Diagnostic genes and immune infiltration analysis of colorectal cancer determined by LASSO and SVM machine learning methods: a bioinformatics analysis

Combining single-cell and bulk RNA sequencing, NK cell marker genes reveal a prognostic and immune status in pancreatic ductal adenocarcinoma

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning

BACKGROUND

There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression.

METHODS

Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies.

RESULTS

The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation.

CONCLUSION

Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions.

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

Purpose

The aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci.

Methods

To do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA.

Results

Our research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose.

Conclusion

We searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.

TRIB3, as a robust prognostic biomarker for HNSC, is associated with poor immune infiltration and cancer cell immune evasion

Objective

As a pseudokinase, Tribbles Pseudokinase 3 (TRIB3) is implicated in a wide array of biological processes, including cell signal transduction, metabolic regulation, stress responses, and immune regulation. While its significant role in the immune regulation of certain cancers is well-established, the specific functions and impact of TRIB3 in head and neck squamous cell carcinoma (HNSC) remain unclear.

Methods

The data of RNA-sequence was acquired from the TCGA database to analyze the expression patterns of TRIB3 and elucidate its prognostic value in HNSC patients. Furthermore, the correlation between TRIB3 and tumor mutation burden, clinical data, immune checkpoint genes, and immune cell infiltration was explored. Moreover, the TRIB3 location in tumor tissues and subcellular structures was identified via Tisch in the HPA database, and the potential protein interaction molecules for TRIB3 were elucidated in the STRING database. The potential TRIB3 gene function was assessed using gene set enrichment analysis (GSEA), whereas the TRIB3 expression levels in clinical HNSC samples were verified by RT-qPCR and immunohistochemistry. the role of TRIB3 in enhancing the malignant behavior of HNSC cells was validated in vitro through a series of methods including RT-qPCR, CCK8 assay, wound healing assay, and transwell assay.

Results

It was revealed that TRIB3 was significantly overexpressed in the nucleus and cytoplasm of HNSC. Furthermore, this overexpression markedly enhanced the migration ability of tumor cells. As an independent prognostic factor, TRIB3 was associated with advanced tumor T stage and was significantly involved with tumor mutation burden and immune cell infiltration in HNSC. Moreover, it was observed that TRIB3 was not a predicted factor for PD1/PDL1 and ATL4 inhibitor treatment; however, it was substantially correlated with various immune evasion-related genes in HNSC.

Conclusion

TRIB3 could serve as a potential prognostic marker for HNSC and might be a key gene mediating HNSC immune evasion.

ALOX5AP suppresses osteosarcoma progression via Wnt/β-catenin/EMT pathway and associates with clinical prognosis and immune infiltration

Osteosarcoma (OS) is one of the most common malignant neoplasms in children and adolescents. Immune infiltration into the microenvironment of the tumor has a positive correlation with overall survival in patients with OS. The purpose of this study was to search for potential diagnostic markers that are involved in immune cell infiltration for OS. Patients with OS who acquired metastases within 5 years (n = 34) were compared to patients who did not develop metastases within 5 years (n = 19). Differentially expressed genes (DEGs) were tested for in both patient groups. To discover possible biomarkers, the LASSO regression model and the SVM-RFE analysis were both carried out. With the assistance of CIBERSORT, the compositional patterns of the 22 different types of immune cell fraction in OS were estimated. In this research, a total of 33 DEGs were obtained: 33 genes were significantly downregulated. Moreover, we identified six critical genes, including ALOX5AP, HLA-DOA, HLA-DMA, HLA-DRB4, HCLS1 and LOC647450. ROC assays confirmed their diagnostic value with AUC > 0.7. In addition, we found that the six critical genes were associated with immune infiltration. Then, we confirmed the expression of ALOX5AP was distinctly decreased in OS specimens and cell lines. High expression of ALOX5AP predicted an advanced clinical stage and overall survival of OS patients. Functionally, we found that overexpression of ALOX5AP distinctly suppressed the proliferation, migration, invasion and EMT via modulating Wnt/β-catenin signaling. Overall, we found that ALOX5AP overexpression inhibits OS development via regulation of Wnt/β-catenin signaling pathways, suggesting ALOX5AP as a novel molecular biomarker for enhanced therapy of OS.

Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning

Background

Increasing evidence has proven that rheumatoid arthritis (RA) can aggravate atherosclerosis (AS), and we aimed to explore potential diagnostic genes for patients with AS and RA.

Methods

We obtained the data from public databases, including Gene Expression Omnibus (GEO) and STRING, and obtained the differentially expressed genes (DEGs) and module genes with Limma and weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, the protein-protein interaction (PPI) network, and machine learning algorithms [least absolute shrinkage and selection operator (LASSO) regression and random forest] were performed to explore the immune-related hub genes. We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE73754. Finally, immune infiltration was developed in AS.

Results

The AS dataset included 5,322 DEGs, while there were 1,439 DEGs and 206 module genes in RA. The intersection of DEGs for AS and crucial genes for RA was 53, which were involved in immunity. After the PPI network and machine learning construction, six hub genes were used for the construction of a nomogram and for diagnostic efficacy assessment, which showed great diagnostic value (area under the curve from 0.723 to 1). Immune infiltration also revealed the disorder of immunocytes.

Conclusion

Six immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) were recognized, and the nomogram was developed for AS with RA diagnosis.