Related lncRNAs Research Articles

Interplay between lncRNAs and the PI3K/AKT signaling pathway in the progression of digestive system neoplasms (Review).

Long non‑coding RNA (lncRNA) is a class of non‑coding RNA molecules located in the cytoplasm or nucleus, which can regulate chromosome structure and function by interacting with DNA, RNA, proteins and other molecules; binding to mRNA bases in a complementary manner, affecting the splicing, stabilization, translation and degradation of mRNA; acting as competing endogenous RNA competitively binds to microRNAs to regulate gene expression and participate in the regulation of various vital activities of the body. The PI3K/AKT signalling pathway plays a key role in numerous biological and cellular processes, such as cell proliferation, invasion, migration and angiogenesis. It has been found that the lncRNA/PI3K/AKT axis regulates the expression of cancer‑related genes and thus tumour progression. The abnormal regulation of lncRNA expression in the lncRNA/PI3K/AKT axis is clearly associated with clinicopathological features and plays an important role in regulating biological functions. In the present review, the expression and biological functions of PI3K/AKT‑related lncRNAs both in vitro and in vivo over recent years, were comprehensively summarized and analyzed. Their correlation with clinicopathological features was also evaluated, with the objective of furnishing a solid theoretical foundation for clinical diagnosis and the monitoring of efficacy in digestive system neoplasms. The present review aimed to provide a comprehensive overview of the expression and biological functions of PI3K/AKT‑related lncRNAs in digestive system neoplasms and to assess their correlation with clinicopathological features. This endeavor seeks to establish a solid theoretical foundation for the clinical diagnosis and efficacy monitoring of digestive system tumors.

Human-specific protein-coding and lncRNA genes cast sex-biased genes in the brain and their relationships with brain diseases

BackgroundGene expression shows sex bias in the brain as it does in other organs. Since female and male humans exhibit noticeable differences in emotions, logical thinking, movement, spatial orientation, and even the incidence of neurological disorders, sex biases in the brain are especially interesting, but how they are determined, whether they are conserved or lineage specific, and what the consequences of the biases are, remain poorly explored and understood.MethodsBased on RNA-seq datasets from 16 and 14 brain regions in humans and macaques across developmental periods and from patients with brain diseases, we used linear mixed models (LMMs) to differentiate variations in gene expression caused by factors of interest and confounding factors and identify four types of sex-biased genes. Effect size and confidence in each effect were measured upon the local false sign rate (LFSR). We utilized the biomaRt R package to acquire orthologous genes in humans and macaques from the BioMart Ensembl website. Transcriptional regulation of sex-biased genes by sex hormones and lncRNAs were analyzed using the CellOracle, GENIE3, and Longtarget programs. Sex-biased genes’ functions were revealed by gene set enrichment analysis using multiple methods.ResultsLineage-specific sex-biased genes greatly determine the distinct sex biases in human and macaque brains. In humans, those encoding proteins contribute directly to immune-related functions, and those encoding lncRNAs intensively regulate the expression of other sex-biased genes, especially genes with immune-related functions. The identified sex-specific differentially expressed genes (ssDEGs) upon gene expression in disease and normal samples also indicate that protein-coding ssDEGs are conserved in humans and macaques but that lncRNA ssDEGs are not conserved. The results answer the above questions, reveal an intrinsic relationship between sex biases in the brain and sex-biased susceptibility to brain diseases, and will help researchers investigate human- and sex-specific ncRNA targets for brain diseases.ConclusionsHuman-specific genes greatly cast sex-biased genes in the brain and their relationships with brain diseases, with protein-coding genes contributing to immune response related functions and lncRNA genes critically regulating sex-biased genes. The high proportions of lineage-specific lncRNAs in mammalian genomes indicate that sex biases may have evolved rapidly in not only the brain but also other organs.

Identification of Disulfidptosis-Related LncRNA Subtypes, Establishment of a Prognostic Signature, and Characterization of Immune Infiltration in Ovarian Cancer.

Ovarian Cancer (OC) is a lethal malignant tumor with a poor prognosis. Disulfidptosis is a newly identified form of cell death caused by disulfide stress. Targeting disulfidptosis is a new metabolic therapeutic strategy in cancer treatment. We aimed to establish a disulfidptosis- related lncRNA signature for prognosis prediction and explore its treatment values in OC patients. Data from the TCGA and GTEx databases and a disulfidptosis gene set were used to establish a disulfidptosis-related lncRNA signature for prognosis prediction in OC patients. Then, we internally and externally (PCR) validated our model. We also built a nomogram to improve our model's predictive power. Afterward, GSEA was employed to explore our model's potential functions. The ESTIMATE, CIBERSORT, TIMER, and ssGSEA were applied to estimate the immune landscape. Finally, the drug sensitivity of certain drugs for OC patients was analyzed. We built a prognosis model based on seven drlncRNAs, including AL157871.2, HCP5, AC027348.1, AL109615.3, AL928654.1, LINC02585, and AC011445.1. Our model performed well by internal validation. PCR data also confirmed the same trend in the lncRNA levels. Furthermore, the nomogram-integrated age, grade, stage, and risk score could accurately predict the survival outcomes of OC patients. Subsequently, GSEA unveiled that our model genes enriched the Hedgehog signaling pathway, a key regulator in OC tumorigenesis. Our predictive signature was associated with immune checkpoints, such as PD-1(P < 0.01), PD-L1(P < 0.001), and CTLA4 (P < 0.01), which might help screen out OC patients who are sensitive to immunotherapy. Small molecule drugs, such as AZD-2281, GDC-0449, imatinib, and nilotinib, might benefit OC patients with different risk scores. Our disulfidptosis-related lncRNA signature comprised of AL157871.2, HCP5, AC027348.1, AL109615.3, AL928654.1, LINC02585, and AC011445.1 could serve as a prognostic biomarker and guidance to therapy response for OC patients.

A novel arginine methylation-associated lncRNA signature effectively predicts prognosis in breast cancer patients.

Breast cancer (BC) is a disease highly associated with epigenetic modification, and arginine methylation is particularly important in its genetic regulation. However, the role of arginine methylation related lncRNAs in breast cancer has not been studied. First, we identified the related lncRNAs (from TCGA database) according to the differentially expressed genes related to arginine methylation in breast cancer. Then the lncRNAs related to protein arginine methylation were obtained by regression analysis, and the risk score model was constructed. Finally, the cell experiment and subcutaneous tumor model verified that the arginine methylation related lncRNA z68871.1 in the model had a significant effect on the proliferation and invasion of breast cancer cells. In conclusion, we successfully constructed an arginine methylation related lncRNA model, which has strong predictive ability. At the same time, this study provides an experimental basis for exploring the mechanism of arginine methylation in BC and helps to find new biomarkers of BC.

Upregulation of long non-coding RNA ENSG00000267838 is related to the high risk of progression and non-response to chemoradiotherapy treatment for cervical cancer

Cervical cancer (CC) is a global public health concern, primarily caused by persistent infection with oncogenic types of human papillomavirus (HPV). The World Health Organization (WHO) has established a plan to eliminate CC as a public health issue by the year 2100. However, the implementation of the HPV vaccine is impeded by vaccine restrictions and misinformation despite its demonstrated effectiveness. The CC treatment is influenced by the disease stage, with an unfavorable prognosis for those in advanced stages. This study aimed to investigate the potential of long non-coding RNAs (lncRNAs) in CC by identifying and characterizing related lncRNAs, elucidating their regulatory mechanisms and molecular interactions, and analyzing their expression patterns in patients with diverse responses to chemoradiotherapy. Non-stem cells from CC were isolated using flow cytometry sorting and used for total RNA extraction. The RNA was used to build libraries that were subsequently sequenced using the Illumina Nextseq 550.417 lncRNAs that showed differentially expressed between CC patients who responded or not to treatment. Further analysis demonstrated that these lncRNAs significantly interact with several molecules, which play crucial roles in CC progression and therapeutic resistance. Statistical analysis correlated the expression profile of these lncRNAs with treatment efficacy. Three lncRNAs, ENSG00000267838, ENSG00000266340, and FRMD6-AS1, were identified with positive expression related to non-response to chemoradiotherapy and worse progression-free survival in CC patients. Specifically, lncRNA ENSG00000267838 has its up-regulation related to non-response and down-regulation to response to chemoradiotherapy treatment.

Integrating Bioinformatics and Experimental Validation to Identify Mitochondrial Permeability Transition-Driven Necrosis-Related lncRNAs that can Serve as Prognostic Biomarkers and Therapeutic Targets in Endometrial Carcinoma.

Endometrial carcinoma (EC) is a common malignant tumor in women with high mortality and relapse rates. Mitochondrial permeability transition (MPT)-driven necrosis is a novel form of programmed cell death. The MPT-driven necrosis related lncRNAs (MRLs) involved in EC development remain unclear. We aimed to predict the outcomes of patients with EC by constructing a novel prognostic model based on MRLs and explore potential molecular functions. A risk prognostic model was developed utilizing multi-Cox regression in conjunction with the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, which was based on MRLs. The predictive efficacy of the model was evaluated through receiver operating characteristic (ROC) curve analysis, as well as nomogram and concordance index (C-index) assessments. Patients were categorized into high- and low-risk groups based on their median risk scores. Notably, the high-risk group exhibited significantly poorer overall survival (OS) outcomes. Gene ontology (GO) and Gene set enrichment analysis (GSEA) demonstrated that Hedgehog and cell cycle pathways were enriched in the high-risk group. Tumor Immune Dysfunction and Exclusion (TIDE) displayed that patients in the high-risk group showed a high likelihood of immune evasion and less effective immunotherapy. A significant disparity in immune function was also observed between two groups. Based on the nine-MRLs, drug sensitivity analysis identified several anticancer drugs with potential efficacy in prognosis. Meanwhile, the results demonstrated that OGFRP1 plays a carcinogenic role by affecting mitochondrial membrane permeability in EC. Therefore, the risk model constructed by nine MRLs could be used to predict the clinical outcomes and therapeutic responses in patients with EC effectively.

Exploring and validating the necroptotic gene regulation and related lncRNA mechanisms in colon adenocarcinoma based on multi-dimensional data.

Necroptosis is intimately associated with the initiation and progression of colon adenocarcinoma (COAD). However, studies on necroptosis-related genes (NRGs) and the regulating long non-coding RNAs (NRGlncRNAs) in the context of COAD are limited. We retrieved the cancer genome atlas (TCGA) to collect datasets of NRGs and NRGlncRNAs on COAD patients. The risk model constructed using Cox and least absolute shrinkage and selection operator (LASSO) regression was then employed to identify NRGs and NRGlncRNAs with prognostic significance. Subsequently, we validated the results using gene expression omnibus (GEO) datasets from different populations, conducted Mendelian randomization (MR) analysis to explore the potential causal relationships between prognostic NRGs and COAD, and conducted cell experiments to verify the expression of prognostic NRGlncRNAs in COAD. Furthermore, we explored potential pathways and regulatory mechanisms of these prognostic NRGlncRNAs and NRGs in COAD through enrichment analysis, immune cell correlation analysis, tumor microenvironment analysis, immune checkpoint analysis, tumor sample clustering, and so on. We identified eight NRGlncRNAs (AC245100.5, AP001619.1, LINC01614, AC010463.3, AL162595.1, ITGB1-DT, LINC01857, and LINC00513) used for constructing the prognostic model and nine prognostic NRGs (AXL, BACH2, CFLAR, CYLD, IPMK, MAP3K7, ATRX, BRAF, and OTULIN) with regulatory relationships with them, and their validation was performed using GEO and GWAS datasets, as well as cell experiments, which showed largely consistent results. These prognostic NRGlncRNAs and NRGs modulate various biological functions, including immune inflammatory response, oxidative stress, immune escape, telomere regulation, and cytokine response, influencing the development of COAD. Additionally, stratified analysis of the high-risk and low-risk groups based on the prognostic model revealed elevated expression of immune cells, increased expression of tumor microenvironment cells, and upregulation of immune checkpoint gene expression in the high-risk group. Finally, through cluster analysis, we identified tumor subtypes, and the results of cluster analysis were essentially consistent with the analysis between risk groups. The prognostic NGRlncRNAs and NRGs identified in our study serve as prognostic indicators and potential therapeutic targets for COAD, providing a theoretical basis for the clinical diagnosis and treatment of COAD and offering guidance for further research.

Construction of an endoplasmic reticulum stress and cuproptosis -related lncRNAs signature in chemosensitivity in hepatocellular carcinoma by comprehensive bioinformatics analysis

Endoplasmic reticulum stress (ERS) and cuproptosis have remarkable effects on hepatocellular carcinoma (HCC) leading to a poor prognosis. The current study aimed to explore credible signature for predicting the prognosis of HCC based on ERS and cuproptosis-related lncRNAs. In our study, clinical and transcriptomic profiles of HCC patients were obtained from the Cancer Genome Atlas (TCGA) database. An ERS and cuproptosis-related lncRNA prognostic signature, including NRAV, SNHG3, LINC00839 and AC004687.1, was determined by correlation tests, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) methods. Survival and predictive value were evaluated using Kaplan–Meier and receiver operating characteristic (ROC) curves, while calibration and nomograms curves were developed. Besides the enrichment analyses for ERS and cuproptosis-related lncRNAs, mutational status and immune status were assessed with TMB and ESTIMATE. Additionally, consensus cluster analysis was employed to compare cancer subtype differences, while drug sensitivity and immunologic efficacy were evaluated for further exploration. qRT-PCR and CCK-8 were utilized to verify the alteration of the prognostic lncRNAs expression and proliferation in vitro. High-risk groups exhibited poorer prognosis. The signature exhibited robust predictive value as an independent prognostic indicator and showed significant correlation with clinicopathological features. In the enriched analysis, biological membrane pathways were enriched. Low-risk patients had lower TMB and higher immune status. A cluster analysis revealed that cluster 2 had the best clinical immunological efficacy and most active immune function. In brief, our constructed signature with ERS and cuproptosis-related lncRNAs was associated survival outcomes of HCC, and can be used to predict the clinical classification and curative effect.

M6A-mediated lnc-OXAR promotes oxaliplatin resistance by enhancing Ku70 stability in non-alcoholic steatohepatitis-related hepatocellular carcinoma

BackgroundThe escalating prevalence of metabolic diseases has led to a rapid increase in non-alcoholic steatohepatitis (NASH)-related hepatocellular carcinoma (NASH-HCC). While oxaliplatin (OXA)-based hepatic arterial infusion chemotherapy (HAIC) has shown promise in advanced-stage HCC patients, its efficacy in NASH-HCC remains uncertain. This study aims to assess the effectiveness of OXA-based HAIC and elucidate the mechanisms underlying OXA resistance in NASH-HCC.MethodsThe key lncRNAs were screened through RNA-seq analysis of NASH/non-NASH and OXA-sensitive/OXA-resistant (OXA-S/R) HCC tissues. The biological functions of the lnc-OXAR (OXA resistance–related lncRNA in NASH-HCC) in NASH-HCC were verified through a series of in vitro and in vivo experiments. The molecular mechanism of lnc-OXAR was elucidated by fluorescence in situ hybridization, immunoprecipitation-mass spectrometry (FISH), Immunoprecipitation-Mass Spectrometry (IP-MS), RNA pulldown, RNA immunoprecipitation (RIP), methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and a dual-luciferase reporter assay.ResultsNASH-HCC exhibited reduced responsiveness to OXA-based HAIC compared to non-NASH HCC. We identified and validated a novel transcript namedlnc-OXAR, which played a crucial role in conferring OXA resistance to NASH-HCC. Inhibition of lnc-OXAR suppressed HCC cell growth and restored OXA sensitivity both in NASH-HCC mouse models and in vitro. Mechanistically, lnc-OXAR recruited Ku70 and cystatin A (CSTA), preventing Ku70 degradation and facilitating DNA double-strand break (DSB) repair, thereby promoting OXA resistance in NASH-HCC. Additionally, WTAP-mediated m6A modification enhanced the stability of lnc-OXAR in an IGF2BP2-dependent manner. Notably, silencing lnc-OXAR significantly enhanced the response to OXA in patient-derived xenograft (PDX) models derived from NASH-HCC.ConclusionsThe reduced responsiveness of NASH-HCC to OXA treatment can be attributed to the upregulation of lnc-OXAR. Our findings provide a rationale for stratifying HCC patients undergoing OXA-based HAIC based on etiology. Lnc-OXAR holds promise as a novel target for overcoming OXA resistance in NASH-HCC and improving prognosis.

Long non-coding RNA SNHG7 serves as a diagnostic biomarker for acute coronary syndrome and its predictive value for the clinical outcome after percutaneous coronary intervention

BackgroundAcute coronary syndrome (ACS) is one of the common causes of cardiovascular death. The related lncRNAs were novel approaches for early diagnosis and intervention. This paper focused on the clinical function of SNHG7 for patients after PCI.MethodsThe expression of SNHG7 was assessed in ACS patients. The predictive roles of SNHG7 were unveiled by the ROC curve. The relationship between SNHG7 and Gensini scores was judged by Pearson analysis. One-year follow-up was conducted and all patients were catalogued into different groups based on the prognosis. The qRT-PCR, K-M curve, and Cox regression analysis were performed to document the prognostic significance of SNHG7.ResultsSNHG7 was highly expressed in ACS and its three subtypes. SNHG7 showed a certain value in predicting ACS, UA, NSTEMI, and STEMI. Gensini is a closely correlated indicator of SNHG7. The declined expression of SNHG7 was observed in the non-MACE and survival groups. The risk of MACE and death was increased in the group with high expression of SNHG7. SNHG7 was an independent biomarker in patients with ACS after PCI.ConclusionsSNHG7 might be a diagnostic and prognostic tool for ACS patients.

Identification of Potential Biomarkers for Patients with DWI-Negative Ischemic Stroke

Ischemic stroke is the leading cause of long-term disability in adults, accounting for 80% of stroke cases. Diffusion weighted imaging (DWI) examination is the main test for acute ischemic stroke, but in recent years, several studies have shown that some patients show negative DWI examination after the onset of ischemic stroke with symptoms of significant neurological deficits. In this study, we investigated potential biomarkers related to immune metabolism in the peripheral blood of DWI-negative versus DWI-positive patients after ischemic stroke and explored their possible regulatory processes in ischemic stroke. The datasets related to ischemic stroke were downloaded from the GEO database, immune-related genes and metabolism-related genes were obtained from the ImmPort database and MSigDB database, respectively, and immune-related differential genes were obtained based on immune scores using the algorithm of the R software package “GSVA.” Candidate genes were selected based on intersections, hub genes were screened using the algorithm in Cytoscape software, and finally, GeneMANIA analysis, GSEA enrichment analysis, subcellular localization, gene transcription factor and gene-drug interaction networks, and disease correlation analyses were performed for the hub genes. Five hub genes (GART, TYMS, PPAT, CTPS1, and PAICS) were obtained by PPI network analysis and software analysis. Among them, PPAT and PAICS may be the real hub genes with consistent and significantly differentiated results from the discovery and validation sets. The functions of these hub genes may be related to pathways such as nucleotide biosynthetic processes. The constructed hub gene ceRNA network showed that hsa-10a-5p is the key miRNA connecting PAICS and multiple lncRNAs in this study. Differential genes related to immunity and metabolism in DWI-negative and DWI-positive patients after IS were identified using bioinformatics analysis, and their pathways and related TF-RNAs, miRNAs, and lncRNAs were identified. These genes may be considered effective targets for the diagnosis and treatment of ischemic stroke.

M6A- and m5C- modified lncRNAs orchestrate the prognosis in cutaneous melanoma and m6A- modified LINC00893 regulates cutaneous melanoma cell metastasis.

As the most important modifications on the RNA level, N6-methyladenosine (m6A-) and 5-methylcytosine (m5C-) modification could have a direct influence on the RNAs. Long non-coding RNAs (lncRNAs) could also be modified by methylcytosine modification. Compared with mRNAs, the function of lncRNAs could be more potent to some extent in biological processes like tumorigenesis. Until now, rare reports have been done associated with cutaneous melanoma. Herein, we wonder if the m6A- and m5C- modified lncRNAs could influence the immune landscape and prognosis in melanoma, and we also want to find some lncRNAs which could directly affect the malignant behaviors of melanoma. Systematically, we explored the expression pattern of m6A- and m5C- modified lncRNAs in melanoma from datasets including UCSC Xena and NCBI GEO, and the prognostic lncRNAs were selected. Then, according to the expression pattern of lncRNAs, melanoma samples from these datasets were divided into several subtypes. Prognostic model, nomogram survival model, drug sensitivity, GO, and KEGG pathway analysis were performed. Furthermore, among several selected lncRNAs, we identified one lncRNA named LINC00893 and investigated its expression pattern and its biological function in melanoma cell lines. We identified 27 m6A- and m5C- related lncRNAs which were significantly associated with survival, and we made a subtype analysis of melanoma samples based on these 27 lncRNAs. Among the two subtypes, we found differences of immune cells infiltration between these two subtypes. Then, LASSO algorithm was used to screen the optimized lncRNAs combination including ZNF252P-AS1, MIAT, FAM13A-AS1, LINC-PINT, LINC00893, AGAP2-AS1, OIP5-AS1, and SEMA6A-AS1. We also found that there was a significant correlation between the different risk groups predicted based on RS model and the actual prognosis. The nomogram survival model based on independent survival prognostic factors was also constructed. Besides, sensitivity to chemotherapeutic agents, GO and KEGG analysis were performed. In different risk groups, a total of 14 drug molecules with different distributions were obtained, which included AZD6482, AZD7762, AZD8055, camptothecin, dasatinib, erlotinib, gefitinib, gemcitabine, GSK269962A, nilotinib, rapamycin, and sorafenib. A total of 55 significantly related biological processes and 17 KEGG signaling pathways were screened. At last, we noticed that LINC00893 had a relatively lower expression in melanoma tissue and cell lines compared with adjacent tissues and epidermal melanocyte, and down-regulation of LINC00893 could promote the malignant behavior of melanoma cells in A875 and MV3. In these two melanoma cell lines, down-regulation of m6A-related molecules like YTHDF3 and METTL3 could promote the expression of LINC00893. We made an analysis of m6A- and m5C- related lncRNAs in melanoma samples and a prediction of these lncRNAs' role in prognosis, tumor microenvironment, immune infiltration, and clinicopathological features. We also found that LINC00893, which is potentially regulated by m6A modification, could serve as a tumor-suppressor in melanoma and play an inhibitory role in melanoma metastasis.

Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan–Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis.

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Long non-coding RNAs (lncRNAs) and cancer stem cells (CSCs) are crucial for the growth, migration, recurrence, and medication resistance of tumors. However, the impact of lncRNAs related to stemness on the outcome and tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC) is still unclear. In this study, we aimed to predict the outcome and TIME of ccRCC by constructing a stem related lncRNAs (SRlncRNAs) signature. We firstly downloaded ccRCC patients’ clinical data and RNA sequencing data from UCSC and TCGA databases, and abtained the differentially expressed lncRNAs highly correlated with stem index in ccRCC through gene expression differential analysis and Pearson correlation analysis. Then, we selected suitable SRlncRNAs for constructing a prognostic signature of ccRCC patients by LASSO Cox regression. Further, we used nomogram and Kaplan Meier curves to evaluate the SRlncRNA signature for the prognose in ccRCC. At last, we used ssGSEA and GSVA to evaluate the correlation between the SRlncRNAs signature and TIME in ccRCC. Finally, We obtained a signtaure based on six SRlncRNAs, which are correlated with TIME and can effectively predict the ccRCC patients’ prognosis. The SRlncRNAs signature may be a noval prognostic indicator in ccRCC.

Bioinformatics analysis of markers based on m6A related to prognosis combined with immune invasion of rectal adenocarcinoma.

Colorectal cancer (CRC) is a common form of cancer, with rectal cancer accounting for approximately one-third of all cases. Among rectal cancers, 95% are classified as rectal adenocarcinoma (READ). Emerging evidence suggests that long noncoding RNAs (lncRNAs) play a significant role in the development and progression of various cancers. In our study, we aimed to identify differentially expressed lncRNAs potentially associated with m6A and establish a risk assessment model to predict clinical outcomes for READ patients. The READ dataset from the TCGA database was utilized in this study to synergistically and logically integrate m6A and lncRNA, while employing bioinformatics technology for the identification of suitable biomarkers. A risk prediction model comprising m6A-associated lncRNAs was constructed to investigate the prognostic, diagnostic, and biological functional relevance of these m6A-related lncRNAs. Our research builds a composed of three related to m6A lncRNA rectal gland cancer prognosis model, and the model has been proved in the multi-dimensional can serve as the potential of the prognosis of rectal gland cancer biomarkers. Our study constructed a prognostic model of rectal adenocarcinoma consisting of three related m6A lncRNAs: linc00702, ac106900.1 and al583785.1. The model has been validated as a potential prognostic biomarker for rectal cancer in multiple dimensions, aiming to provide clinicians with an indicator to assess the duration of straight adenocarcinoma. This enables early detection of rectal cancer and offers a promising target for immunotherapy.

Basement membrane-related lncRNA signature for the prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the main type of primary liver cancer. This study aimed to develop a basement membrane (BM) related lncRNAs risk signature to evaluate the prognosis of HCC patients. We screened differentially expressed BM-related lncRNAs (DE-BMRlncRNAs) for risk evaluation, and identified six DE-BMRlncRNAs (AC072054.1, NUP50-DT, AC026412.3, AC109322.2, POLH-AS1 and LINC00595) for prognostic risk signature. HCC patients were divided to high or low risk according to median risk score. Our prognostic model predicted that patients with higher risk score had worse prognosis. We also created a nomogram to assist clinical decision-making according to risk score and clinicopathological features. Meanwhile, we confirmed the expression of six lncRNAs in HCC tissue and cells. POLH-AS1 knockdown inhibited the migration and invasion of HCC cells. In conclusion, we established a predictive model based on BMRlncRNAs to predict the prognosis of HCC. Our findings offer a rationale to further explore BM-related biomarkers for HCC.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

PurposeThe objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis. MethodsAML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs. Results816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups. ConclusionLncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Expression analysis of necroptosis related genes and lncRNAs in patients with pituitary neuroendocrine tumors

Necroptosis can either be the cause of tumorigenesis or it can impede its process. Recently, it has been proved that long non-coding RNAs (lncRNAs) have different crucial roles at cellular level, especially on cell death. Regarding the important role of necroptosis and lncRNAs in the pathogenesis of different cancers, especially pituitary adenomas (PAs), we assessed expression levels of two necroptosis related genes, namely TRADD and BIRC2, in addition to three related lncRNAs, namely FLVCR1-DT, MAGI2-AS3, and NEAT1 in PAs compared with adjacent normal tissues (ANTs). TRADD had no significant difference between two groups; however, BIRC2, FLVCR1-DT, MAGI2-AS3, and NEAT1 were upregulated in PAs compared to ANTs (Expression ratios [95% CI] = 2.3 [1.47–3.6], 2.13 [1.02–4.44], 3.01 [1.76–5.16] and 2.47 [1.37–4.45], respectively). When taking into account different types of PAs, significant upregulation of BIRC2, MAGI2-AS3 and NEAT1 was recorded in non-functioning PAs compared with corresponding ANTs (Expression ratios [95% CI] =1.9 [1.04–3.43], 2.69 [1.26–5.72] and 2.22 [0.98–5.01], respectively). Additionally, higher levels of BIRC2 were associated with higher flow of CSF (P value=0.048). Moreover, higher Knosp classified tumors had lower levels of BIRC2 (P value=0.001). Finally, lower levels of MAGI2-AS3 were associated with larger tumor size (P value=0.006). NEAT1 expression was correlated with FLVCR1-DT and TRADD. TRADD expression was correlated with FLVCR1-DT. Additional correlation was observed between expression of BIRC2 and MAGI2-AS3. In sum, this study provides evidence that dysregulated levels of studied genes could contribute to the pathogenesis of pituitary tumors.

A 69 long noncoding RNA signature predicts relapse and acts as independent prognostic factor in pediatric AML

AbstractRisk stratification using genetics and minimal residual disease has allowed for an increase in the cure rates of pediatric acute myeloid leukemia (pedAML) to up to 70% in contemporary protocols. Nevertheless, ∼30% of patients still experience relapse, indicating a need to optimize stratification strategies. Recently, long noncoding RNA (lncRNA) expression has been shown to hold prognostic power in multiple cancer types. Here, we aimed at refining relapse prediction in pedAML using lncRNA expression. We built a relapse–related lncRNA prognostic signature, named AMLlnc69, using 871 transcriptomes of patients with pedAML obtained from the Therapeutically Applicable Research to Generate Effective Treatments repository. We identified a 69 lncRNA signature AMLlnc69 that is highly predictive of relapse risk (c-index = 0.73), with area under the receiver operating characteristic curve (AUC) values for predicting the 1-, 2-, and 3-year relapse-free survival (RFS) of 0.78, 0.77, and 0.77, respectively. The internal validation using a bootstrap method (resampling times = 1000) resulted in a c-index of 0.72 and AUC values for predicting the 1-, 2-, and 3-year RFS of 0.77, 0.76, and 0.76, respectively. Through a Cox regression analysis, AMLlnc69, nucleophosmin mutation, and white blood cell at diagnosis were identified as independent predictors of RFS. Finally, a nomogram was build using these 2 parameters, showing a c-index of 0.80 and 0.71 after bootstrapping (n = 1000). In conclusion, the identified AMLlnc69 will, after prospective validation, add important information to guide the management of patients with pedAML. The nomogram is a promising tool for easy stratification of patients into a novel scheme of relapse-risk groups.

Long noncoding RNA UNC5B-AS1 suppresses cell proliferation by sponging miR-24-3p in glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is the most common primary CNS tumor, characterized by high mortality and heterogeneity. However, the related lncRNA signatures and their target microRNA (miRNA) for GBM are still mostly unknown. Therefore, it is critical that we discover lncRNA markers in GBM and their biological activities.Materials and methodsGBM-related RNA-seq data were obtained from the Cancer Genome Atlas (TCGA) database. The “edger” R package was used for differently expressed lncRNAs (DELs) identification. Then, we forecasted prospective miRNAs that might bind to lncRNAs by Cytoscape software. Survival analysis of those miRNAs was examined by the starBase database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the miRNAs’ target genes was conducted by the Gene Set Enrichment Analysis (GSEA) database and R software. Moreover, the proliferative ability of unc-5 netrin receptor B antisense RNA 1 (UNC5B-AS1) cells was evaluated by Cell Counting Kit-8 (CCK-8) analysis. Mechanistically, the regulatory interaction between UNC5B-AS1 and miRNA in GBM biological processes was studied using CCK-8 analysis.ResultsOur results indicated that overexpression of UNC5B-AS1 has been shown to suppress GBM cell growth. Mechanistically, miR-24-3p in GBM was able to alleviate the anti-oncogenic effects of UNC5B-AS1 on cell proliferation.ConclusionThe discovery of the novel UNC5B-AS1-miR-24-3p network suggests possible lncRNA and miRNA roles in the development of GBM, which may have significant ramifications for the analysis of clinical prognosis and the development of GBM medications.