Identification of diagnostic biomarkers of rheumatoid arthritis based on machine learning-assisted comprehensive bioinformatics and its correlation with immune cells.

Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammatory cell infiltration, which can lead to chronic disability, joint destruction and loss of function. At present, the pathogenesis of RA is still unclear. The purpose of this study is to explore the potential biomarkers and immune molecular mechanisms of rheumatoid arthritis through machine learning-assisted bioinformatics analysis, in order to provide reference for the early diagnosis and treatment of RA disease. Methods: RA gene chips were screened from the public gene GEO database, and batch correction of different groups of RA gene chips was performed using Strawberry Perl. DEGs were obtained using the limma package of R software, and functional enrichment analysis such as gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), and gene set (GSEA) were performed. Three machine learning methods, least absolute shrinkage and selection operator regression (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest tree (Random Forest), were used to identify potential biomarkers of RA. The validation group data set was used to verify and further confirm its expression and diagnostic value. In addition, CIBERSORT algorithm was used to evaluate the infiltration of immune cells in RA and control samples, and the correlation between confirmed RA diagnostic biomarkers and immune cells was analyzed. Results: Through feature screening, 79 key DEGs were obtained, mainly involving virus response, Parkinson's pathway, dermatitis and cell junction components. A total of 29 hub genes were screened by LASSO regression, 34 hub genes were screened by SVM-RFE, and 39 hub genes were screened by Random Forest. Combined with the three algorithms, a total of 12 hub genes were obtained. Through the expression and diagnostic value verification in the validation group data set, 7 genes that can be used as diagnostic biomarkers for RA were preliminarily confirmed. At the same time, the correlation analysis of immune cells found that γδT cells, CD4+ memory activated T cells, activated dendritic cells and other immune cells were positively correlated with multiple RA diagnostic biomarkers, CD4+ naive T cells, regulatory T cells and other immune cells were negatively correlated with multiple RA diagnostic biomarkers. Conclusions: The results of novel characteristic gene analysis of RA showed that KYNU, EVI2A, CD52, C1QB, BATF, AIM2 and NDC80 had good diagnostic and clinical value for the diagnosis of RA, and were closely related to immune cells. Therefore, these seven DEGs may become new diagnostic markers and immunotherapy markers for RA.

Exploring the role of estrogen and progestins in breast cancer: A genomic approach to diagnosis.

Breast cancer (BC) is the most common cancer among women and a major cause of death from cancer. The role of estrogen and progestins, including synthetic hormones like R5020, in the development of BC has been highlighted in numerous studies. In our study, we employed machine learning and advanced bioinformatics to identify genes that could serve as diagnostic markers for BC. We thoroughly analyzed the transcriptomic data of two BC cell lines, T47D and UDC4, and performed differential gene expression analysis. We also conducted functional enrichment analysis to understand the biological functions influenced by these genes. Our study identified several diagnostic genes strongly associated with BC, including MIR6728, ENO1-IT1, ENO1-AS1, RNU6-304P, HMGN2P17, RP3-477M7.5, RP3-477M7.6, and CA6. The genes MIR6728, ENO1-IT1, ENO1-AS1, and HMGN2P17 are involved in cancer control, glycolysis, and DNA-related processes, while CA6 is associated with apoptosis and cancer development. These genes could potentially serve as predictors for BC, paving the way for more precise diagnostic methods and personalized treatment plans. This research enhances our understanding of BC and offers promising avenues for improving patient care in the future.

Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms.

OBJECTIVES: To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration. METHODS: GSE149507 and GSE6044 were used as the training group, while GSE108055 served as validation group A and GSE73160 served as validation group B. Differentially expressed genes (DEGs) were identified and analyzed for functional enrichment. Machine learning (ML) was used to identify candidate diagnostic genes for SCLC. The area under the receiver operating characteristic curves was applied to assess diagnostic efficacy. Immune cell infiltration analyses were carried out. RESULTS: There were 181 DEGs identified. The gene ontology analysis showed that DEGs were enriched in 455 functional annotations, some of which were associated with immunity. The kyoto encyclopedia of genes and genomes analysis revealed that there were 9 signaling pathways enriched. The disease ontology analysis indicated that DEGs were related to 116 diseases. The gene set enrichment analysis results displayed multiple items closely related to immunity. ZWINT and NRCAM were screened using ML and further validated as diagnostic genes. Significant differences were observed in SCLC with normal lung tissue samples among immune cell infiltration characteristics. Strong associations were found between the diagnostic genes and immune cell infiltration. CONCLUSION: This study identified 2 diagnostic genes, ZWINT and NRCAM, that were related to immune cell infiltration by integrating bioinformatics analysis and ML algorithms. These genes could serve as potential diagnostic biomarkers and provide possible molecular targets for immunotherapy in SCLC.

Expression profile of diagnostic genes in oral submucous fibrosis.

Oral Submucous Fibrosis (OSMF) is a chronic precancerous disorder of the oral mucosa caused by chewing of areca nut and its other variants. Chewing of areca nuts leads to dysregulated expression of specific genes, leading to various premalignant or malignant disorders. This study aimed to determine the differential expression of the diagnostic genes (MYH6, TNNT3, MYL1, and TPM2) in healthy controls and OSMF patients using saliva and tissue samples, determining the histopathological grade of the clinical samples. A total of 20 patients were included in the study and were divided into two groups: Group I consisted of 10 healthy patients (control group) and Group II consisted of 10 OSMF patients. Unstimulated whole saliva samples were collected from both groups, and the tissue samples were divided into two parts: one for RT-qPCR analysis and the other for histopathological assay. The expression profile of genes concerning OSMF saliva and tissue samples was significantly upregulated compared to the healthy control, and all the clinical samples of the study were categorized into histopathological grade 1. The findings of this study concluded that these genes can be referred to as diagnostic genes for OSMF in early and very early clinical samples, and saliva can be used as a promising diagnostic tool for early OSMF studies.

Identification of Diagnostic Genes of Aortic Stenosis That Progresses from Aortic Valve Sclerosis.

Background: Aortic valve sclerosis (AVS) is a pathological state that can progress to aortic stenosis (AS), which is a high-mortality valvular disease. However, effective medical therapies are not available to prevent this progression. This study aimed to explore potential biomarkers of AVS-AS advancement. Methods: A microarray dataset and an RNA-sequencing dataset were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened from AS and AVS samples. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning model construction were conducted to identify diagnostic genes. A receiver operating characteristic (ROC) curve was generated to evaluate diagnostic value. Immune cell infiltration was then used to analyze differences in immune cell proportion between tissues. Finally, immunohistochemistry was applied to further verify protein concentration of diagnostic factors. Results: A total of 330 DEGs were identified, including 92 downregulated and 238 upregulated genes. The top 5% of DEGs (n = 17) were screened following construction of a PPI network. IL-7 and VCAM-1 were identified as the most significant candidate genes via least absolute shrinkage and selection operator (LASSO) regression. The diagnostic value of the model and each gene were above 0.75. Proportion of anti-inflammatory M2 macrophages was lower, but the fraction of pro-inflammatory gamma-delta T cells was elevated in AS samples. Finally, levels of IL-7 and VCAM-1 were validated to be higher in AS tissue than in AVS tissue using immunohistochemistry. Conclusion: IL-7 and VCAM-1 were identified as biomarkers during the disease progression. This is the first study to analyze gene expression differences between AVS and AS and could open novel sights for future studies on alleviating or preventing the disease progression.

Screening and validation of atherosclerosis PAN-apoptotic immune-related genes based on single-cell sequencing.

Background: Carotid atherosclerosis (CAS) is a complication of atherosclerosis (AS). PAN-optosome is an inflammatory programmed cell death pathway event regulated by the PAN-optosome complex. CAS's PAN-optosome-related genes (PORGs) have yet to be studied. Hence, screening the PAN-optosome-related diagnostic genes for treating CAS was vital. Methods: We introduced transcriptome data to screen out differentially expressed genes (DEGs) in CAS. Subsequently, WGCNA analysis was utilized to mine module genes about PANoptosis score. We performed differential expression analysis (CAS samples vs. standard samples) to obtain CAS-related differentially expressed genes at the single-cell level. Venn diagram was executed to identify PAN-optosome-related differential genes (POR-DEGs) associated with CAS. Further, LASSO regression and RF algorithm were implemented to were executed to build a diagnostic model. We additionally performed immune infiltration and gene set enrichment analysis (GSEA) based on diagnostic genes. We verified the accuracy of the model genes by single-cell nuclear sequencing and RT-qPCR validation of clinical samples, as well as in vitro cellular experiments. Results: We identified 785 DEGs associated with CAS. Then, 4296 module genes about PANoptosis score were obtained. We obtained the 7365 and 1631 CAS-related DEGs at the single-cell level, respectively. 67 POR-DEGs were retained Venn diagram. Subsequently, 4 PAN-optosome-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) were identified via machine learning. Cellular function tests on four genes showed that these genes have essential roles in maintaining arterial cell viability and resisting cellular senescence. Conclusion: We obtained four PANoptosis-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) associated with CAS, laying a theoretical foundation for treating CAS.

MAPK14 as a key gene for regulating inflammatory response and macrophage M1 polarization induced by ferroptotic keratinocyte in psoriasis.

Psoriasis is a prevalent condition characterized by chronic inflammation, immune dysregulation, and genetic alterations, significantly impacting the well-being of affected individuals. Recently, a novel aspect of programmed cell death, ferroptosis, linked to iron metabolism, has come to light. This research endeavors to unveil novel diagnostic genes associated with ferroptosis in psoriasis, employing bioinformatic methods and experimental validation. Diverse analytical strategies, including "limma," Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were employed to pinpoint pivotal ferroptosis-related diagnostic genes (FRDGs) in the training datasets GSE30999, testing dataset GSE41662 and GSE14905. The discriminative potential of FRDGs in distinguishing between normal and psoriatic patients was gauged using Receiver Operating Characteristic (ROC) curves, while the functional pathways of FRDGs were scrutinized through Gene Set Enrichment Analysis (GSEA). Spearman correlation and ssGSEA analysis were applied to explore correlations between FRDGs and immune cell infiltration or oxidative stress-related pathways. The study identified six robust FRDGs - PPARD, MAPK14, PARP9, POR, CDCA3, and PDK4 - which collectively formed a model boasting an exceptional AUC value of 0.994. GSEA analysis uncovered their active involvement in psoriasis-related pathways, and substantial correlations with immune cells and oxidative stress were noted. In vivo, experiments confirmed the consistency of the six FRDGs in the psoriasis model with microarray results. In vitro, genetic knockdown or inhibition of MAPK14 using SW203580 in keratinocytes attenuated ferroptosis and reduced the expression of inflammatory cytokines. Furthermore, the study revealed that intercellular communication between keratinocytes and macrophages was augmented by ferroptotic keratinocytes, increased M1 polarization, and recruitment of macrophage was regulated by MAPK14. In summary, our findings unveil novel ferroptosis-related targets and enhance the understanding of inflammatory responses in psoriasis. Targeting MAPK14 signaling in keratinocytes emerges as a promising therapeutic approach for managing psoriasis.

Construction and validation of a diagnostic model for rheumatoid arthritis based on mitochondrial autophagy-related genes.

Rheumatoid arthritis (RA) is an autoimmune disease associated with an increased risk of disability. Due to its slow progression, timely diagnosis and treatment during the early stages can effectively decelerate disease advancement. Consequently, there is a pressing need to investigate additional biomarkers and therapeutic targets relevant to RA diagnosis. Mitochondrial autophagy, a biological process that regulates the quantity of mitochondria, is intricately linked to the development of tumor diseases. However, the role of autophagy in RA remains unclear. To address this, transcriptome data from the GEO database were collected for RA and its controls and subjected to differential expression analysis. The differentially expressed genes obtained were then intersected with mitochondrial autophagy-related genes. Subsequently, the overlapping genes were further intersected with genes from critical modules obtained through the weighted co-expression network algorithm. Diagnostic genes were identified, and diagnostic models were constructed for the resulting genes using the random forest and LASSO algorithms. The model achieved an AUC of 0.916 in the GSE93272 dataset and 0.951 in the GSE17755 dataset. Additionally, qPCR experiments were conducted on the diagnostic genes. Finally, we explored the correlation between the abundance of immune cell infiltration and diagnostic genes, constructing a drug-gene interaction network. The diagnostic genes identified in this study can serve as a reference for early diagnosis and the discovery of therapeutic targets in RA.

Machine learning algorithm predicts fibrosis-related blood diagnosis markers of intervertebral disc degeneration.

BACKGROUND: Intervertebral disc cell fibrosis has been established as a contributing factor to intervertebral disc degeneration (IDD). This study aimed to identify fibrosis-related diagnostic genes for patients with IDD. METHODS: RNA-sequencing data was downloaded from Gene Expression Omnibus (GEO) database. The diagnostic genes was identified using Random forest based on the differentially expressed fibrosis-related genes (DE-FIGs) between IDD and control samples. The immune infiltration states in IDD and the regulatory network as well as potential drugs targeted diagnostic genes were investigated. Quantitative Real-Time PCR was conducted for gene expression valifation. RESULTS: CEP120 and SPDL1 merged as diagnostic genes. Substantial variations were observed in the proportions of natural killer cells, neutrophils, and myeloid-derived suppressor cells between IDD and control samples. Further experiments indicated that AC144548.1 could regulate the expressions of SPDL1 and CEP120 by combininghsa-miR-5195-3p and hsa-miR-455-3p, respectively. Additionally, transcription factors FOXM1, PPARG, and ATF3 were identified as regulators of SPDL1 and CEP120 transcription. Notably, 56 drugs were predicted to target these genes. The down-regulation of SPDL1 and CEP120 was also validated. CONCLUSION: This study identified two diagnostic genes associated with fibrosis in patients with IDD. Additionally, we elucidated their potential regulatory networks and identified target drugs, which offer a theoretical basis and reference for further study into fibrosis-related genes involved in IDD.

Single-Cell RNA-Sequencing Analysis of Colonic Lamina Propria Immune Cells Reveals the Key Immune Cell-Related Genes of Ulcerative Colitis.

Background: Ulcerative colitis (UC) is a severe threat to humans worldwide. Single-cell RNA sequencing (scRNA-seq) can be used to screen gene expression patterns of each cell in the intestine, provide new insights into the potential mechanism of UC, and analyze the development of immune cell changes. These findings can provide new ideas for the diagnosis and treatment of intestinal diseases. In this study, bioinformatics analysis combined with experiments applied in dextran sulfate sodium (DSS)-induced colitis mice was used to explore new diagnostic genes for UC and their potential relationship with immune cells. Methods: We downloaded microarray datasets (GSE75214, GSE87473, GSE92415) from the Gene Expression Omnibus and used these datasets to screen differentially expressed genes (DEGs) and conduct Weighted Gene Co-expression Network Analysis (WGCNA) after quality control. The hub genes were screened, and ROC curves were drawn to verify the reliability of the results in both training set (GSE75214, GSE87473, GSE92415) and validation cohort (GSE87466). Also, we explored the relation of diagnostic genes and immune cells by CIBERSORT algorithm and single-cell analysis. Finally, the expression of hub genes and their relation with immune cells were verified in DSS-induced colitis mice. Results: Diagnostic genes (ANXA5, MMP7, NR1H4, CYP3A4, ABCG2) were identified. In addition, we found these five genes firmly related to immune infiltration. The DSS-induced colitis mice confirm that the expression of ANXA5 mainly increased in the intestinal macrophages and had a strong negative correlation with M2 macrophages, which indicated its possible influence on the polarization of macrophages in UC patients. Conclusion: We identified ANXA5, MMP7, NR1H4, CYP3A4, and ABCG2 as diagnostic genes of UC that are closely related to immune infiltration and ANXA5 maintains a negative correlation with M2 macrophages which indicated its possible influence on the polarization of macrophage in UC patients.

Four Markers Useful for the Distinction of Intrauterine Growth Restriction in Sheep.

Intrauterine growth restriction (IUGR) is a common perinatal complication in animal reproduction, with long-lasting negative effects on neonates and postnatal animals, which seriously negatively affects livestock production. In this study, we aimed to identify potential genes associated with the diagnosis of IUGR through bioinformatics analysis. Based on the 73 differentially expressed related genes obtained by differential analysis and weighted gene co-expression network analysis, we used three machine learning algorithms to identify 4 IUGR-related hub genes (IUGR-HGs), namely, ADAM9, CRYL1, NDP52, and SERPINA7, whose ROC curves showed that they are a good diagnostic target for IUGR. Next, we identified two molecular subtypes of IUGR through consensus clustering analysis and constructed a gene scoring system based on the IUGR-HGs. The results showed that the IUGR score was positively correlated with the risk of IUGR. The AUC value of IUGR scoring accuracy was 0.970. Finally, we constructed a new artificial neural network model based on the four IUGR-HGs to diagnose sheep IUGR, and its accuracy reached 0.956. In conclusion, the IUGR-HGs we identified provide new potential molecular markers and models for the diagnosis of IUGR in sheep; they can better diagnose whether sheep have IUGR. The present findings provide new perspectives on the diagnosis of IUGR.

Identification of Potential Diagnostic Genes of HIV-Infected Immunological Non-Responders on Bioinformatics Analysis.

Purpose: HIV-infected immunological non-responders (INRs) failed to achieve the normalization of CD4+ T cell counts despite their undetectable viral load. INRs have an increased risk of clinical progressions of Acquired Immunodeficiency Syndrome (AIDS) and non-AIDS events, accompanied by higher mortality rates than immunological responders (IRs). This study aimed to discover the genes, which help to distinguish INRs from IRs and explore the possible mechanism of INRs. Methods: Screening DEGs between INRs and IRs using GEO microarray dataset GSE143742. DEG biological functions were investigated using GO and KEGG analysis. DEGs and WGCNA linked modules were intersected to find common genes. Key genes were identified using SVM-RFE and LASSO regression models. ROC analysis was done to evaluate key gene diagnostic effectiveness using GEO database dataset GSE106792. Cytoscape created a miRNA-mRNA-TF network for diagnostic genes. CIBERSORT and flow cytometry examined the INRs and IRs immune microenvironments. In 10 INR and 10 IR clinical samples, diagnostic gene expression was verified by RT-qPCR and Western blot. Results: We obtained 190 DEGs between the INR group and IR group. Functional enrichment analysis found a significant enrichment in mitochondria and apoptosis-related pathways. CD69 and ZNF207 were identified as potential diagnostic genes. CD69 and ZNF207 shared a transcription factor, NCOR1, in the miRNA-mRNA-TF network. Immune microenvironment analysis by CIBERSORT showed that IRs had a higher level of resting memory CD4+ T cells, lower level of activated memory CD4+ T cells and resting dendritic cells than INRs, as confirmed by flow cytometry analysis. In addition, CD69 and ZNF207 were correlated with immune cells. Experiments confirmed the expression of the diagnostic genes in INRs and IRs. Conclusion: CD69 and ZNF207 were identified as potential diagnostic genes to discriminate INRs from IRs. Our findings offered new clues to diagnostic and therapeutic targets for INRs.

The essential role of forkhead box P4 (FOXP4) in thyroid cancer: a study related to The Cancer Genome Atlas and experimental data.

Objective: Thyroid cancer (THCA) is the most common endocrine cancer in the world. Although most patients with THCA have a good prognosis, the prognosis of those with THCA who have an extra-glandular invasion, vascular invasion, and distant metastasis is poor. Therefore, it is very important to find potential biomarkers that can effectively predict the prognosis and progression of highly aggressive THCAs. It has been identified that forkhead box P4 (FOXP4) may be a new biomarker for the proliferation and prognosis for tumor diagnosis. However, the expression and function of FOXP4 in THCA remain to be determined. Methods: In the present study, the function of FOXP4 in cells was investigated through the comprehensive analysis of data in The Cancer Genome Atlas and combined with experiments including immunohistochemistry (IHC), colony formation, Cell Counting Kit-8 assay, wound scratch healing, and transwell invasion assay. Results: In the present study, relevant bioinformatic data showed that FOXP4 was highly expressed in THCA, which was consistent with the results of the IHC and cell experiments. Meanwhile, 10 FOXP4-related hub genes were identified as potential diagnostic genes for THCA. It was found in further experiments that FOXP4 was located in the nucleus of THCA cells, and the expression of FOXP4 in the nucleus was higher than that in the cytoplasm. FOXP4 knockdown inhibited in vitro proliferation of the THCA cells, whereas overexpression promoted the proliferation and migration of THCA cells. Furthermore, deficiency of FOXP4 induced cell-cycle arrest. Conclusion: FOXP4 might be a potential target for diagnosing and treating THCA.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer.

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

Low Efficacy of Genetic Tests for the Diagnosis of Primary Lymphedema Prompts Novel Insights into the Underlying Molecular Pathways.

Lymphedema is a chronic inflammatory disorder caused by ineffective fluid uptake by the lymphatic system, with effects mainly on the lower limbs. Lymphedema is either primary, when caused by genetic mutations, or secondary, when it follows injury, infection, or surgery. In this study, we aim to assess to what extent the current genetic tests detect genetic variants of lymphedema, and to identify the major molecular pathways that underlie this rather unknown disease. We recruited 147 individuals with a clinical diagnosis of primary lymphedema and used established genetic tests on their blood or saliva specimens. Only 11 of these were positive, while other probands were either negative (63) or inconclusive (73). The low efficacy of such tests calls for greater insight into the underlying mechanisms to increase accuracy. For this purpose, we built a molecular pathways diagram based on a literature analysis (OMIM, Kegg, PubMed, Scopus) of candidate and diagnostic genes. The PI3K/AKT and the RAS/MAPK pathways emerged as primary candidates responsible for lymphedema diagnosis, while the Rho/ROCK pathway appeared less critical. The results of this study suggest the most important pathways involved in the pathogenesis of lymphedema, and outline the most promising diagnostic and candidate genes to diagnose this disease.

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

Background: Genetic factors account for approximately 35% of colorectal cancer risk. The specificity and sensitivity of previous diagnostic biomarkers for colorectal cancer could not meet the need of clinical application. The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning to build informative and predictive models of the underlying biological processes. The aim of this study is to identify diagnostic genes of colorectal cancer by using machine learning methods. Methods: The GSE41328 and GSE106582 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The gene expression differences between colon cancer and normal tissues were analyzed. The key colorectal cancer genes were screened and validated by Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) regression. Immune cell infiltration and the correlation with the key genes in patients with colon cancer were further analyzed by CIBERSORT. Results: Eleven key genes were identified as biomarkers for colon cancer, namely ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4. The mean area under the receiver operating characteristic (ROC) curve (AUC) of all 11 genes for colon cancer diagnosis were 0.94 with a range of 0.91-0.97. In the validation set, the expression of the 11 key genes was significantly different between colon cancer and normal subjects (P<0.05) and the mean AUCs were 0.82 with a range of 0.70-0.88. Immune cell infiltration analyses demonstrated that the relative quantity of plasma cells, T cells, B cells, NK cells, MO, M1, Dendritic cells resting, Mast cells resting, Mast cells activated, and Neutrophils in the tumor group were significantly different to the normal group. Conclusions: ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4 were identified as the key genes for colon cancer diagnosis. These genes are expected to become novel diagnostic markers and targets of new pharmacotherapies for colorectal cancer.

Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification.

BACKGROUND: The pathogenesis of rheumatoid arthritis (RA) is complex. This study aimed to identify diagnostic biomarkers and transcriptional regulators that underlie RA based on bioinformatics analysis and experimental verification. MATERIAL AND METHODS: We applied weighted gene co-expression network analysis (WGCNA) to analyze dataset GSE55457 and obtained the key module most relevant to the RA phenotype. We then conducted gene function annotation, gene set enrichment analysis (GSEA) and immunocytes quantitative analysis (CIBERSORT). Moreover, the intersection of differentially expressed genes (DEGs) and genes within the key module were entered into the STRING database to construct an interaction network and to mine hub genes. We predicted microRNA (miRNA) using a web-based tool (miRDB). Finally, hub genes and vital miRNAs were validated with independent GEO datasets, RT-qPCR and Western blot. RESULTS: A total of 367 DEGs were characterized by differential expression analysis. The WGCNA method divided genes into 14 modules, and we focused on the turquoise module containing 845 genes. Gene function annotation and GSEA suggested that immune response and inflammatory signaling pathways are the molecular mechanisms behind RA. Nine hub genes were screened from the network and seven vital regulators were obtained using miRNA prediction. CIBERSORT analysis identified five cell types enriched in RA samples, which were closely related to the expression of hub genes. Through ROC curve and RT-qPCR validation, we confirmed five genes that were specific for RA, including CCL25, CXCL9, CXCL10, CXCL11, and CXCL13. Moreover, we selected a representative gene (CXCL10) for Western blot validation. Vital miRNAs verification showed that only the differences in has-miR-573 and has-miR-34a were statistically significant. CONCLUSION: Our study reveals diagnostic genes and vital microRNAs highly related to RA, which could help improve our understanding of the molecular mechanisms underlying the disorder and provide theoretical support for the future exploration of innovative therapeutic approaches.