influence of N7-methylguanosine (m7G) on cancer prognosis and immune response has been well-reported. However, the role of m7G-related long non-coding RNAs (lncRNAs) in bladder cancer (BC) remains largely unexplored. This study wanted to explore the relationship between m7G-related lncRNAs and BC. Methods To construct the m7G-related lncRNA signature, we utilized data obtained from TCGA. The collected data was then analyzed using R (version 4.2.1, Bell Laboratories, Boston, USA) and relevant packages. Results The m7G-related lncRNA signature consisted of seven lncRNAs (including GATA3-AS1, LINC00930, LINC01341, MED14OS, MIR100HG, RUSC1-AS1, SNHG4). The prognostic and clinical relevance of the risk score was corroborated in both the TCGA and IMvigor210 datasets. Individuals characterized by a high-risk score displayed substantial enrichment in pathways associated with immunity, notably those pertaining to the innate immune response, cytokine-mediated signaling pathways, and the adaptive immune system. Additionally, the high-risk score group showed a positive correlation with many immune checkpoints, including CD274, CD40, CTLA4, PDCD1, PDCD1LG2, among others. Moreover, a significant difference in the TCIA score was observed between the high-risk and low-risk score groups, indicating the potential distinct immunotherapy response rates. Furthermore, patients with a high-risk score demonstrated increased sensitivity to cisplatin, docetaxel, doxorubicin, gemcitabine, and vinblastine. Conclusions This m7G-related lncRNA signature demonstrates considerable promise as a prognostic biomarker in BC, facilitating the anticipation of responses to both immunotherapy and chemotherapy. This study provides a solid foundation for future investigations into the role of m7G-related lncRNAs in BC.

LncRNA-HANR exacerbates malignant behaviors of cholangiocarcinoma cells through activating Notch pathway

ObjectivesCholangiocarcinoma (CHOL) is a malignant tumor from extrahepatic bile duct with poor prognosis. The critical roles of long non-coding RNAs (lncRNAs) in cancers including CHOL have been unveiled in recent decades. The present study was aimed to investigate the role and mechanism of a certain lncRNA, namely, hepatocellular carcinoma (HCC) associated long non-coding RNA (HANR) in CHOL. MethodsReverse transcription quantitative polymerase chain reaction (RT-qPCR) was applied for detecting gene expression. Functional assays were done for assessing CHOL cell malignancy and mechanistic assays were conducted for analyzing correlation between HANR and Notch signal pathway, as well as the relation between HANR and Notch intracellular domain (NICD) in CHOL cells. ResultsHANR was detected to be significantly overexpressed in CHOL cell lines. HANR silence inhibited cell proliferation, migration and stemness. Besides, HANR could positively regulate the Notch signaling pathway through modulating RBP-JK. HANR could bind to NICD and affect the transcriptional activity of RBP-JK. Furthermore, p-Notch1-NICD-r could wholly countervail the inhibitory effects of HANR silence on CHOL cell proliferation, migration and stemness. ConclusionHANR could activate Notch pathway by regulating the RBP-JK transcriptional activity, thus contributing to exacerbated malignant behaviors of CHOL cells.

AL360181.1 promotes proliferation and invasion in colon cancer and is one of ten m6A-related lncRNAs that predict overall survival.

N6-methyladenosine (m6A) exerted a pivotal role in colon cancer. Nevertheless, the long non-coding RNAs (lncRNAs) associated with this process have yet to be elucidated. The open-access data used for analysis was downloaded from The Cancer Genome Atlas (TCGA) database for analysis, employing the R software for computational evaluations. The RNA level of specific molecules was assessed using the quantitative real-time PCR. CCK8, colony formation and transwell assay were used to evaluate the proliferation, invasion and migration ability of colon cancer cells. Here, we identified the m6A regulators from TCGA data and subsequently pinpointed lncRNAs with a -Cor->0.3 and P<0.05, categorizing them as m6A-associated lncRNAs. Moreover, we formulated a prognosis signature rooted in ten m6A-related lncRNAs, consisting of AL360181.1, PCAT6, SNHG26, AC016876.1, AC104667.2, AL114730.3, LINC02257, AC147067.1, AP006621.3 and AC009237.14. This signature exhibited notable predictive accuracy in gauging patient survival. Immune-related evaluations revealed varied immune cell infiltration patterns across different risk groups, with our findings suggesting superior immunotherapy response in low-risk patients. Biological enrichment analysis indicated that the high-risk patients had a higher activity of multiple carcinogenic pathways, including glycolysis. The previously unreported lncRNA, AL360181.1, displayed a connection to glycolytic activity and diminished survival rates, warranting further investigation. The result indicated that AL360181.1 was correlated with more aggressive clinical characteristics. Immune infiltration assessments found AL360181.1 to have a positive correlation with Tcm infiltration, but an inverse relationship with entities like Th2 cells, T cells, neutrophils and macrophages. Biological enrichment analysis indicated that the pathways of WNT/β-catenin, pancreas beta cells, hedgehog signaling and some metabolism pathways were upregulated in high AL360181.1 patients. In vitro experiments showed that AL360181.1 was upregulated in the colon cancer cells. Moreover, AL360181.1 significantly promotes the proliferation, invasion and migration of colon cancer cells. Our results can provide direction for future studies on m6A-related lncRNA in colon cancer.

Expression and Regulatory Roles of Small Nucleolar RNA Host Gene 4 in Gastric Cancer.

The role of SNHG4 in the initiation and development of gastric cancer. Gastric cancer is one of the leading causes of cancer death worldwide. Studies have shown that lncRNAs have a regulatory function in human diseases, particularly cancers. Small nuclear RNA host gene 4 (SNHG4) has been known as an oncogenic long noncoding RNA (lncRNA) in various cancers, and its dysregulation can lead to tumorigenesis and cancer progression. The present study aims to determine the molecular mechanism of SNHG4 and the effects of its expression on the development of GC. Based on the bioinformatics investigations, we studied gene expression analysis, Kaplan-Meier survival, Gene ontology (GO), KEGG pathway enrichment, microRNA targets, transcription factor targets, and proteins interacting with SNHG4. During the experimental phase, SNHG4 expression was examined by quantitative real-time PCR (qRTPCR) in 40 paired gastric adenocarcinoma tissues and normal neighboring tissues. Also, we investigated the correlation between SNHG4 expression and patients' clinicopathological characteristics. Alteration of SNHG4 expression in gastric cancer and its correlation whit with clinical features of patients with stomach cancer; also, the accomplishment of bioinformatic analysis to find the potential pathways which could be impressed by changes in SNHG4 RNA expression. Increased SNHG4 expression was detected in GC tissues, which is significantly associated with the TNM stage, grade group, tumor size, and metastatic status. Evaluation survival analysis demonstrated that overexpression of SNHG4 in GC tissues is remarkably related to poor overall survival (OS). SNHG4 is closely related to miR-490 and E2F family transcription factors. GO analysis suggested the possible role of SNHG4 in cell-cell adhesion, and KEGG enrichment analysis revealed that SNHG4 could be associated with the gastric cancer signaling pathway. ELAVL1 and IGF2BP2 have the highest number of SNHG4 target sites, and these proteins are involved in the PI3K-Akt-mTOR and ERK-MAPK signaling pathways. Based on our results, we conclude that SNHG4 may have a function in GC development by regulating tumor-related signaling pathways.

X-LDA: An interpretable and knowledge-informed heterogeneous graph learning framework for LncRNA-disease association prediction

The identification of disease-related long noncoding RNAs (lncRNAs) is beneficial to unravel the intricacies of gene expression regulation and epigenetic signatures. Computational methods provide a cost-effective means to explore lncRNA-disease associations (LDAs). However, these methods often lack interpretability, leaving their predictions less convincing to biological and medical researchers. We propose an interpretable and knowledge-informed heterogeneous graph learning framework based on graph patch convolution and integrated gradients to predict LDAs and provides intuitive explanations for its predictions, called X-LDA. The heterogeneous graph is the foundation of the predictions of LDAs, we construct the knowledge-informed heterogeneous graph including LDAs drawn from biological experiments, lncRNA similarities rooted in gene sequences, disease similarities constructed based on disease categorizations. To integrate diverse biological premises and facilitate interpretability, we define nine distinct graph patch types, which encapsulate essential topological relationships within lncRNA-disease node pairs. X-LDA is designed to employ parameter sharing and multi-convolution kernels to grasp common and multiple perspectives of the graph patches, respectively. This approach culminates in the fusion of various semantic information into context embeddings. These post-hoc explanations hinge on graph patch features and integrated gradients, shedding light on the underlying factors driving predictions. Cross validation experiment on the dataset curated from databases and literatures demonstrates that the superior performance of X-LDA in comparison to nine state-of-the-art methods of three categories. X-LDA achieves a larger average area under the receiver operating curve 0.9891 (by at least 6.68%), and a larger average area under the precision–recall curve 0.7907 (by at least 23.2%) than competitive methods. The results of our well-designed ablation and interpretability experiments and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis demonstrate X-LDA’s robustness, learnability, predictability, and interpretability. The applicability of X-LDA is also demonstrated through a case study involving the investigation of associated lncRNAs in prostate cancer, colorectal cancer, and breast cancer.

Identification of pyroptosis-related lncRNA subtype and signature predicts the prognosis in bladder cancer.

Pyroptosis is a new type of programmed cell death involved in all stages of tumorigenesis. Herein, a comprehensive study was conducted to evaluate the prognostic significance of pyroptosis-related lncRNAs in bladder cancer. Consensus clustering analysis was performed to identify the subclusters of bladder cancer. The prognostic pyroptosis-related lncRNA signature was constructed using LASSO Cox regression analysis. Consensus clustering identified 2 clusters of bladder cancer. Interestingly, significant differences in the ESTIMAE score, immune cell infiltration and immune checkpoint expression were obtained between the 2 clusters. A signature consisting of 11 pyroptosis-related lncRNAs was established and it had a good performance in predicting the overall survival rate of bladder cancer, with an AUC of 0.713. Moreover, pyroptosis-related lncRNA signature acted as a risk factor in bladder cancer. Bladder cancer patients with high-risk score had a higher tumor grade and higher clinical stage. A significant correlation was obtained between the risk score and immune cell infiltration. The expression of most checkpoints was higher in bladder cancer patients with high-risk score. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets in bladder cancer.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms.

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

BackgroundMicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking. MethodsUsing the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules. ResultsOur tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA–mRNA–lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers. ConclusionExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

Systematic analysis of DNA methylation-mediated TF dysregulation on lncRNAs reveals critical roles in tumor immunity

Emerging evidence suggests that DNA methylation affects transcriptional regulation and expression perturbations of long non-coding RNAs (lncRNAs) in cancer. However, a comprehensive investigation into the transcriptional control of DNA methylation-mediated dysregulation of transcription factors (TFs) on lncRNAs has been lacking. Here, we integrated the transcriptome, methylome, and regulatome across 21 human cancers and systematically identified the transcriptional regulation of DNA methylation-mediated TF dysregulations (DMTDs) on lncRNAs. Our findings reveal that TF regulation of lncRNAs is significantly impacted by DNA methylation. Comparative analysis of DMTDs on mRNAs revealed a conserved pattern of TFs involvement. Pan-cancer Methylation TFs (MethTFs) and Methylation LncRNAs (MethLncRNAs) were identified, and were found to be closely associated with cancer hallmarks and clinical features. In-depth analysis of co-expressed mRNAs with pan-cancer MethLncRNAs unveiled frequent disruptions in cancer immunity, particularly in the context of inflammatory response. Furthermore, we identified five immune-related network modules that contribute to immune cell infiltration in cancer. Immune-related subtypes were subsequently classified, characterized by high levels of immune cell infiltration, expression of immunomodulatory genes, and relevant immune cytolytic activity score, major histocompatibility complex score, response to chemotherapy, and prognosis. Our findings provide valuable insights into cancer immunity from the epigenetic and transcriptional regulation perspective.

Systematic integration of m6A regulators and autophagy-related genes in combination with long non-coding RNAs predicts survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is probably the only tumor in which a unique epigenetic alteration, namely methylation of the MGMT gene, possesses direct clinical relevance. Now with the emergence of aberrant N6 methyladenosine (m6A) modifications (the most common epigenetic modification of mRNA, closely linked to the autophagy process) in cancer, the epi-transcriptomic landscape of GBM pathobiology has been expanded. Considering this, herein, we systematically analyzed m6A regulators, assessed their correlation with autophagy-related genes (ATG), and established a long non-coding RNAs (lncRNA)-dependent prognostic signature (m6A-autophagy-lncRNAs) for GBM. Our analysis identified a novel signature of five long non-coding RNAs (lncRNAs: ITGA6-AS1, AC124248.1, NFYC-AS1, AC025171.1, and AC005229.3) associated with survival of GBM patients, and four among them clearly showed cancer-associated potential. We further validated and confirmed the altered expression of two lncRNAs (AC124248.1, AC005229.3) in GBM associated clinical samples using RT-PCR. Concerning the prognostic ability, the obtained signature determined high-/low-risk groups in GBM patients and showed sensitivity to anticancer drugs. Collectively, the m6A-autophagy-lncRNAs signature presented in the study is clinically relevant and is the first attempt to systematically predict the potential interaction between the three key determinants (m6A, autophagy, lncRNA) in cancer, particularly in GBM.

Expression analysis of PPAR-related lncRNAs in breast cancer

Breast cancer is a genetically heterogeneous disorder associated with dysregulation of several genes. Peroxisome proliferator-activated receptor gamma (PPARγ) is a ligand-dependent transcription factor that is expressed by many tumoral cells such as transformed breast cancer cells. We investigated expressions of nine PPARγ-related lncRNAs, namely KCNIP2-AS1, TRHDE-AS1, FAM13A-AS1, ALDH1A1-AS2, SH3BP5-AS1, HID1-AS1, LINC01140, LIPE-AS1 and ABCA9-AS1 in paired breast cancer samples and non-tumoral tissues. Expression assays showed lower expression levels of TRHDE-AS1, ALDH1L1-AS2, KCNIP2-AS1, ABCA9-AS1, LIPE-AS1 and LINC01140 in tumoral compared with non-tumoral samples. The mentioned genes could differentiate between breast tumors and non-tumoral samples with AUC values ranging from 0.77 to 0.62 for LINC01140 and LIPE-AS1, respectively. The highest specificity and sensitivity values were reported for KCNIP2-AS1 and LINC01140, respectively. Significant correlations were reported between all pairs of genes in both tumoral and non-tumoral tissues. The most robust ones were between ABCA9-AS1 and KCNIP2-AS1 (correlation coefficient=0.85) in non-tumoral tissues and between LIPE-AS1 and TRHDE-AS1 (correlation coefficient=0.83) in tumoral tissues. There was a significant negative association between expression levels of KCNIP2-AS1 gene in tumor tissues and different histological grades. Besides, there was a significant negative association between expression levels of FAM13A-AS1, KCNIP2-AS1and LIPE-AS1 genes in tumor tissues and different mitotic rates. Taken together, PPARγ-related lncRNAs might be regarded as potential contributors to the pathogenesis of breast cancer.