Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma

Comprehensive review of Bayesian network applications in gastrointestinal cancers

Gastrointestinal cancers, including esophageal, gastric, colorectal, liver, gallbladder, cholangiocarcinoma, and pancreatic cancers, pose a significant global health challenge due to their high mortality rates and poor prognosis, particularly when diagnosed at advanced stages. These malignancies, characterized by diverse clinical presentations and etiologies, require innovative approaches for improved management. Bayesian networks (BN) have emerged as a powerful tool in this field, offering the ability to manage uncertainty, integrate heterogeneous data sources, and support clinical decision-making. This review explores the application of BN in addressing critical challenges in gastrointestinal cancers, including the identification of risk factors, early detection, treatment optimization, and prognosis prediction. By integrating genetic predispositions, lifestyle factors, and clinical data, BN hold the potential to enhance survival rates and improve quality of life through personalized treatment strategies. Despite their promise, the widespread adoption of BN is hindered by challenges such as data quality limitations, computational complexities, and the need for greater clinical acceptance. The review concludes with future research directions, emphasizing the development of advanced BN algorithms, the integration of multi-omics data, and strategies to ensure clinical applicability, aiming to fully realize the potential of BN in personalized medicine for gastrointestinal cancers.

Development of a PANoptosis-related LncRNAs for prognosis predicting and immune infiltration characterization of gastric Cancer

PANoptosis is a newly discovered form of programmed cell death (PCD), involving the interaction of cellular pyroptosis, apoptosis, and necroptosis. Although PANoptosis plays a significant role in carcinogenesis process, the impact of PANoptosis-related lncRNAs (PANlncRNAs) on the prognostic value and mechanism of immune infiltration of gastric cancer have not been studied. All information of gastric cancer (GC) patients was downloaded from the TCGA database. PANoptosis-related genes were obtained from molecular characteristic databases, and PANlncRNAs were screened through Pearson correlation analysis. Based on this, PANlncRNAs were subjected to univariate Cox regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm to obtain lncRNA associated with survival outcomes, which were subsequently used to calculate survival scores and to construct signatures. Through further analysis of clinical subgroups, immune infiltration, drug sensitivity analysis, tumor mutation burden testing, and GSEA enrichment pathway analysis, their clinical significance was comprehensively analyzed. This study constructed a prognosis model for gastric cancer based on 8 PANlncRNAs and validated its prognostic value. The study showed that the survival time and outcome of the high-risk subgroup was significantly worse than that of the low-risk subgroup. The bar graph showed satisfactory predictive results, and the calibration curve showed good consistency between the prognostic model and actual prognostic outcomes. TIDE and drug sensitivity analysis showed significant differences between high and low-risk subgroups. The prognosis model based on PANlncRNAs has important implications for the judgment and precision treatment of gastric cancer.

Glycolysis-associated lncRNAs in cancer energy metabolism and immune microenvironment: a magic key

The dependence of tumor cells on glycolysis provides essential energy and raw materials for their survival and growth. Recent research findings have indicated that long chain non-coding RNAs (LncRNAs) have a key regulatory function in the tumor glycolytic pathway and offer new opportunities for cancer therapy. LncRNAs are analogous to a regulatory key during glycolysis. In this paper, we review the mechanisms of LncRNA in the tumor glycolytic pathway and their potential therapeutic strategies, including current alterations in cancer-related energy metabolism with lncRNA mediating the expression of key enzymes, lactate production and transport, and the mechanism of interaction with transcription factors, miRNAs, and other molecules. Studies targeting LncRNA-regulated tumor glycolytic pathways also offer the possibility of developing new therapeutic strategies. By regulating LncRNA expression, the metabolic pathways of tumor cells can be interfered with to inhibit tumor growth and metastasis, thus affecting the immune and drug resistance mechanisms of tumor cells. In addition, lncRNAs have the capacity to function as molecular markers and target therapies, thereby contributing novel strategies and approaches to the field of personalized cancer therapy and prognosis evaluation. In conclusion, LncRNA, as key molecules regulating the tumor glycolysis pathway, reveals a new mechanism of abnormal metabolism in cancer cells. Future research will more thoroughly investigate the specific mechanisms of LncRNA glycolysis regulation and develop corresponding therapeutic strategies, thereby fostering new optimism for the realization of precision medicine.

Emerging roles of long noncoding RNAs in enzymes related intracellular metabolic pathways in cancer biology

Metabolic reprogramming plays critical roles in the development and progression of tumor by providing cancer cells with a sufficient supply of nutrients and other factors needed for fast-proliferating. Emerging evidence indicates that long noncoding RNAs (lncRNAs) are involved in the initiation of metastasis via regulating the metabolic reprogramming in various cancers. In this paper, we aim to summarize that lncRNAs could participate in intracellular nutrient metabolism including glucose, amino acid, lipid, and nucleotide, regardless of whether lncRNAs have tumor-promoting or tumor-suppressor function. Meanwhile, modulation of lncRNAs in glucose metabolic enzymes in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle (TCA) in cancer is reviewed. We also discuss therapeutic strategies targeted at interfering with enzyme activity to decrease the utilization of glucoses, amino acid, nucleotide acid and lipid in tumor cells. This review focuses on our current understanding of lncRNAs participating in cancer cell metabolic reprogramming, paving the way for further investigation into the combination of such approaches with existing anti-cancer therapies.

Comprehensive analysis of lncRNA-associated ceRNA network reveals novel potential prognostic regulatory axes in glioblastoma multiforme

Deciphering the lncRNA-associated competitive endogenous RNA (ceRNA) network is essential in decoding glioblastoma multiforme (GBM) pathogenesis by regulating miRNA availability and controlling mRNA stability. This study aimed to explore novel biomarkers for GBM by constructing a lncRNA-miRNA-mRNA network. A ceRNA network in GBM was constructed using lncRNA, mRNA and miRNA expression profiles from the TCGA and GEO datasets. Seed nodes were identified by protein-protein interaction (PPI) network analysis of deregulated-mRNAs (DEmRNAs) in the ceRNA network. A lncRNA-miRNA-seed network was constructed by mapping the seed nodes into the preliminary ceRNA network. The impact of the seed nodes on the overall survival (OS) of patients was assessed by the GSCA database. Functional enrichment analysis of the deregulated-lncRNAs (DElncRNA) in the ceRNA network and genes interacting with OS-related genes in the PPI network were performed. Finally, the positive correlation between seed nodes and their associated lncRNAs and the expression level of these molecules in GBM tissue compared with normal samples was validated using the GEPIA database. Our analyzes revealed that three novel regulatory axes AL161785.1/miR-139-5p/MS4A6A, LINC02611/miR-139-5p/MS4A6A and PCED1B-AS1/miR-433-3p/MS4A6A may play essential roles in GBM pathogenesis. MS4A6A is upregulated in GBM and closely associated with shorter survival time of patients. We also identified that MS4A6A expression positively correlates with genes related to tumour-associated macrophages, which induce macrophage infiltration and immune suppression. The functional enrichment analysis demonstrated that DElncRNAs are mainly involved in neuroactive ligand-receptor interaction, calcium/MAPK signalling pathway, ribosome, GABAergic/Serotonergic/Glutamatergic synapse and immune system process. In addition, genes related to MS4A6A contribute to immune and inflammatory-related biological processes. Our findings provide novel insights to understand the ceRNA regulation in GBM and identify novel prognostic biomarkers or therapeutic targets.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Analysis of m6A-related lncRNAs for prognostic and immunotherapeutic response in hepatocellular carcinoma

Background: RNA methylation modifications are important post-translational modifications that are regulated in an epigenetic manner. Recently, N6-methyladenosine (m6A) RNA modifications have emerged as potential epigenetic markers in tumor biology. Methods: Gene expression and clinicopathological data of LIHC were obtained from the cancer genome atlas (TCGA) database. The relationship between long non-coding RNAs (lncRNAs) and m6A-related genes was determined by gene expression analysis using Perl and R software. Co-expression network of m6A-lncRNA was constructed, and the relevant lncRNAs associated with prognosis were identified using univariate Cox regression analysis. These lncRNAs were then divided into two clusters (cluster 1 and cluster 2) to determine the differences in survival, pathoclinical parameters, and immune cell infiltration between the different lncRNA subtypes. The least absolute shrinkage and selection operator (LASSO) was carried out for regression analysis and prognostic model. The HCC patients were randomly divided into a train group and a test group. According to the median risk score of the model, HCC patients were divided into high-risk and low-risk groups. We built models using the train group and confirmed them through the test group. The m6A-lncRNAs derived from the models were analyzed for the tumor mutational burden (TMB), immune evasion and immune function using R software. AL355574.1 was identified as an important m6A-associated lncRNA and selected for further investigation. Finally, in vitro experiments were conducted to confirm the effect of AL355574.1 on the biological function of HCC and the possible biological mechanisms. Huh7 and HepG2 cells were transfected with AL355574.1 siRNA and cell proliferation ability was measured by CCK-8, EdU and colony formation assays. Wound healing and transwell assays were used to determine the cell migration capacity. The expression levels of MMP-2, MMP-9, E-cadherin, N-cadherin and Akt/mTOR phosphorylation were all determined by Western blotting. Results: The lncRNAs with significant prognostic value were classified into two subtypes by a consistent clustering analysis. We found that the clinical features, immune cell infiltration and tumor microenvironment (TME) were significantly different between the lncRNA subtypes. Our analysis revealed significant correlations between these different lncRNA subtypes and immune infiltrating and stromal cells. We created the final risk profile using LASSO regression, which notably included three lncRNAs (AL355574.1, AL158166.1, TMCC1-AS1). A prognostic signature consisting of the three lncRNAs was constructed, and the model showed excellent prognostic predictive ability. The overall survival (OS) of the low-risk cohort was significantly higher than that of the high-risk cohort in both the train and test group. Both risk score [hazard ratio (HR)=1.062; P<0.001] and stage (HR=1.647; P< 0.001) were considered independent indicators of HCC prognosis by univariate and multivariate Cox regression analysis. In Huh7 and HepG2 cells, AL355574.1 knockdown inhibited cell proliferation and migration, suppressed the protein expression levels of MMP-2, MMP-9, N-cadherin and Akt/mTOR phosphorylation, but promoted the protein expression levels of E-cadherin. Conclusions: This study established a predictive model for the OS of HCC patients, and these OS-related m6A-lncRNAs, especially AL355574.1 may play a potential role in the progression of HCC. In vitro experiments also showed that AL355574.1 could enhance the expression of MMPs and EMT through the Akt/mTOR signaling pathway, thereby affected the proliferation and migration of HCC. This provides a new perspective on the anticancer molecular mechanism of AL355574.1 in HCC.

Comprehensive Analysis of Disulfidptosis-Related LncRNAs in Molecular Classification, Immune Microenvironment Characterization and Prognosis of Gastric Cancer

BACKGROUND

Disulfidptosis is a novel form of programmed cell death that unveils promising avenues for the exploration of tumor treatment modalities. Gastric cancer (GC) is a malignant tumor characterized by high incidence and mortality rate. However, there has been no systematic study of disulfidptosis-related long noncoding RNAs (DRLs) signature in GC patients.

METHODS

The lncRNA expression profiles containing 412 GC samples were acquired from the Cancer Genome Atlas (TCGA) database. Differential expression analysis was performed alongside Pearson correlation analysis to identify DRLs. Prognostically significant DRLs were further screened using univariate COX regression analysis. Subsequently, LASSO regression and multifactorial COX regression analyses were employed to establish a risk signature composed of DRLs that exhibit independent prognostic significance. The predictive value of this risk signature was further validated in a test cohort. The ESTIMATE, CIBERSORT and ssGSEA methodologies were utilized to investigate the tumor immune microenvironment of GC populations with different DRLs profiles. Finally, the correlation between DRLs and various GC drug responses was explored.

RESULTS

We established a prognostic signature comprising 12 disulfidptosis-related lncRNAs (AC110491.1, AL355574.1, RHPN1-AS1, AOAH-IT1, AP001065.3, MEF2C-AS1, AC016394.2, LINC00705, LINC01952, PART1, TNFRSF10A-AS1, LINC01537). The Kaplan-Meier survival analysis revealed that patients in the high-risk group exhibited a poor prognosis. Both univariate and multivariate COX regression models demonstrated that the DRLs signature was an independent prognostic indicator in GC patients. Furthermore, the signature exhibited accurate predictions of survival at 1-, 3- and 5- years with the area under the curve (AUC) values of 0.708, 0.689 and 0.854, respectively. In addition, we also observed significant associations between the DRLs signature and various clinical variables, distinct immune landscape and drug sensitivity profiles in GC patients. The low-risk group patients may be more likely to benefit from immunotherapy and chemotherapy.

CONCLUSIONS

Our study investigated the role and potential clinical implications of DRLs in GC. The risk model constructed by DRLs demonstrated high accuracy in predicting the survival outcomes of GC and improving the treatment efficacy for GC patients.

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Gastric Cancer

Gastric cancer (GC) is a malignant tumor with a low survival rate, high recurrence rate, and poor prognosis. With respect to this, pyroptosis is a type of programmed cell death that can affect the occurrence and development of tumors. Indeed, long non-coding RNAs (lncRNAs) were broadly applied for the purposes of early diagnosis, treatment, and prognostic analysis in regard to cancer. Based on the association of these three purposes, we developed a novel prognosis risk model based on pyroptosis-related lncRNAs (PRlncRNAs) for GC. The PRlncRNAs were obtained via univariate and multivariate Cox regression in order to build the predictive signatures. The Kaplan–Meier and gene set enrichment analysis (GSEA) methods were used to evaluate the overall survival (OS) and functional differences between the high- and low-risk groups. Moreover, the correlation of the signatures with immune cell infiltration was determined through single-sample gene set enrichment analysis (ssGSEA). Finally, we analyzed this correlation with the treatment responses in the GC patients; then, we performed quantitative reverse transcription polymerase chain reactions (qRT-PCRs) in order to verify the risk model. The high-risk group received a worse performance in terms of prognosis and OS when compared to the low-risk group. With respect to this, the area under the receiver operating characteristic curve (ROC) was found to be 0.808. Through conducting the GSEA, it was found that the high-risk groups possessed a significant enrichment in terms of tumor–immunity pathways. Furthermore, the ssGSEA revealed that the predictive features possessed strong associations with immune cell infiltration in regard to GC. In addition, we highlighted that anti-immune checkpoint therapy, combined with conventional chemotherapy drugs, may be more suitable for high-risk patients. The expression levels of LINC01315, AP003392.1, AP000695.2, and HAGLR were significantly different between the GC cell lines and the normal cell lines. As such, the six PRlncRNAs could be regarded as important prognostic biomarkers for the purposes of subsequent diagnoses, treatments, prognostic predictions, and the mechanism research of GC.