A potential prognostic prediction model of colon adenocarcinoma with recurrence based on prognostic lncRNA signatures

Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma

BACKGROUND

Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients.

METHODS

RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs.

RESULTS

A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined.

CONCLUSIONS

A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

LINC00174 Promotes Colon Cancer Progression by Regulating Inflammation and Glycolysis by Targeting the MicroRNA-2467-3p/Enolase 3 Axis

Objective

To elucidate the mechanism by which LINC00174 promotes colon cancer progression by targeting the microRNA-2467-3p (miR-2467-3p)/enolase 3 (ENO3) axis to regulate inflammation and glycolysis.

Methods

The expression of LINC00174 and ENO3 in colon cancer tissues, its relationship with survival rate, and correlation were analyzed using bioinformatic analysis. The effects of LINC00174 overexpression and silencing on the biological behavior of and inflammation in colon cancer cells were analyzed via transfection experiments. The target relationships between miR-2467-3p or LINC00174 and ENO3 were verified using sequence prediction and the dual-luciferase reporter assay, respectively. Furthermore, LINC00174- and/or miR-2467-3p-overexpressing cells were prepared to determine the effects on ENO3 protein levels and glycolysis. Finally, the effects of LINC00174 and/or miR-2467-3p overexpression on colon cancer, ENO3 protein levels, and inflammation were analyzed using a tumor-bearing mice model.

Results

LINC00174 and ENO3 were overexpressed and associated with a lower survival rate. LINC00174 was positively correlated with ENO3 in colon cancer tissues. Furthermore, the overexpression of LINC00174 in colon cancer cell lines promoted the proliferation, migration, and invasion of colon cancer cells and inflammation but inhibited apoptosis. The overexpression of miR-2467-3p inhibited ENO3 protein levels, which was attenuated via LINC00174 overexpression. Furthermore, it inhibited the biological behavior of and inflammation and glycolysis in colon cancer cells and blocked their LINC00174-induced promotion. Moreover, using animal experiments, the regulatory effects of LINC00174 on tumor growth, ENO3 protein levels, and inflammation via miR-2467-3p were confirmed.

Conclusion

LINC00174 promotes the glycolysis, inflammation, proliferation, migration, and invasion of colon cancer cells and inhibits apoptosis. The cancer-promoting mechanism of LINC00174 is related to targeting miR-2467-3p to promote ENO3 protein levels.

Comprehensive analyses of N6 -methyladenosine-related long noncoding RNA profiles with prognosis, chemotherapy response, and immune landscape in small cell lung cancer

Small cell lung cancer (SCLC) is the most devastating subtype of lung cancer with no clinically available prognostic biomarkers. N⁶ -methyladenosine (m⁶ A) and noncoding RNAs play critical roles in cancer development and treatment response. However, little is known about m⁶ A-related long noncoding RNAs (lncRNAs) in SCLC. We used 206 limited-stage SCLC (LS-SCLC) samples from two cohorts to undertake the first and most comprehensive exploration of the m⁶ A-related lncRNA profile in SCLC and constructed a relevant prognostic signature. In total, 289 m⁶ A-related lncRNAs were screened out. We then built a seven-lncRNA-based signature in the training cohort with 48 RNA sequencing data using univariate and multivariate Cox regression models. The signature was well validated in an independent cohort containing 158 cases with quantitative PCR data. In both cohorts, the signature divided patients into high- and low-risk groups with significantly different survival rates (both p < 0.001). Our signature predicted chemotherapy survival benefit in patients with LS-SCLC. Receiver operating characteristic and C-index analyses indicated that the signature was better at predicting prognosis and chemotherapy benefit than other clinicopathologic features. Moreover, the signature was identified as an independent predictor of prognosis and chemotherapy response in different cohorts. Furthermore, functional analysis showed that multiple activated immune-related pathways were enriched in the low-risk group. Additionally, the signature was also closely related to various immune checkpoints and inflammatory responses. We generated the first clinically available m⁶ A-related lncRNA signature to predict prognosis and chemotherapy benefit in patients with LS-SCLC. Our findings could help optimize the clinical management of patients with LS-SCLC and inform future therapeutic targets for SCLC.

Identification of cuproptosis-related long non-coding ribonucleic acid signature as a novel prognosis model for colon cancer

Cuproptosis is a novel type of cell death that may play a vital role in preventing various types of cancer. Studies examining cuproptosis are limited, and the cuproptosis-related lncRNAs (long non-Coding ribonucleic acids) involved in the regulation of colon cancer remain unclear. This study aimed to identify the prognostic signature of cupronosis-related lncRNAs and explore their potential molecular functions in colon cancer. Data on the clinical correlation were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed cuproptosis-related long non-coding ribonucleic acids (lncRNAs) were analyzed using the "limma" package. Then, the prognostic cuproptosis-related lncRNA signature (CupRLSig) was identified through univariate Cox and co-expression analyses, and a prognostic model was constructed based on CupRLSig using the least absolute shrinkage selection operator (LASSO) algorithm and Cox regression analysis. The Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used for evaluating the model's capacity for prognosis prediction. In addition, the immune landscape, and drug sensitivity of CupRLSig were analyzed. Finally, the functions of AL512306.3 and ZEB1-AS1 were verified through in vitro experiments. The high- or low-risk groups were classified according to the risk score. The signature-based risk score showed a stronger ability to predict patient's survival compared with the traditional clinicopathological features. In addition, immune responses, such as inflammation-promoting response and T-cell co-inhibition, were significantly different between the two groups. Moreover, chemotherapy drugs or inhibitors, such as axitinib, cisplatin, doxorubicin, and elesclomol, may be considered as potential therapeutic drugs for patients in high-risk groups. Finally, inhibition of AL512306.3 and ZEB1-AS1 significantly suppressed the cell proliferation in colon cancer cells. These results provide novel insights into the pathogenesis of colon cancer and offer promising biomarkers with the potential to guide research on carcinogenesis and cancer treatment.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

Weighted correlation network analysis revealed novel long non-coding RNAs for colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, which after breast, lung and, prostate cancers, is the fourth prevalent cancer in the United States. Long non-coding RNAs (lncRNAs) have an essential role in the pathogenesis of CRC. Therefore, bioinformatics studies on lncRNAs and their target genes have potential importance as novel biomarkers. In the current study, publicly available microarray gene expression data of colorectal cancer (GSE106582) was analyzed with the Limma, Geoquery, Biobase package. Afterward, identified differentially expressed lncRNAs and their target genes were inserted into Weighted correlation network analysis (WGCNA) to obtain modules and hub genes. A total of nine differentially expressed lncRNAs (LINC01018, ITCH-IT, ITPK1-AS1, FOXP1-IT1, FAM238B, PAXIP1-AS1, ATP2B1-AS1, MIR29B2CHG, and SNHG32) were identified using microarray data analysis. The WGCNA has identified several hub genes for black (LMOD3, CDKN2AIPNL, EXO5, ZNF69, BMS1P5, METTL21A, IL17RD, MIGA1, CEP19, FKBP14), blue (CLCA1, GUCA2A, UGT2B17, DSC2, CA1, AQP8, ITLN1, BEST4, KLF4, IQCF6) and turquoise (PAFAH1B1, LMNB1, CACYBP, GLO1, PUM3, POC1A, ASF1B, SDCCAG3, ASNS, PDCD2L) modules. The findings of the current study will help to improve our understanding of CRC. Moreover, the hub genes that we have identified could be considered as possible prognostic/diagnostic biomarkers. This study led to the determination of nine lncRNAs with no previous association with CRC development.

Genome Instability and Long Noncoding RNA Reveal Biomarkers for Immunotherapy and Prognosis and Novel Competing Endogenous RNA Mechanism in Colon Adenocarcinoma

Background: Long noncoding RNAs (lncRNAs) crucially modulate DNA damage responses/repair in cancer cells. However, the underlying regulatory role of genome integrity and its clinical value in colon adenocarcinoma (COAD) remains unclear. This study links genome instability to lncRNA using computational biology techniques, in attempt to propose novel biomarkers of immunotherapy outcome, and investigated a potential competing endogenous RNA (ceRNA) as a molecular regulatory mechanism.

Methods: TCGA-COAD patients were divided into genome unstable (GU)-like and genome stable (GS)-like clusters via hierarchical clustering to predict immunotherapy outcomes. Multivariate Cox model was established to predict the overall survival rate in COAD patients. Additionally, SVM and LASSO algorithms were applied to obtain hub lncRNAs. A novel genome instability-related ceRNA network was predicted with the Starbase 2.0 database. To better understand how these genes fundamentally interact during tumor progression and development, the mutation analysis and single-gene analysis for each gene was performed.

Results: In contrast to those in the GS-like cluster, GU-like-cluster patients demonstrated a higher tumor mutational burden (TMB)/microsatellite instability (MSI), DNA polymerase epsilon (POLE) mutation rate, and immune checkpoint expression, all indicate a greater predictive power for response rate for immunotherapy. The novel prognostic signature demonstrated an outstanding predictive performance (AUC > 0.70). The genes in the genome insatiability-related ceRNA network (including four axes: AL161772.1-has-miR-671-5p (hsa-miR-181d-5p, has-miR-106a-5p)-NINL, AL161772.1-has-miR-106a-5p-TNFSF11, AC124067.4-hsa-miR-92b-3p (hsa-miR-589-5p)-PHYHIPL, and BOLA3-AS1-has-miR-130b-3p-SALL4) were identified as critical regulators of tumor microenvironment infiltration, cancer stemness, and drug resistance. qPCR was performed to validate the expression patterns of these genes. Furthermore, the MSI-high proportion was greater in patients with mutated type than in those with the wild type according to all four target genes, indicating that these four genes modulate genomic integrity and could serve as novel immunotherapy biomarkers.

Conclusion: We demonstrated that genome instability-related lncRNA is a novel biomarker for immunotherapy outcomes and prognosis. A novel ceRNA network that modulates genomic integrity, including four lncRNA-miRNA-mRNA axes, was proposed.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma

AIM

To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis.

METHODS

UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package 'edgeR'. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM.

RESULTS

A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib.

CONCLUSION

The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

The COX10-AS1/miR-641/E2F6 Feedback Loop Is Involved in the Progression of Glioma

Glioma is the most common primary tumour of the central nervous system and is considered one of the greatest challenges for neurosurgery. Mounting evidence has shown that lncRNAs participate in various biological processes of tumours, including glioma. This study aimed to reveal the role and relevant mechanism of COX10-AS1 in glioma. The expression of COX10-AS1, miR-641 and E2F6 was measured by qRT-PCR and/or western blot. Clone formation assays, EdU assays, Transwell assays and tumour xenograft experiments were performed to evaluate the effects of COX10-AS1, miR-641 and E2F6 on glioma proliferation, migration and invasion. Luciferase reporter assays, RNA pull-down assays and ChIP assays were conducted to analyse the relationship among COX10-AS1, miR-641 and E2F6. We demonstrated that COX10-AS1 was upregulated in glioma tissues and cell lines, which was related to the grade of glioma and patient survival. Next, through functional assays, we found that COX10-AS1 influenced the proliferation, migration and invasion of glioma cell lines. Then, with the help of bioinformatics analysis, we confirmed that COX10-AS1 regulated glioma progress by acting as a sponge of miR-641 to regulate E2F6. Moreover, further study indicated that E2F6 could promote COX10-AS1 expression by binding to its promoter region. Taken together, the data indicated that COX10-AS1 acts as an oncogene in combination with COX10-AS1/miR-641/E2F6 in glioma, which may be beneficial to the diagnosis and treatment of glioma.

Systematic analysis of the function and prognostic value of RNA binding proteins in Colon Adenocarcinoma

Background: Abnormal expression of RNA-binding proteins (RBPs) is closely related to tumorigenesis, progression, and prognosis. This study performed systematic bioinformatic analysis of RBPs abnormally expressed in colon adenocarcinoma (COAD) using the Cancer Genome Atlas (TCGA) database to screen prognostic markers and potential therapeutic targets.

Methods: First, the gene expression data from COAD samples were used to screen out differentially expressed RBPs for functional enrichment analysis and to visualize interaction relationships. Second, RBPs that were significantly related to prognosis were screened through univariate and multivariate Cox regression analysis to construct a prognostic model. The prediction performance of the prognostic model was evaluated by survival analysis and receiver operating characteristic (ROC) curve analysis. It addition, it was verified in the test cohort. The Human Protein Atlas (HPA) online database was used to verify the expression levels of RBPs in the prognostic model.

Results: The study identified 181 differentially expressed RBPs and analyzed their interaction and functional enrichment, which were mainly related to non-coding RNA processing, ribosome biogenesis, RNA metabolic processes, RNA phosphodiester bond hydrolysis, and alternative mRNA splicing. Five RBPs related to prognosis were used to construct a prognostic model, and its predictive ability was verified by the test cohort. ROC curve analysis showed that the prognostic model had good sensitivity and specificity. Independent prognostic analysis showed that risk scores could be used as independent prognostic factors for COAD.

Conclusion: This study constructed a reliable prognostic model by analyzing COAD differentially expressed RBPs, facilitating the screening of COAD prognostic markers and therapeutic targets.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.