Powerful Identification of Large Quantitative Trait Loci Using Genome-wide R/glmnet-Based Regression

Constructing a prognostic model for hepatocellular carcinoma based on bioinformatics analysis of inflammation-related genes

Background

This study aims to screen inflammation-related genes closely associated with the prognosis of hepatocellular carcinoma (HCC) to accurately forecast the prognosis of HCC patients.

Methods

Gene expression matrices and clinical information for liver cancer samples were obtained from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An intersection of differentially expressed genes of HCC and normal and GeneCards yielded inflammation-related genes associated with HCC. Cox regression and the minor absolute shrinkage and selection operator (LASSO) regression analysis to filter genes associated with HCC prognosis. The prognostic value of the model was confirmed by drawing Kaplan-Meier and ROC curves. Select differentially expressed genes between the high-risk and low-risk groups and perform GO and KEGG pathways analyses. CIBERSORT analysis was conducted to assess associations of risk models with immune cells and verified using real-time qPCR.

Results

A total of six hub genes (C3, CTNNB1, CYBC1, DNASE1L3, IRAK1, and SERPINE1) were selected using multivariate Cox regression to construct a prognostic model. The validation evaluation of the prognostic model showed that it has an excellent ability to predict prognosis. A line plot was drawn to indicate the HCC patients' survival, and the calibration curve revealed satisfactory predictability. Among the six hub genes, C3 and DNASE1L3 are relatively low expressed in HCCLM3 and 97H liver cancer cell lines, while CTNNB1, CYBC1, IRAK1, and SERPINE1 are relatively overexpressed in liver cancer cell lines.

Conclusion

One new inflammatory factor-associated prognostic model was constructed in this study. The risk score can be an independent predictor for judging the prognosis of HCC patients' survival.

Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis

Severe acute pancreatitis (SAP) is a life-threatening gastrointestinal emergency. The study aimed to identify biomarkers and investigate molecular mechanisms of SAP. The GSE194331 dataset from GEO database was analyzed using bioinformatics. Differentially expressed genes (DEGs) associated with SAP were identified, and a protein-protein interaction network (PPI) was constructed. Machine learning algorithms were used to determine potential biomarkers. Gene set enrichment analysis (GSEA) explored molecular mechanisms. Immune cell infiltration were analyzed, and correlation between biomarker expression and immune cell infiltration was calculated. A competing endogenous RNA network (ceRNA) was constructed, and biomarker expression levels were quantified in clinical samples using RT-PCR. 1101 DEGs were found, with two modules most relevant to SAP. Potential biomarkers in peripheral blood samples were identified as glutathione S-transferase 1 (MGST1) and glutamyl peptidyltransferase (QPCT). GSEA revealed their association with immunoglobulin regulation, with QPCT potentially linked to pancreatic cancer development. Correlation between biomarkers and immune cell infiltration was demonstrated. A ceRNA network consisting of 39 nodes and 41 edges was constructed. Elevated expression levels of MGST1 and QPCT were verified in clinical samples. In conclusion, peripheral blood MGST1 and QPCT show promise as SAP biomarkers for diagnosis, providing targets for therapeutic intervention and contributing to SAP understanding.

Identification and functional analysis of lactic acid metabolism-related differentially expressed genes in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality rate that seriously threatens human health. We aimed to investigate the expression, prognostic value, and immune cell infiltration of lactic acid metabolism-related genes (LAMRGs) in HCC using bioinformatics. Methods: The HCC database (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) was downloaded from the Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between normal and tumor groups were identified. The LAMRGs were obtained from literature and GeneCards and MSigDB databases. Lactic acid metabolism-related differentially expressed genes (LAMRDEGs) in HCC were screened from the DEGs and LAMRGs. Functional enrichment analyses of the screened LAMRDEGs were further conducted using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA). The genes were used in multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Then, a protein-protein interaction network was constructed using STRING and CTD databases. Furthermore, the CIBERSORTx online database was used to assess the relationship between immune cell infiltration and hub genes. Results: Twenty-eight lactic acid metabolism-related differentially expressed genes (LAMRDEGs) were identified. The GO and KEGG analyses showed that the LAMRDEGs were related to the prognosis of HCC. The GSEA indicated that the LAMRDEGs were significantly enriched in tumor related pathways. In the multivariate Cox regression analysis, 14 key genes (E2F1, SERPINE1, GYS2, SPP1, PCK1, CCNB1, CYP2C9, IGFBP3, KDM8, RCAN1, ALPL, FBP1, NQO1, and LCAT) were found to be independent prognostic factors of HCC. Finally, the LASSO and Cox regression analyses showed that six key genes (SERPINE1, SPP1, CCNB1, CYP2C9, NQO1, and LCAT) were associated with HCC prognosis. Moreover, the correlation analyses revealed that the expression of the six key genes were associated with immune infiltrates of HCC. Conclusion: The LAMRDEGs can predict the prognosis and may be associated with immune cells infiltration in patients with HCC. These genes might be the promising biomarkers for the prognosis and treatment of HCC.

Bioinformatics-driven identification and validation of diagnostic biomarkers for cerebral ischemia reperfusion injury

Objective

This article aims to identify genetic features associated with immune cell infiltration in cerebral ischemia-reperfusion injury (CIRI) development through bioinformatics, with the goal of discovering diagnostic biomarkers and potential therapeutic targets.

Methods

We obtained two datasets from the Gene Expression Omnibus (GEO) database to identify immune-related differentially expressed genes (IRDEGs). These genes' functions were analyzed via Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Tools such as CIBERSORT and ssGSEA assessed immune cell infiltration. The Starbase and miRDB databases predicted miRNAs interacting with hub genes, and Cytoscape software mapped mRNA-miRNA interaction networks. The ENCORI database was employed to predict RNA binding proteins interacting with hub genes. Key genes were identified using a random forest algorithm and constructing a Support Vector Machine (SVM) model. LASSO regression analysis constructed a diagnostic model for hub genes to determine their diagnostic value, and PCR analysis validated their expression in cerebral ischemia-reperfusion.

Results

We identified 10 IRDEGs (C1qa, Ccl4, Cd74, Cd8a, Cxcl10, Gmfg, Grp, Lgals3bp, Timp1, Vim). The random forest algorithm, and SVM model intersection revealed three key genes (Ccl4, Gmfg, C1qa) as diagnostic biomarkers for CIRI. LASSO regression analysis, further refined this to two key genes (Ccl4 and C1qa), With ROC curve, analysis confirming their diagnostic efficacy (C1qa AUC = 0.75, Ccl4 AUC = 0.939). PCR analysis corroborated these findings.

Conclusions

Our study elucidates immune and metabolic response mechanisms in CIRI, identifying two immune-related genes as key biomarkers and potential therapeutic targets in response to cerebral ischemia-reperfusion injury.

Bioinformatics analysis of differentially expressed genes related to ischemia and hypoxia in spinal cord injury and construction of miRNA-mRNA or mRNA-transcription factor interaction network

BACKGROUND

Previous studies show that spinal cord ischemia and hypoxia is an important cause of spinal cord necrosis and neurological loss. Therefore, the study aimed to identify genes related to ischemia and hypoxia after spinal cord injury (SCI) and analyze their functions, regulatory mechanism, and potential in regulating immune infiltration.

METHODS

The expression profiles of GSE5296, GSE47681, and GSE217797 were downloaded from the Gene Expression Omnibus database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to determine the function and pathway enrichment of ischemia- and hypoxia-related differentially expressed genes (IAHRDEGs) in SCI. LASSO model was constructed, and support vector machine analysis was used to identify key genes. The diagnostic values of key genes were evaluated using decision curve analysis and receiver operating characteristic curve analysis. The interaction networks of miRNAs-IAHRDEGs and IAHRDEGs-transcription factors were predicted and constructed with the ENCORI database and Cytoscape software. CIBERSORT algorithm was utilized to analyze the correlation between key gene expression and immune cell infiltration.

RESULTS

There were 27 IAHRDEGs identified to be significantly expressed in SCI at first. These genes were mostly significantly enriched in wound healing function and the pathway associated with lipid and atherosclerosis. Next, five key IAHRDEGs (Abca1, Casp1, Lpl, Procr, Tnfrsf1a) were identified and predicted to have diagnostic value. Moreover, the five key genes are closely related to immune cell infiltration.

CONCLUSION

Abca1, Casp1, Lpl, Procr, and Tnfrsf1a may promote the pathogenesis of ischemic or hypoxic SCI by regulating vascular damage, inflammation, and immune infiltration.

A novel disulfidptosis-related lncRNAs signature for predicting survival and immune response in hepatocellular carcinoma

The accumulation of intracellular disulfides induces a novel and unique form of metabolic-related cell death known as disulfidptosis. A previous study revealed the prognostic value of a risk model of disulfidptosis-related genes in hepatocellular carcinoma (HCC). However, to date, no studies have investigated the relationship between disulfidptosis-related long non-coding RNAs (DRLs) and HCC. In this study, we collected and analyzed RNA sequencing data from 370 HCC samples to explore the DRLs in the tumorigenesis and development of HCC. By employing Lasso Cox regression and multivariate Cox regression analyses, we identified five prognostic DRLs, which were used to construct a prognostic signature. The signature was subsequently validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, Cox regression analyses, nomograms, and calibration curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed, revealing that the DRLs signature was associated with HCC and several cancer-related pathways. Furthermore, the DRLs signature showed correlations with the infiltration of M0 and M1 macrophages, immune-related functions, and multiple immune checkpoints, including PDCD1, LAG3, CTLA4, TIGIT, CD47, and others. Analysis using the tumor immune dysfunction and exclusion (TIDE) approach demonstrated that the DRLs signature could predict the response to immunotherapy. Finally, we screened potential chemotherapy drugs that could sensitize HCC. In conclusion, our novel DRLs signature provides valuable insights into predicting patient survival and immunotherapy responses.

Comprehensive analysis reveals key genes and environmental toxin exposures underlying treatment response in ulcerative colitis based on in-silico analysis and Mendelian randomization

BACKGROUND

UC is increasingly prevalent worldwide and represents a significant global disease burden. Although medical therapeutics are employed, they often fall short of being optimal, leaving patients struggling with treatment non-responsiveness and many related complications.

MATERIALS AND METHODS

The study utilized gene microarray data and clinical information from GEO. Gene enrichment and differential expression analyses were conducted using Metascape and Limma, respectively. Lasso Regression Algorithm was constructed using glmnet and heat maps were generated using pheatmap. ROC curves were used to assess diagnostic parameter capability, while XSum was employed to screen for small-molecule drugs exacerbating UC. Molecular docking was carried out using Autodock Vina. The study also performed Mendelian randomization analysis based on TwoSampleMR and used CTD to investigate the relationship between exposure to environmental chemical toxicants and UC therapy responsiveness.

RESULTS

Six genes (ELL2, DAPP1, SAMD9L, CD38, IGSF6, and LYN) were found to be significantly overexpressed in UC patient samples that did not respond to multiple therapies. Lasso analysis identified ELL2 and DAPP1 as key genes influencing UC treatment response. Both genes accurately predicted intestinal inflammation in UC and impacted the immunological infiltration status. Clofibrate showed therapeutic potential for UC by binding to ELL2 and DAPP1 proteins. The study also reviews environmental toxins and drug exposures that could impact UC progression.

CONCLUSIONS

We used microarray technology to identify DAPP1 and ELL2 as key genes that impact UC treatment response and inflammatory progression. Clofibrate was identified as a promising UC treatment. Our review also highlights the impact of environmental toxins on UC treatment response, providing valuable insights for personalized clinical management.

Prognostic Model and Tumor Immune Microenvironment Analysis of Complement-Related Genes in Gastric Cancer

Introduction

The complement system is integral to the innate and adaptive immune response, helping antibodies eliminate pathogens. However, the potential role of complement and its modulators in the tumor microenvironment (TME) of gastric cancer (GC) remains unclear.

Methods

This study assessed the expression, frequency of somatic mutations, and copy number variations of complement family genes in GC derived from The Cancer Genome Atlas (TCGA). Lasso and Cox regression analyses were conducted to develop a prognostic model based on the complement genes family, with the training and validation sets taken from the TCGA-GC cohort (n=371) and the International Gene Expression Omnibus (GEO) cohort (n=433), correspondingly. The nomogram assessment model was used to predict patient outcomes. Additionally, the link between immune checkpoints, immune cells, and the prognostic model was investigated.

Results

In contrast to patients at low risk, those at high risk had a less favorable outcome. The prognostic model-derived risk score was shown to serve as a prognostic marker of GC independently, as per the multivariate Cox analysis. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with GC in the 1, 3, 5 years. Additionally, the risk score was positively linked to the expression of immune checkpoints, notably CTLA4, LAG3, PDCD1, and CD274, according to an analysis of immune processes. The core gene C5aR1 in the prognostic model was found to be upregulated in GC tissues in contrast to adjoining normal tissues, and patients with elevated expressed levels of C5aR1 had lower 10-year overall survival (OS) rates.

Conclusion

Our work reveals that complement genes are associated with the diversity and complexity of TME. The complement prognosis model help improves our understanding of TME infiltration characteristics and makes immunotherapeutic strategies more effective.

Development and validation of a novel nomogram to predict the risk of the prolonged postoperative length of stay for lumbar spinal stenosis patients

BACKGROUND

Lumber spinal stenosis (LSS) is the increasingly reason for spine surgery for elder patients since China is facing the fastest-growing aging population. The aim of this research was to create a model to predict the probabilities of requiring a prolonged postoperative length of stay (PLOS) for lumbar spinal stenosis patients, minimizing the healthcare burden.

METHODS

A total of 540 LSS patients were enrolled in this project. The outcome was a prolonged PLOS after spine surgery, defined as hospitalizations ≥ 75th percentile for PLOS, including the day of discharge. The least absolute shrinkage and selection operator (LASSO) was used to identify independent risk variables related to prolonged PLOS. Multivariable logistic regression analysis was utilized to generate a prediction model utilizing the variables employed in the LASSO approach. The receiver operating characteristic (ROC) curve's area under the curve (AUC) and the calibration curve's respective curves were used to further validate the model's calibration with predictability and discriminative capabilities. By using decision curve analysis, the resulting model's clinical effectiveness was assessed.

RESULTS

Among 540 individuals, 344 had PLOS that was within the usual range of P75 (8 days), according to the interquartile range of PLOS, and 196 had PLOS that was above the normal range of P75 (prolonged PLOS). Four variables were incorporated into the predictive model, named: transfusion, operation duration, blood loss and involved spine segments. A great difference in clinical scores can be found between the two groups (P < 0.001). In the development set, the model's AUC for predicting prolonged PLOS was 0.812 (95% CI: 0.768-0.859), while in the validation set, it was 0.830 (95% CI: 0.753-0.881). The calibration plots for the probability showed coherence between the expected probability and the actual probability both in the development set and validation set respectively. When intervention was chosen at the potential threshold of 2%, analysis of the decision curve revealed that the model was more clinically effective.

CONCLUSIONS

The individualized prediction nomogram incorporating five common clinical features for LSS patients undergoing surgery can be suitably used to smooth early identification and improve screening of patients at higher risk of prolonged PLOS and minimize health care.

The prognostic and biological importance of chromatin regulation-related genes for lung cancer is examined using bioinformatics and experimentally confirmed

BACKGROUND

The pathogenesis and clinical diagnosis of lung adenocarcinoma (LUAD), a malignant illness with substantial morbidity and mortality, are still being investigated. Genes involved in chromatin regulation are crucial in the biological function of LUAD.

METHODS

The prognostic prediction model for LUAD was developed using multivariables and least absolute shrinkage and selection operator (LASSO) regression. It consisted of 10 chromatin regulators. The LUAD has been divided into two groups, high- and low-risk, using a predictive model. The model was shown to be accurate in predicting survival by the nomogram, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). An analysis of differences in immune-cell infiltration, immunologicalfunction, and clinical traits between low- and high-risk populations was conducted. Protein-protein interaction (PPI) networks and Gene Ontology (GO) pathways of differentially expressed genes (DEGs) in the high versus low risk group were also examined to investigate the association between genes and biological pathways. The biological roles of chromatin regulators (CRs) in LUAD were finally estimated using colony formation and cell movement. The important genes' mRNA expression has been measured using real-time polymerase chain reaction (RT-PCR).

RESULTS AND CONCLUSION

Risk score and stage based on the model could be seen as separate prognostic indicators for patients with LUAD. The main signaling pathway difference across various risk groups was in cell cycle. The immunoinfiltration profile of the tumor microenvironment (TME) and individuals with different risk levels were correlated, suggesting that the interaction of immune cells with the tumor led to the creation of a favorable immunosuppressive microenvironment. These discoveries aid in the creation of individualized therapies for LUAD patients.

Machine learning-based prediction of diagnostic markers for Graves' orbitopathy

PURPOSE

The pathogenesis of Graves' orbitopathy/thyroid-associated orbitopathy (TAO) is still unclear, and abnormal DNA methylation in TAO has been reported. Thus, selecting and exploring TAO biomarkers associated with DNA methylation may provide a reference for new therapeutic targets.

METHODS

The TAO-associated expression data and methylation data were downloaded from The Gene Expression Omnibus database. Firstly, weighted gene co-expression network analysis was used to obtain the TAO-related genes, which were intersected with differentially methylated genes (DMGs), and differentially expressed genes between TAO samples and normal samples to obtain TAO-associated DMGs (TA-DMGs). Thereafter, the functions of the TA-DMGs were analyzed, and diagnostic markers were screened by least absolute shrinkage and selection operator (Lasso) regression analysis and support vector machine (SVM) analysis. The expression levels and diagnostic values of the diagnostic markers were also analyzed. Furthermore, single gene pathway enrichment analysis was performed for each diagnostic marker separately using gene set enrichment analysis (GSEA) software. Next, we also performed immune infiltration analysis for each sample in the GSE58331 dataset using the single-sample GSEA algorithm, and the correlation between diagnostic markers and differential immune cells was explored. Lastly, the expressions of diagnostic markers were explored by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 125 TA-DMGs were obtained. The enrichment analysis results indicated that these TA-DMGs were mainly involved in immune-related pathways, such as Th1 and Th2 cell differentiation and the regulation of innate immune response. Moreover, two diagnostic markers, including S100A11 and NKD2, were obtained by Lasso regression analysis and SVM analysis. Single gene pathway enrichment analysis showed that S100A11 was involved in protein polyufmylation, pancreatic-mediated proteolysis, and NKD2 was involved in innate immune response in mucosa, Wnt signaling pathway, etc. Meanwhile, immune cell infiltration analysis screened 12 immune cells, including CD56 dim natural killer cells and Neutrophil cells that significantly differed between TAO and normal samples, with the strongest positive correlation between NKD2 and CD56 dim natural killer cells. Finally, the qRT-PCR illustrated the expressions of NKD2 and S100A11 between normal and TAO.

CONCLUSION

NKD2 and S100A11 were screened as biomarkers of TAO and might be regulated by DNA methylation in TAO, providing a new reference for the diagnosis and treatment of TAO patients.

A novel cuproptosis-related prognostic lncRNA signature for predicting immune and drug therapy response in hepatocellular carcinoma

Intratumoral copper levels are closely associated with immune escape from diverse cancers. Cuproptosis-related lncRNAs (CRLs), however, have an unclear relationship with hepatocellular carcinoma (HCC). Gene expression data from 51 normal tissues and 373 liver cancer tissues from the Cancer Genome Atlas (TCGA) database were collected and analyzed. To identify CRLs, we employed differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRNAs) analysis, Kaplan-Meier (K-M) analysis, and univariate regression. By univariate and Lasso Cox regression analyses, we screened 10 prognosis-related lncRNAs. Subsequently, five CRLs were identified by multivariable Cox regression analysis to construct the prognosis model. This feature is an independent prognostic indicator to forecast overall survival. According to Gene Set Variation Analysis (GSVA) and Gene Ontology (GO), both immune-related biological processes (BPS) and pathways have CRL participation. In addition, we found that the characteristics of CRLs were associated with the expression of the tumor microenvironment (TME) and crucial immune checkpoints. CRLs could predict the clinical response to immunotherapy based on the studies of tumor immune dysfunction and rejection (TIDE) analysis. Additionally, it was verified that tumor mutational burden survival and prognosis were greatly different between high-risk and low-risk groups. Finally, we screened potential sensitive drugs for HCC. In conclusion, this study provides insight into the TME status in patients with HCC and lays a basis for immunotherapy and the selection of sensitive drugs.