A Prognostic Pyroptosis-Related lncRNAs Risk Model Correlates With the Immune Microenvironment in Colon Adenocarcinoma

Study on the Characteristics of Coarse Feeding Tolerance of Ding'an Pigs: Phenotypic and Candidate Genes Identification

Ding'an (DA) pig, a prominent local breed in Hainan Province, exhibits notable advantages in coarse feeding tolerance and high-quality meat. To explore the potential genetic mechanism of coarse feeding tolerance in DA pigs, 60-day-old full sibling pairs of DA and DLY (Duroc-Landrace-Yorkshire) pigs were subjected to fed normal (5%) and high (10%) crude fiber diets for 56 days, respectively. The findings showed that increasing the crude fiber level had no impact on the apparent digestibility of crude fiber, intramuscular fat, and marbling scores in DA pigs, whereas these factors were significantly reduced in DLY pigs (p < 0.05). Through differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) of the colonic mucosal transcriptome data, 65 and 482 candidate genes with coarse feeding tolerance in DA pigs were identified, respectively. Joint analysis screened four key candidate genes, including LDHB, MLC1, LSG1, and ESM1, potentially serving as key regulated genes for coarse feeding tolerance. Functional analysis revealed that the most significant pathway enriched in differential genes associated with coarse feeding tolerance in Ding'an pigs was the signaling receptor binding. The results hold substantial significance for advancing our understanding of the genetic mechanisms governing coarse feeding tolerance in Ding'an pigs.

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

The implication of pyroptosis in cancer immunology: Current advances and prospects

Pyroptosis is a regulated cell death pathway involved in numerous human diseases, especially malignant tumors. Recent studies have identified multiple pyroptosis-associated signaling molecules, like caspases, gasdermin family and inflammasomes. In addition, increasing in vitro and in vivo studies have shown the significant linkage between pyroptosis and immune regulation of cancers. Pyroptosis-associated biomarkers regulate the infiltration of tumor immune cells, such as CD4+ and CD8+ T cells, thus strengthening the sensitivity to therapeutic strategies. In this review, we explained the relationship between pyroptosis and cancer immunology and focused on the significance of pyroptosis in immune regulation. We also proposed the future application of pyroptosis-associated biomarkers in basic research and clinical practices to address malignant behaviors. Exploration of the underlying mechanisms and biological functions of pyroptosis is critical for immune response and cancer immunotherapy.

Down-regulation of OIP5-AS1 inhibits obesity-induced myocardial pyroptosis and miR-22/NLRP3 inflammasome axis

BACKGROUND

Obesity can induce myocardial pyroptosis, but the exact mechanism is still unknown. A recent study reported the association of opa-interacting protein 5-antisense transcript 1 (OIP5-AS1), an evolutionarily conserved long noncoding RNA, with pyroptosis. Therefore, this study aimed to investigate the role of OIP5-AS1 in obesity-induced myocardial pyroptosis.

METHODS

OIP5-AS1 was downregulated in H9c2 cells, followed by treatment with 400 μM palmitic acid (PA). Propidium iodide (PI) staining, lactic dehydrogenase (LDH) release assay, caspase-1 activity assay, IL-1β, and IL-18 activity assay were performed to detect pyroptotic phenotype. The interaction between OIP5-AS1 and microRNAs (miRNAs) was analyzed using RNA pull-down and luciferase assay. The effect of OIP5-AS1 knockdown in high-fat diet (HFD)-induced obesity rat on cardiac function, myocardial hypertrophy, fibrosis, and remodeling was evaluated.

RESULTS

Fat deposition was observed in cardiomyocytes 24 h after PA treatment; moreover, PA-treated cardiomyocytes showed significant increase in the rate of pyroptotic cells, release of LDH, protein expressions of NLRP3 and cleaved caspase-1, and the activity of caspase-1, IL-1β, and IL-18 as well as OIP5-AS1 expression. These findings suggested that PA activated pyroptosis and induced OIP5-AS1 expression in cardiomyocytes. Moreover, OIP5-AS1 knockdown inhibited PA-induced pyroptosis. Mechanistically, OIP5-AS1 was found to specifically bind to miR-22 and to regulate NLRP3 inflammasome-mediated pyroptosis via miR-22. Furthermore, OIP5-AS1 knockdown ameliorated HFD-induced cardiac dysfunction, myocardial hypertrophy, fibrosis, remodeling, and pyroptosis.

CONCLUSION

Our results revealed that downregulation of OIP5-AS1 can inhibit obesity-induced myocardial pyroptosis via miR-22/NLRP3 inflammasome axis. This finding lays a foundation of gene therapy for heart disease targeting OIP5-AS1.

Analysis on EZH2: mechanism identification of related CeRNA and its immunoassay in hepatocellular carcinoma

OBJECTIVE

To screen the possible potential signaling pathways related to enhancer of zeste homolog 2 (EZH2) based on ceRNA mechanism, and to analyze the correlation between E2H2 and depths of various immune cell infiltration depths. The relationship between different immune checkpoints were also analyzed.

METHODS

First, the expression of EZH2 in pan-cancer (18 malignancies) was analyzed with the TCGA database. Hepatocellular carcinoma (HCC) tissues of 374 cases and normal tissues of 50 cases were analyzed in terms of the differential expression, overall survival (OS) and progression-free-survival (PFS). Then, we conducted GO and KEGG enrichment analysis on target gene. We also analyzed mRNA-miRNA and MicroRNA (miRNA)- long non-coding RNA (lncRNA) correlation with starbase databse, so as to determine the potential ceRNA mechanism associated with EZH2. Finally, immunoassay and drug-sensitivity analysis of EZH2 was performed.

RESULTS

Seven potential EZH2-related ceRNA pathways were screened out, namely lncRNA: Small Nucleolar RNA Host Gene 1 (SNHG1), SNHG 3, and SNHG 6-miR-101-3p-EZH2; and lncRNA: Long Intergenic Non-Protein Coding RNA 1978 (LINC01978), SNHG12, Ring Finger Protein 216 Pseudogene 1 (RNF216P1), and Coiled-coil Domain Containing 18 Antisense RNA 1 (CCDC18-AS1)-let-7c-5p-EZH2. Finally, 4 potential EZH2-related ceRNA pathways were identified through qPCR.According to immune correlation analysis, EZH2 may be positively correlated with T cells follicular helper, T cells Cluster of differentiation (CD)4 memory activated, Macrophages M0, and B cells memory (P < 0.05, cof > 0.2); while be negatively correlated with T cells CD4 + memory resting (P < 0.05, cof < -0.2). And EZH2 is positively correlated with Programmed Cell Death 1 (PDCD1) (R = 0.22), CD274 (R = 0.3) and Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4) (R = 0.23). According to drug sensitivity analysis, patients in the high expression group were more susceptible to the effects of various drugs including Sorafenib, 5-Fluorouracil, Doxorubicin, Etoposide, Paclitaxel, and Vinorelbine than those with low expression.

CONCLUSION

This study revealed seven potential pathways of Enhancer of Zeste Homolog 2 (EZH2)-related ceRNA mechanisms: lncRNA (SNHG3, 6) -Mir-101-3P-ezh2; lncRNA (SNHG12, RNF216P1)-let-7c-5p-EZH2. We also analyzed the immunity and drug sensitivity of EZH2. Our study proves that EZH2 still has great research prospects in HCC.

A Prognostic Model of Angiogenesis and Neutrophil Extracellular Traps Related Genes Manipulating Tumor Microenvironment in Colon Cancer

Colon adenocarcinoma (COAD) is one of the most common carcinomas worldwide. The main causes of cancer-related mortality of COAD are metastases. The fundamental processes for how angiogenesis and neutrophil extracellular traps (NETs) contributing to tumor progression and metastasis are still uncertain. In our study, The Cancer Genome Atlas (TCGA)-COAD dataset (train set) and GSE17536 (test set) were analyzed. Angiogenesis potential index (API) and NETs potential index (NPI) based on angiogenesis and NETs-related genes were respectively built using bioinformatic methods and machine learning algorithms. Subjects were split into groups with low API/NPI or high API/NPI. Survival analysis showed the high API and high NPI patients with the worst survival compared with the others. Between the high API/NPI group and the other groups, differentially expressed genes (DEGs) were found. A four-gene signature (TIMP1, FSL3, CALB2, and FABP4) was included in a risk model based on least absolute shrinkage and selection operator (LASSO) analysis. Additionally, the model displayed a significant association with many immune microenvironment characteristics. Finally, we verified the clinical significance of CALB2 expression and its role to promote the invasion and migration of colon cancer cells in vitro.

The Pyroptotic and Nonpyroptotic Roles of Gasdermins in Modulating Cancer Progression and Their Perspectives on Cancer Therapeutics

Gasdermins (GSDMs) are a protein family encoded by six paralogous genes in humans, including GSDMA, GSDMB, GSDMC, GSDMD, GSDME (also known as DFNA5), and DFNB59 (also known as pejvakin). Structurally, members of the GSDM family possess a C-terminus (an autoinhibitory domain) and a positively charged N-terminus (a pore-forming domain) linked with divergent peptide linkers. Recently, GSDMs have been identified as key executors of pyroptosis (an immunogenic programmed cell death) due to their pore-forming activities on the plasma membrane when proteolytically cleaved by caspases or serine proteases. Accumulating studies suggest that chemoresistance is attributed to dysregulation of apoptotic machinery and that inducing pyroptosis to bypass aberrant apoptosis can potently resensitize apoptosis-resistant cancer to chemotherapeutics. Pyroptosis is initiated by pore formation and culminates with plasma membrane rupture; these processes enable the release of proinflammatory cytokines (e.g., IL-1β and IL-18) and damage-associated molecular patterns, which further modulate antitumor immunity within the tumor microenvironment. Although pyroptosis is considered a promising strategy to boost antitumor effects, it is also reported to cause unwanted tissue damage (e.g., gut damage and nephrotoxicity). Intriguingly, mounting evidence has uncovered nonpyroptotic roles of GSDMs in tumorigenesis, such as proliferation, invasion, metastasis, and drug resistance. Thus, this provides a rationale for GSDMs as potential therapeutic targets. Taken together, we shed unbiased light on the pyroptosis-dependent roles of GSDMs in cancer progression and highlighted how GSDMs modulate tumorigenesis in a pyroptosis-independent manner. It is evident that targeting GSDMs seems profound in cancer management; however, several problems require further investigation to target GSDMs from bench to bedside, which is elucidated in the discussion section.

A Prognostic Cuproptosis-Related LncRNA Signature for Colon Adenocarcinoma

Background

Cuproptosis, a recently discovered form of cell death, is caused by copper levels exceeding homeostasis thresholds. Although Cu has a potential role in colon adenocarcinoma (COAD), its role in the development of COAD remains unclear.

Methods

In this study, 426 patients with COAD were extracted from the Cancer Genome Atlas (TCGA) database. The Pearson correlation algorithm was used to identify cuproptosis-related lncRNAs. Using the univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) was used to select cuproptosis-related lncRNAs associated with COAD overall survival (OS). A risk model was established based on the multivariate Cox regression analysis. A nomogram model was used to evaluate the prognostic signature based on the risk model. Finally, mutational burden and sensitivity analyses of chemotherapy drugs were performed for COAD patients in the low- and high-risk groups.

Result

Ten cuproptosis-related lncRNAs were identified and a novel risk model was constructed. A signature based on ten cuproptosis-related lncRNAs was an independent prognostic predictor for COAD. Mutational burden analysis suggested that patients with high-risk scores had higher mutation frequency and shorter survival.

Conclusion

Constructing a risk model based on the ten cuproptosis-related lncRNAs could accurately predict the prognosis of COAD patients, providing a fresh perspective for future research on COAD.

Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm

Background

Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying the mechanisms responsible for the regulation of these genes in patients with COAD has become important. The present study aims to investigate the underlying mechanisms of platelet adhesion-related genes in COAD patients.

Methods

The present study was an experimental study. Initially, the effects of platelet number and related genomic alteration on survival were explored using real-world data and the cBioPortal database, respectively. Then, the differentially expressed platelet adhesion-related genes of COAD were analyzed using the TCGA database, and patients were further classified by employing the non-negative matrix factorization (NMF) analysis method. Afterward, some of the clinical and expression characteristics were analyzed between clusters. Finally, least absolute shrinkage and selection operator regression analysis was used to establish the prognostic nomogram. All data analyses were performed using the R package.

Results

High platelet counts are associated with worse survival in real-world patients, and alternations to platelet adhesion-related genes have resulted in poorer prognoses, based on online data. Based on platelet adhesion-related genes, patients with COAD were classified into two clusters by NMF-based clustering analysis. Cluster2 had a better overall survival, when compared to Cluster1. The gene copy number and enrichment analysis results revealed that two pathways were differentially enriched. In addition, the differentially expressed genes between these two clusters were enriched for POU6F1 in the transcription factor signaling pathway, and for MATN3 in the ceRNA network. Finally, a prognostic nomogram, which included the ALOX12 and ACTG1 genes, was established based on the platelet adhesion-related genes, with a concordance (C) index of 0.879 (0.848-0.910).

Conclusion

The mRNA expression-based NMF was used to reveal the potential role of platelet adhesion-related genes in COAD. The series of experiments revealed the feasibility of targeting platelet adhesion-associated gene therapy.

A novel autophagy-related long non-coding RNAs prognostic risk score for clear cell renal cell carcinoma

BACKGROUND

As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.

METHODS

The RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores.

RESULTS

A total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC.

CONCLUSIONS

We established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.

Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) is a common digestive system malignancy with high mortality and poor prognosis. Accumulating evidence indicates that choline metabolism is closely related to tumorigenesis and development. However, the efficacy of choline metabolism-related signature in predicting patient prognosis, immune microenvironment and chemotherapy response has not been fully clarified.

METHODS

Choline metabolism-related differentially expressed genes (DEGs) between normal and COAD tissues were screened using datasets from The Cancer Genome Atlas (TCGA), Kyoto Encyclopedia of Genes and Genomes (KEGG), AmiGO2 and Reactome Pathway databases. Two choline metabolism-related genes (CHKB and PEMT) were identified by univariate and multivariate Cox regression analyses. TCGA-COAD was the training cohort, and GSE17536 was the validation cohort. Patients in the high- and low-risk groups were distinguished according to the optimal cutoff value of the risk score. A nomogram was used to assess the prognostic accuracy of the choline metabolism-related signature. Calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) were used to improve the clinical applicability of the prognostic signature. Gene Ontology (GO) and KEGG pathway enrichment analyses of DEGs in the high- and low-risk groups were performed. KEGG cluster analysis was conducted by the KOBAS-i database. The distribution and expression of CHKB and PEMT in various types of immune cells were analyzed based on single-cell RNA sequencing (scRNA-seq). The CIBERSORT and ESTIMATE algorithms evaluated tumor immune cell infiltration in the high- and low-risk groups. Evaluation of the half maximal inhibitory concentration (IC50) of common chemotherapeutic drugs based on the choline metabolism-related signature was performed. Small molecule compounds were predicted using the Connectivity Map (CMap) database. Molecular docking is used to simulate the binding conformation of small molecule compounds and key targets. By immunohistochemistry (IHC), Western blot, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiments, the expression levels of CHKB and PEMT in human, mouse, and cell lines were detected.

RESULTS

We constructed and validated a choline metabolism-related signature containing two genes (CHKB and PEMT). The overall survival (OS) of patients in the high-risk group was significantly worse than that of patients in the low-risk group. The nomogram could effectively and accurately predict the OS of COAD patients at 1, 3, and 5 years. The DCA curve and CIC demonstrate the clinical utility of the nomogram. scRNA-seq showed that CHKB was mainly distributed in endothelial cells, while PEMT was mainly distributed in CD4+ T cells and CD8+ T cells. In addition, multiple types of immune cells expressing CHKB and PEMT differed significantly. There were significant differences in the immune microenvironment, immune checkpoint expression and chemotherapy response between the two risk groups. In addition, we screened five potential small molecule drugs that targeted treatment for COAD. Finally, the results of IHC, Western blot, and qRT-PCR consistently showed that the expression of CHKB in human, mouse, and cell lines was elevated in normal samples, while PMET showed the opposite trend.

CONCLUSION

In conclusion, we constructed a choline metabolism-related signature in COAD and revealed its potential application value in predicting the prognosis, immune microenvironment, and chemotherapy response of patients, which may lay an important theoretical basis for future personalized precision therapy.

Glycosyltransferase-related long non-coding RNA signature predicts the prognosis of colon adenocarcinoma

Purpose

Colon adenocarcinoma (COAD) is the most common type of colorectal cancer (CRC) and is associated with poor prognosis. Emerging evidence has demonstrated that glycosylation by long noncoding RNAs (lncRNAs) was associated with COAD progression. To date, however, the prognostic values of glycosyltransferase (GT)-related lncRNAs in COAD are still largely unknown.

Methods

We obtained the expression matrix of mRNAs and lncRNAs in COAD from The Cancer Genome Atlas (TCGA) database. Then, the univariate Cox regression analysis was conducted to identify 33 prognostic GT-related lncRNAs. Subsequently, LASSO and multivariate Cox regression analysis were performed, and 7 of 33 GT-related lncRNAs were selected to conduct a risk model. Gene set enrichment analysis (GSEA) was used to analyze gene signaling pathway enrichment of the risk model. ImmuCellAI, an online tool for estimating the abundance of immune cells, and correlation analysis were used to explore the tumor-infiltrating immune cells in COAD. Finally, the expression levels of seven lncRNAs were detected in colorectal cancer cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

A total of 1,140 GT-related lncRNAs were identified, and 7 COAD-specific GT-related lncRNAs (LINC02381, MIR210HG, AC009237.14, AC105219.1, ZEB1-AS1, AC002310.1, and AC020558.2) were selected to conduct a risk model. Patients were divided into high- and low-risk groups based on the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, indicating the good reliability and specificity of our risk model. Additionally, a nomogram based on the risk score and clinical traits was built to help clinical decisions. GSEA showed that the risk model was significantly enriched in metabolism-related pathways. Immune infiltration analysis revealed that five types of immune cells were significantly different between groups, and two types of immune cells were negatively correlated with the risk score. Besides, we found that the expression levels of these seven lncRNAs in tumor cells were significantly higher than those in normal cells, which verified the feasibility of the risk model.

Conclusion

The efficient risk model based on seven GT-related lncRNAs has prognostic potential for COAD, which may be novel biomarkers and therapeutic targets for COAD patients.

Construction and Validation of Pyroptosis-Related lncRNA Prediction Model for Colon Adenocarcinoma and Immune Infiltration Analysis

Objective

Colon adenocarcinoma (COAD) is one of the most prevalent cancers worldwide. However, the pyroptosis-related lncRNAs of COAD have not been deeply examined and validated. Here, we constructed and validated a risk model on pyroptosis-related lncRNAs in COAD.

Methods

The RNA sequencing transcriptome and clinical data of COAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed pyroptosis-related mRNAs and mRNA-lncRNA coexpression network were identified. After univariate and multifactorial cox analyses of prognosis-related lncRNAs, a risk model was constructed. Next, we analyzed the differences in immune infiltration, immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related gene expressions between the high- and low-risk groups. RT-qPCR was used to validate the expression of lncRNAs.

Result

A risk model was constructed based on 9 pyroptosis-related lncRNAs and separated COAD patients into the high- and low-risk groups. Immune infiltration analysis and immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related genes showed significant differences between the two subgroups. RT-qPCR showed that the 9 pyroptosis-related lncRNAs could be used as prognostic indicators.

Conclusion

A novel risk model based on pyroptosis-related lncRNAs was constructed and demonstrated that these lncRNAs might be used as independent prognostic biomarkers. This will also assist shed light on the COAD prognosis and therapy.

Identification of a glycolysis- and lactate-related gene signature for predicting prognosis, immune microenvironment, and drug candidates in colon adenocarcinoma

Background: Colon adenocarcinoma (COAD), a malignant gastrointestinal tumor, has the characteristics of high mortality and poor prognosis. Even in the presence of oxygen, the Warburg effect, a major metabolic hallmark of almost all cancer cells, is characterized by increased glycolysis and lactate fermentation, which supports biosynthesis and provides energy to sustain tumor cell growth and proliferation. However, a thorough investigation into glycolysis- and lactate-related genes and their association with COAD prognosis, immune cell infiltration, and drug candidates is currently lacking.

Methods: COAD patient data and glycolysis- and lactate-related genes were retrieved from The Cancer Genome Atlas (TCGA) and Gene Set Enrichment Analysis (GSEA) databases, respectively. After univariate Cox regression analysis, a nonnegative matrix factorization (NMF) algorithm was used to identify glycolysis- and lactate-related molecular subtypes. Least absolute shrinkage and selection operator (LASSO) Cox regression identified twelve glycolysis- and lactate-related genes (ADTRP, ALDOB, APOBEC1, ASCL2, CEACAM7, CLCA1, CTXN1, FLNA, NAT2, OLFM4, PTPRU, and SNCG) related to prognosis. The median risk score was employed to separate patients into high- and low-risk groups. The prognostic efficacy of the glycolysis- and lactate-related gene signature was assessed using Kaplan–Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. The nomogram, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) were employed to improve the clinical applicability of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on differentially expressed genes (DEGs) from the high- and low-risk groups. Using CIBERSORT, ESTIMATE, and single-sample GSEA (ssGSEA) algorithms, the quantities and types of tumor-infiltrating immune cells were assessed. The tumor mutational burden (TMB) and cytolytic (CYT) activity scores were calculated between the high- and low-risk groups. Potential small-molecule agents were identified using the Connectivity Map (cMap) database and validated by molecular docking. To verify key core gene expression levels, quantitative real-time polymerase chain reaction (qRT–PCR) assays were conducted.

Results: We identified four distinct molecular subtypes of COAD. Cluster 2 had the best prognosis, and clusters 1 and 3 had poor prognoses. High-risk COAD patients exhibited considerably poorer overall survival (OS) than low-risk COAD patients. The nomogram precisely predicted patient OS, with acceptable discrimination and excellent calibration. GO and KEGG pathway enrichment analyses of DEGs revealed enrichment mainly in the “glycosaminoglycan binding,” “extracellular matrix,” “pancreatic secretion,” and “focal adhesion” pathways. Patients in the low-risk group exhibited a larger infiltration of memory CD4+ T cells and dendritic cells and a better prognosis than those in the high-risk group. The chemotherapeutic agent sensitivity of patients categorized by risk score varied significantly. We predicted six potential small-molecule agents binding to the core target of the glycolysis- and lactate-related gene signature. ALDOB and APOBEC1 mRNA expression was increased in COAD tissues, whereas CLCA1 and OLFM4 mRNA expression was increased in normal tissues.

Conclusion: In summary, we identified molecular subtypes of COAD and developed a glycolysis- and lactate-related gene signature with significant prognostic value, which benefits COAD patients by informing more precise and effective treatment decisions.

A novel m7G-related lncRNA risk model for predicting prognosis and evaluating the tumor immune microenvironment in colon carcinoma

N7-Methylguanosine (m7G) modifications are a common type of posttranscriptional RNA modifications. Its function in the tumor microenvironment (TME) has garnered widespread focus in the past few years. Long non-coding RNAs (lncRNAs) played an essential part in tumor development and are closely associated with the tumor immune microenvironment. In this study, we employed a comprehensive bioinformatics approach to develop an m7G-associated lncRNA prognostic model based on the colon adenocarcinoma (COAD) database from The Cancer Genome Atlas (TCGA) database. Pearson’s correlation analysis was performed to identify m7G-related lncRNAs. Differential gene expression analysis was used to screen lncRNAs. Then, we gained 88 differentially expressed m7G-related lncRNAs. Univariate Cox analysis and Lasso regression analysis were performed to build an eight-m7G-related-lncRNA (ELFN1-AS1, GABPB1-AS1, SNHG7, GS1-124K5.4, ZEB1-AS1, PCAT6, C1RL-AS1, MCM3AP-AS1) risk model. Consensus clustering analysis was applied to identify the m7G-related lncRNA subtypes. We also verified the risk prediction effect of a gene signature in the GSE17536 test set (177 patients). A nomogram was constructed to predict overall survival rates. Furthermore, we analyzed differentially expressed genes (DEGs) between high-risk and low-risk groups. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted with the analyzed DEGs. At last, single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, MCP-COUNTER, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were utilized to discover the relationship between the risk model and the TME. Consequently, the m7G-related lncRNA risk model for COAD patients could be a viable prognostic tool and treatment target.

An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a frequent malignancy of the digestive system with a poor prognosis and high mortality rate worldwide. Intratumor heterogeneity (ITH) is associated with tumor progression, poor prognosis, immunosuppression, and therapy resistance. However, the relationship between ITH and prognosis, the immune microenvironment, and the chemotherapy response in COAD patients remains unknown, and this knowledge is urgently needed.

Methods

We obtained clinical information and gene expression data for COAD patients from The Cancer Genome Atlas (TCGA) database. The DEPTH2 algorithm was utilized to evaluate the ITH score. X-tile software was used to determine the optimal cutoff value of the ITH score. The COAD patients were divided into high- and low-ITH groups based on the cutoff value. We analyzed prognosis, tumor mutation burden (TMB), gene mutations, and immune checkpoint expression between the high- and low-ITH groups. Differentially expressed genes (DEGs) in the high- and low-ITH groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to screen the prognosis-related genes for the construction of an ITH-related prognostic signature. The nomogram was used to predict the overall survival (OS) of COAD patients. The protein–protein interaction (PPI) network was constructed by using the GeneMANIA database. Principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were employed to explore the differences in biological pathway activation status between the high- and low-risk groups. The proportion and type of tumor-infiltrating immune cells were evaluated by the CIBERSORT and ESTIMATE algorithms. Additionally, we assessed the chemotherapy response and predicted small-molecule drugs for treatment. Finally, the expression of the prognosis-related genes was validated by using the UALCAN database and Human Protein Atlas (HPA) database.

Results

The OS of the high-ITH group was worse than that of the low-ITH group. A positive correlation between ITH and TMB was identified. In subgroups stratified by age, gender, and tumor stage, the OS of the low-ITH group remained better than that of the high-ITH group. There were dramatic differences in the mutated genes, single nucleotide variant classes, variant types, immune checkpoints and cooccurring and mutually exclusive mutations of the DEGs between the high- and low-ITH groups. Based on the DEGs between the high- and low-ITH groups, we constructed a five-gene signature consisting of CEACAM5, ENO2, GABBR1, MC1R, and SLC44A4. The COAD patients were divided into high- and low-risk groups according to the median risk score. The OS of the high-risk group was worse than that of the low-risk group. The nomogram was used to accurately predict the 1-, 3- and 5-year OS of COAD patients and showed good calibration and moderate discrimination ability. The stromal score, immune score, and ESTIMATE score of the high-risk group were significantly higher than those of the low-risk group, whereas tumor purity showed the opposite trend. The patients classified by the risk score had distinguishable sensitivity to chemotherapeutic drugs. Finally, two public databases confirmed that CEACAM5 and SLC44A4 were upregulated in normal tissues compared with COAD tissues, and ENO2, GABBR1, and MC1R were upregulated in COAD tissues compared with normal tissues.

Conclusion

Overall, we identified an ITH-related prognostic signature for COAD that was closely related to the tumor microenvironment and chemotherapy response. This signature may help clinicians make more personalized and precise treatment decisions for COAD patients.

Identification and Validation of Immune-Related Biomarker Gene and Construction of ceRNA Networks in Septic Cardiomyopathy

Septic cardiomyopathy (SCM) is a cardiac dysfunction caused by severe sepsis, which greatly increases the risk of heart failure and death, and its molecular mechanism is unclear. The immune response has been reported to be an important process in septic cardiomyopathy and is present in the cardiac tissue of patients with sepsis, suggesting that the immune response may be an underlying mechanism of myocardial injury in SCM. Therefore, we explored the role of immune-related genes (IRGs) in SCM and aimed to identify pivotal immune-related targets with the aim of identifying key immune-related targets in SCM and potential therapeutic mechanisms involved in the pathological process of SCM. To explore the regulatory mechanisms of immune responses in SCM, we identified differentially expressed genes (DEGs) shared in the SCM datasets GSE179554 and GSE40180 by bioinformatics analysis and then obtained hub genes from the DEGs. Then, we obtained the immune-related hub genes (IRHGs) by intersecting the hub genes with IRGs and performed quantitative reverse transcription polymerase chain reaction to confirm the abnormal expression of IRHGs. Finally, we further constructed an immune-related lncRNA–miRNA–IRHG ceRNA regulatory network. In this study, we identified an IRHG that may be involved in the pathogenesis of SCM, which helps us to further elucidate the role of immune response in SCM and gain insights into the molecular mechanisms and potential therapeutic targets of SCM.

The Interferon Gamma-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common clinically malignant tumours of the digestive system, with high incidence and mortality and poor prognosis. Interferon-gamma (IFN-γ) and long noncoding RNAs (lncRNAs) have prognostic values and were closely associated with immune microenvironment in COAD. Thus, identifying IFN-γ-related lncRNAs may be valuable in predicting the survival of patients with COAD. In this study, we identified IFN-γ-related lncRNAs and divided COAD patients from the Cancer Genome Atlas (TCGA) database into training and validation sets. Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were performed to select IFN-γ-related lncRNA-associated prognoses. Thirteen lncRNAs (AC025165.8, AC091633.3, FENDRR, LINC00882, LINC01828, LINC01829, MYOSLID, RP11-154H23.4, RP11-20J15.3, RP11-324L17.1, RP11-342A23.2, RP11-805I24.3, SERTAD4-AS1) were identified to construct an IFN-γ-related lncRNA prognostic signature in TCGA training (n =213) and validation (n =213) cohorts. COAD patient risk scores were calculated and classified into high- and low-risk groups based on the median value of the risk scores in each dataset. We compared the overall survival (OS) of patients stratified by age, gender, and stage. The OS in the high-risk group was significantly shorter than that in the low-risk group. In addition, the clinical nomogram incorporating the prognostic signature and clinical features showed a high concordance index of 0.78 and accurately predicted 1-, 3-, and 5-year survival times among COAD patients in the high- and low-risk groups. Based on the risk model, the high- and low-risk groups exhibited distinct differences in the immune system by gene set enrichment analysis (GSEA) functional annotation, and differentially expressed genes (DEGs) between the high- and low-risk groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We investigated the expression of multiple immune checkpoint genes in the high- and low-risk groups and plotted Kaplan-Meier survival curves, indicating that immune checkpoint genes, such as LAG3 and PD. L1, STING and TIM 3, were also expressed differently between the two risk groups. Subsequently, there were dramatic differences in mutated genes, SNV (single nucleotide variants) classes, variant types and variant allele frequencies between low- and high-risk patients with COAD. Patients stratified by risk scores had different sensitivities to common chemotherapeutic agents. Finally, we used quantitative real-time polymerase chain reaction (qRT-PCR) assays to demonstrate that three lncRNAs were significantly differentially expressed in COAD tissues and adjacent normal tissues. Considered together, a thirteen-lncRNA prognostic signature has great potential to be a prognostic biomarker and could play an essential role in the immune microenvironment of COAD.

Genome-wide Exploration of a Pyroptosis-Related Long Non-Coding RNA Signature Associated With the Prognosis and Immune Response in Patients With Bladder Cancer

Background: Bladder cancer (BLCA) is a malignant tumor with a complex molecular mechanism and high recurrence rate in the urinary system. Studies have shown that pyroptosis regulates tumor cell proliferation and metastasis and affects the prognosis of cancer patients. However, the role of pyroptosis-related (PR) genes or long non-coding RNAs (lncRNAs) in BLCA development is not fully understood.

Methods: We comprehensively analyzed the molecular biological characteristics of PR genes in BLCA, including copy number variation, mutations, expression and prognostic value based on TCGA database. We then identified PR lncRNAs with prognostic value based on the expression of PR genes and performed a consistent clustering analysis of 407 BLCA patients according to the expression of prognosis-related PR lncRNAs and identified two clusters. The least absolute shrinkage and selection operator (LASSO) regression was used to establish a PR lncRNA signature and calculate the risk score associated with the prognosis of patients with BLCA. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were used to evaluate the possible functions of PR lncRNA signature. We also evaluated the relationship between the risk score and tumor immune microenvironment (TIME).

Results: A total of 33 PR genes were obtained in our study and 194 prognosis-related PR lncRNAs were identified. We also constructed a signature consisting of eight-PR-lncRNAs and divided patients into high- and low-risk groups. The overall survival rate of patients with a high risk was significantly lower than patients with a low risk. The risk score was significantly correlated with the degree of infiltration of multiple immune cell subtypes and positively correlated with multiple immune checkpoint genes expression in BLCA. Enrichment analyses showed that these lncRNAs are involved in human immune regulatory functions and immune-related pathways.

Conclusion: Our study comprehensively studied the molecular biological characteristics of PR genes BLCA, and the eight-PR-lncRNA signature we identified might play a crucial role in tumor immunity and may be able to predict the prognosis of BLCA patients, providing a theoretical basis for an in-depth study of the relationship between the prognosis and TIME.

5mC-Related lncRNAs as Potential Prognostic Biomarkers in Colon Adenocarcinoma

Simple Summary

To identify the prognostic significance of 5mC-related lncRNAs in colon adenocarcinoma (COAD), we examined the expression levels and mutations of 21 5mC-regulated genes of COAD in TCGA. We also identified lncRNAs associated with 5mC regulatory genes using Pearson correlation analysis. After the least absolute shrinkage and selection operator (Lasso) Cox regression, the risk signature of 4 5mC-related lncRNAs was selected. Next, the risk signature’s predictive efficacy was proven. Moreover, the biological mechanism and potential immunotherapeutic response of this risk signature were identified. Collectively, we constructed the 5mC-related lncRNA risk signature, which could provide a novel prognostic prediction of COAD patients.

Abstract

Globally, colon adenocarcinoma (COAD) is one of the most frequent types of malignant tumors. About 40~50% of patients with advanced colon adenocarcinoma die from recurrence and metastasis. Long non-coding RNAs (lncRNAs) and 5-methylcytosine (5mC) regulatory genes have been demonstrated to involve in the progression and prognosis of COAD. The goal of this study was to explore the biological characteristics and potential predictive value of 5mC-related lncRNA signature in COAD. In this research, The Cancer Genome Atlas (TCGA) was utilized to obtain the expression of genes and somatic mutations in COAD, and Pearson correlation analysis was used to select lncRNAs involved in 5mC-regulated genes. Furthermore, we applied univariate Cox regression and Lasso Cox regression to construct 5mC-related lncRNA signature. Then Kaplan–Meier survival analysis, principal components analysis (PCA), receiver operating characteristic (ROC) curve, and a nomogram were performed to estimate the prognostic effect of the risk signature. GSEA was utilized to predict downstream access of the risk signature. Finally, the immune characteristics and immunotherapeutic signatures targeting this risk signature were analyzed. In the results, we obtained 1652 5mC-related lncRNAs by Pearson correlation analysis in the TCGA database. Next, we selected a risk signature that comprised 4 5mC-related lncRNAs by univariate and Lasso Cox regression. The prognostic value of the risk signature was proven. Finally, the biological mechanism and potential immunotherapeutic response of the risk signature were identified. Collectively, we constructed the 5mC-related lncRNA risk signature, which could provide a novel prognostic prediction of COAD patients.