Gene Expression Omnibus Datasets Research Articles

Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma

ABSTRACT Background TP53 gene mutation is crucial in determining the prognosis of Multiple Myeloma (MM) patients. Understanding metabolic genes linked to TP53 mutation is vital for developing targeted therapies for these patients. Method We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 mutation and metabolism. Using univariate Cox regression and protein–protein interaction (PPI) analysis, we identified key genes. We categorized patients into high and low metabolism groups via non-negative matrix factorization (NMF) clustering, which led to the discovery of relevant differential genes. Integrating these with genes from the Gene Expression Omnibus (GEO) datasets and PPI interactions, we pinpointed crucial metabolic genes associated with TP53 mutation in MM. Additionally, we conducted prognostic analyses involving survival curves and receiver operating characteristic (ROC) charts. Results Our study reveals that the metabolic gene ribonucleotide reductase M2 (RRM2), linked to TP53 mutation, correlates positively with the International Staging System (ISS) stage in MM patients and is an independent prognostic risk factor. In the TCGA dataset, among the 767 patients, the 35 MM patients with TP53 mutation generally had poor survival outcomes. Specifically, the patients with both TP53 mutation and high RRM2 expression had a 2-year survival rate of only 38.87%, whereas those with normal TP53 function and low RRM2 expression had a 2-year survival rate of 86.31% (p < 0.001). Conclusion RRM2 significantly impacts MM prognosis and is associated with TP53 mutation, presenting itself as a potential therapeutic target and prognostic marker for MM.

Exome sequencing confirms the clinical diagnosis of both joubert syndrome and klinefelter syndrome with keratoconus in a han Chinese family

IntroductionJoubert syndrome a rare genetic disorder, is characterized by abnormalities in the development of the central nervous system with “molar signs” on magnetic resonance imaging of the brain and accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. Keratoconus (KC) is a kind of genetically predisposed eye disease that causes blindness characterized by a dilated thinning of the central or paracentral cornea conically projected forward, highly irregular astigmatism, and severe visual impairment. Klinefelter syndrome is caused by an extra X chromosome in the cells of male patients, and the main phenotype is tall stature and dysplasia with secondary sex characteristics. This study was intended to identify the genetic etiology and determine the clinical diagnosis of one Han Chinese family with specific clinical manifestations of keratoconus and multiorgan involvement.MethodsA comprehensive ocular and related general examination was performed on one patient and his asymptomatic parents and brother. Pathogenic genes were tested by exome sequencing. CNV-seq was used to verify the copy number variation, and peripheral blood was cultured for karyotype analysis. The pathogenicity of the identified variant was determined subject to ACMG guidelines. The Gene Expression Omnibus (GEO) dataset of keratoconus-related genes in the NCBI database was obtained to analyze the differentially expressed genes in corneal tissues of the keratoconus group and the normal control group, and analysis of protein-protein interaction networks (PPI) was performed.ResultsProband, a 25-year-old male, had sudden loss of vision in the left eye for 1 week. Best corrected visual acuity (BCVA): 0.5 (−1.00DS/-5.00DC*29°) in the right eye, counting fingers/40 cm in the left eye. Slit-lamp microscopy of the right eye showed mild anterior protrusion of the cornea and thinning of the cone-topped cornea. The left eye showed marked thinning of the central region of the cornea, rounded edema in the form of a cone-like bulge, epithelial bullae, edema and turbidity of the stroma, and bulging of the Descemet’s membrane. Cranial magnetic resonance imaging (MRI) revealed changes in the midbrain and cerebellum, with a “molar sign” and a “bat-winged” ventriculus quartus cerebri. General check-up: 168 cm in height, decreased muscle tone in all four limbs, knee jerk elicited, negative Babinski sign, abdominal reflexes elicited, finger-to-nose test positive, intentional tremor evident in both hands, positive Romberg’s sign, instability of gait, level I intellectual disability, poor adaptive behavior, communication disorders, teeth all dentures, a peculiar face with blepharophimosis, wide inner canthus distance, mild ptosis, severe positive epicanthus, high palatal arches, exotropia, hypotrichosis of beard and face, inconspicuous prominentia laryngea, and short upper and lower limbs. Exome sequencing detected compound heterozygous frameshift variants M1:c.9279dup:p.His3094Thrfs*18 and M2:c.6515_6522del:p.Lys2172Thrfs*37 in the patient’s CPLANE1 gene and the presence of duplication-type CNV on the X chromosome. Sanger sequencing showed that the mother and father carried the M1 and M2 variants, respectively, and the younger brother carried the M2 variant, which was a novel variant. CNV-seq analysis showed the presence of a duplication-type CNV Xp22.33-Xq28 (2757837-156030895) of approximately 155 Mb on the X chromosome of the proband, which was a de novo variant and carried by neither of the parents. The two heterozygous frameshift variants and duplication-type CNV were pathogenic according to the ACMG guidelines. Differential expression analysis of keratoconus-related genes showed that CPLANE1 was upregulated in the corneal tissues of keratoconus patients compared with normal controls, and such a difference was statistically significant (p = 0.000515, &amp;lt;0.05). PPI analysis showed that the CPLANE1-NPHP3 complex protein acted as a bridge between cilia and extracellular matrix tissue. According to the genetic test results and clinical phenotype analysis, the family was finally diagnosed with Joubert syndrome combined with Keratoconus and Klinefelter syndrome.DiscussionIn this study, we report a proband in a Han Chinese family with both Joubert syndrome and X-linked Klinefelter syndrome as well as keratoconus, and the phenotype spectrum of CPLANE1-Joubert syndrome may be expanded accordingly. Meanwhile, the significance of exome sequencing was emphasized in aiding the clinical diagnosis of complex cases, which is difficult to make.

Deciphering the Biological Ageing Impact on Alveolar Bone Loss: Insights from α-Klotho and Renal Function Dynamics.

Alveolar bone loss is generally considered as a chronological age-related disease. As biological ageing process is not absolutely determined by increasing age, whether alveolar bone loss associated with increasing chronological age or biological ageing remains unclear. Accurately distinguishing whether alveolar bone loss is chronological age-related or biological ageing-related is critical for selecting appropriate clinical treatments. This study aimed to identify the relationship between alveolar bone loss and body ageing. 3635 participants from National Health and Nutrition Examination Survey and 71 living kidney transplant recipients from Gene Expression Omnibus Datasets were enrolled. Multivariate regression analysis, smooth curve fittings and generalized additive models were used to explore the association among alveolar bone loss, age, serum α-Klotho level, renal function markers, as well as between preoperative creatinine and renal cortex related α-Klotho gene expression level. Meanwhile, a two-sample Mendelian randomization study was conducted to assess the causal relationship between α-Klotho and periodontal disease (4,376 individuals versus 361,194 individuals). As biological ageing related indicator, α-Klotho level was negatively correlated with impaired renal function and alveolar bone loss. Correspondingly, accompanied by decreasing renal function, it was manifested with down-regulated expression level of α-Klotho in renal cortex and aggravated alveolar bone loss. The MR analysis further identified the negative association between higher genetically predicted α-Klotho concentrations with alveolar bone loss susceptibility using the IVW (OR=0.999, P=0.005). However, an inversely U-shaped association was observed between chronological age and alveolar bone loss, which especially stable in men (the optimal cut-off values were both 62 years old). For male above 62 years old, increasing age converted to protective factor and accompanied by alleviated alveolar bone loss. Alveolar bone loss which directly associated to decreased renal function and α-Klotho level was related to biological ageing rather than chronological age. The renal-alveolar bone axis could provide new sight of clinical therapy in alveolar bone loss.

Tumor purity-related genes for predicting the prognosis and drug sensitivity of DLBCL patients.

Diffuse large B-cell lymphoma (DLBCL) is the predominant type of malignant B-cell lymphoma. Although various treatments have been developed, the limited efficacy calls for more and further exploration of its characteristics. Datasets from the Gene Expression Omnibus (GEO) database were used for identifying the tumor purity of DLBCL. Survival analysis was employed for analyzing the prognosis of DLBCL patients. Immunohistochemistry was conducted to detect the important factors that influenced the prognosis. Drug-sensitive prediction was performed to evaluate the value of the model. VCAN, CD3G, and C1QB were identified as three key genes that impacted the outcome of DLBCL patients both in GEO datasets and samples from our center. Among them, VCAN and CD3G+ T cells were correlated with favorable prognosis, and C1QB was correlated with worse prognosis. The ratio of CD68 + macrophages and CD8 + T cells was associated with better prognosis. In addition, CD3G+T cells ratio was significantly correlated with CD68 + macrophages, CD4 + T cells, and CD8 +T cells ratio, indicating it could play an important role in the anti-tumor immunity in DLBCL. The riskScore model constructed based on the RNASeq data of VCAN, C1QB, and CD3G work well in predicting the prognosis and drug sensitivity. VCAN, CD3G, and C1QB were three key genes that influenced the tumor purity of DLBCL, and could also exert certain impact on drug sensitivity and prognosis of DLBCL patients. This work is supported by the Shenzhen High-level Hospital Construction Fund and CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&T-B-062).

Tumor purity-related genes for predicting the prognosis and drug sensitivity of DLBCL patients

Background:Diffuse large B-cell lymphoma (DLBCL) is the predominant type of malignant B-cell lymphoma. Although various treatments have been developed, the limited efficacy calls for more and further exploration of its characteristics.Methods:Datasets from the Gene Expression Omnibus (GEO) database were used for identifying the tumor purity of DLBCL. Survival analysis was employed for analyzing the prognosis of DLBCL patients. Immunohistochemistry was conducted to detect the important factors that influenced the prognosis. Drug-sensitive prediction was performed to evaluate the value of the model.Results:VCAN, CD3G, and C1QB were identified as three key genes that impacted the outcome of DLBCL patients both in GEO datasets and samples from our center. Among them, VCAN and CD3G+ T cells were correlated with favorable prognosis, and C1QB was correlated with worse prognosis. The ratio of CD68 + macrophages and CD8 + T cells was associated with better prognosis. In addition, CD3G+T cells ratio was significantly correlated with CD68 + macrophages, CD4 + T cells, and CD8 +T cells ratio, indicating it could play an important role in the anti-tumor immunity in DLBCL. The riskScore model constructed based on the RNASeq data of VCAN, C1QB, and CD3G work well in predicting the prognosis and drug sensitivity.Conclusions:VCAN, CD3G, and C1QB were three key genes that influenced the tumor purity of DLBCL, and could also exert certain impact on drug sensitivity and prognosis of DLBCL patients.Funding:This work is supported by the Shenzhen High-level Hospital Construction Fund and CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&amp;T-B-062).

Tumor purity-related genes for predicting the prognosis and drug sensitivity of DLBCL patients

Background:Diffuse large B-cell lymphoma (DLBCL) is the predominant type of malignant B-cell lymphoma. Although various treatments have been developed, the limited efficacy calls for more and further exploration of its characteristics.Methods:Datasets from the Gene Expression Omnibus (GEO) database were used for identifying the tumor purity of DLBCL. Survival analysis was employed for analyzing the prognosis of DLBCL patients. Immunohistochemistry was conducted to detect the important factors that influenced the prognosis. Drug-sensitive prediction was performed to evaluate the value of the model.Results:VCAN, CD3G, and C1QB were identified as three key genes that impacted the outcome of DLBCL patients both in GEO datasets and samples from our center. Among them, VCAN and CD3G+ T cells were correlated with favorable prognosis, and C1QB was correlated with worse prognosis. The ratio of CD68 + macrophages and CD8 + T cells was associated with better prognosis. In addition, CD3G+T cells ratio was significantly correlated with CD68 + macrophages, CD4 + T cells, and CD8 +T cells ratio, indicating it could play an important role in the anti-tumor immunity in DLBCL. The riskScore model constructed based on the RNASeq data of VCAN, C1QB, and CD3G work well in predicting the prognosis and drug sensitivity.Conclusions:VCAN, CD3G, and C1QB were three key genes that influenced the tumor purity of DLBCL, and could also exert certain impact on drug sensitivity and prognosis of DLBCL patients.Funding:This work is supported by the Shenzhen High-level Hospital Construction Fund and CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&amp;T-B-062).

PGD: Shared gene linking polycystic ovary syndrome and endometrial cancer, influencing proliferation and migration through glycometabolism.

The relationship among polycystic ovary syndrome (PCOS), endometrial cancer (EC), and glycometabolism remains unclear. We explored shared genes between PCOS and EC, using bioinformatics to unveil their pathogenic connection and influence on EC prognosis. Gene Expression Omnibus datasets GSE226146 (PCOS) and GSE196033 (EC) were used. A protein-protein interaction (PPI) network was constructed to identify the central genes. Candidate markers were screened using dataset GSE54250. Differences in marker expression were confirmed in mouse PCOS and human EC tissues using RT-PCR and immunohistochemistry. The effect of PGD on EC proliferation and migration was explored using Ki-67 and Transwell assays. PGD's impact on the glycometabolic pathway within carbon metabolism was assessed by quantifying glucose content and lactic acid production. R software identified 31 common genes in GSE226146 and GSE196033. Gene Ontology functional classification revealed enrichment in the "purine nucleoside triphosphate metabolism process," with key Kyoto Encyclopedia of Genes and Genomes pathways related to "carbon metabolism." The PPI network identified 15 hub genes. HK2, NDUFS8, PHGDH, PGD, and SMAD3 were confirmed as candidate markers. The RT-PCR analysis validated distinct HK2 and PGD expression patterns in mouse PCOS ovarian tissue and human EC tissue, as well as in normal and EC cells. Transfection experiments with Ishikawa cells further confirmed PGD's influence on cell proliferation and migration. Suppression of PGD expression impeded glycometabolism within the carbon metabolism of EC cells, suggesting PGD as a significant PCOS risk factor impacting EC proliferation and migration through modulation of single carbon metabolism. These findings highlight PGD's pivotal role in EC onset and prognosis.

Analysis of glutathione Stransferase mu class 5 gene methylation as a prognostic indicator in low-grade gliomas

BACKGROUND: Low-grade gliomas (LGG) are a variety of brain tumors that show different clinical outcomes. The methylation of the GSTM5 gene has been noted in the development of LGG, however, its prognostic importance remains uncertain. OBJECTIVE: The objective of this study was to examine the correlation between GSTM5 DNA methylation and clinical outcomes in individuals diagnosed with LGG. METHODS: Analysis of GSTM5 methylation levels in LGG samples was conducted using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The overall survival based on GSTM5 methylation status was evaluated using Kaplan-Meier curves. The DNA methylation heatmap for particular CpG sites in the GSTM5 gene was visualized using the “pheatmap” R package. RESULTS: The study analyzed that LGG tumors had higher levels of GSTM5 methylation than normal tissues. There was an inverse relationship discovered between GSTM5 expression and methylation. LGG patients with hypermethylation of GSTM5 promoter experienced a positive outcome. Age, grade, and GSTM5 methylation were determined as independent prognostic factors in LGG through both univariate and multivariate Cox regression analyses. CONCLUSION: Methylation of GSTM5 DNA, specifically at certain CpG sites, is linked to a positive outlook in patients with LGG. Utilizing the “pheatmap” R package to visualize GSTM5 methylation patterns offers important information for identifying prognostic markers and therapeutic targets in low-grade gliomas.

Elevated HSPB1 Expression Is Associated with a Poor Prognosis in Glioblastoma Multiforme Patients.

Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer. This study investigated the clinical predictive value of heat shock protein β1 (HSPB1) in patients with GBM. A correlation was established between HSPB1 expression and GBM progression using data from The Cancer Genome Atlas (TCGA) dataset, Chinese Glioma Genome Atlas dataset, Gene Expression Omnibus dataset, and Human Protein Atlas database. A survival analysis was conducted and an HSPB1-based nomogram was constructed to evaluate the prognostic value of HSPB1 in patients with GBM. Based on TCGA data mining, we discovered that HSPB1 was significantly elevated in patients with GBM and may reflect their response to immunotherapy. In survival analysis, it appeared to have a predictive role in the prognosis of patients with GBM. Five signaling pathways were significantly enriched in the high HSPB1 expression phenotype according to the gene set enrichment analysis. In addition, a significant association was found between HSPB1 expression and immune checkpoints, tumor immune infiltration, tumor immune microenvironment, and immune cell markers in glioma. Overall, our results suggest that HSPB1 may regulate the function of immune cells, serve as a new immunotherapy target, and predict the response to immunotherapy in patients with GBM. HSPB1 appears to serve as a potential predictor of the clinical prognosis and response to immunotherapy in patients with GBM. It may be possible to identify patients who are likely to benefit from immunotherapy by assessing the expression level of HSPB1.

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy.

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score. Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis. The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Constructing an immune-related prognostic signature for predicting prognosis and immune response in hepatocellular carcinoma

BackgroundCurrently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC. MethodsImmune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC. Differentially expressed genes between normal and tumor cells from immune-associated cells and the immune-related genes from the ImmPort database were used to identify immune-related differentially expressed genes (IRDEGs). Subsequently, the risk model was established in the TCGA-LIHC cohort (n = 438) from the Cancer Genome Atlas (TCGA) database by using Kaplan-Meier (K-M) survival curve, univariate/multivariate Cox regression analysis. Subsequently, we further analyzed tumor immune microenvironment characteristics, somatic mutation, immune checkpoint and its ligand expression levels between high- and low-risk groups, as well as drug sensitivity prediction. ICGC cohort was set as the validation cohort. TCGA-LIHC cohort and three independent the Gene Expression Omnibus (GEO) datasets (GSE54236, GSE14520, and GSE64041) was used to verify IRDEGs expression, as well as PCR assays using clinical samples. ResultsThe IRDEGs was composed of 4 genes, namely B2M, SPP1, PPIA, and HRG. The 438 HCC patients were divided into high- and low-risk group. The high-risk group was associated with poor prognosis, including higher T stage, advanced pathological stages, less immune cell infiltration, higher TP53 mutation rate, the high expression of CTLA4 and HAVCR2. Besides, high-risk populations benefit from most chemotherapy drugs. Similarly, the performance of the risk model was validated in the ICGC. All four datasets (TCGA-LIHC cohort, GSE54236, GSE14520, and GSE64041) and clinical q-PCR results demonstrated that, compared with normal samples, the expressions of B2M and HRG were lower in tumor samples, and the expression of SPP1 was higher. ConclusionIn summary, the immune-related prognostic signature had a good predictive performance on prognosis and immunotherapy for HCC patients.

Exploration of the Shared Gene Signatures and Molecular Mechanisms between Chronic Bronchitis and Antineutrophil Cytoplasmic Antibody-associated Glomerulonephritis: Evidence from Transcriptome Data

Background: Chronic Bronchitis (CB) is a recurrent and persistent pulmonary inflammation disease. Growing evidence suggests an association between CB and Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN). However, the precise mechanisms underlying their association remain unclear. Aims: The purpose of this study was to further explore the molecular mechanism of the occurrence of chronic bronchitis (CB) associated with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA- GN). Objective: Our study aimed to investigate the potential shared pathogenesis of CB-associated ANCA-GN. Methods: Datasets of ANCA (GSE108113 and GSE104948) and CB (GSE151052 and GSE162635) were obtained from the Gene Expression Omnibus (GEO) datasets. Firstly, GSE108113 and GSE151052 were analyzed to identify common differentially expressed genes (DEGs) by Limma package. Based on common DEGs, protein-protein interaction (PPI) network and functional enrichment analyses, including GO, KEGG, and GSEA, were performed. Then, hub genes were identified by degree algorithm and validated in GSE104948 and GSE162635. Further PPI network and functional enrichment analyses were performed on hub genes. Additionally, a competitive ceRNA network was constructed through miRanda and spongeScan. Transcription factors (TFs) were predicted and verified using the TRRUST database. Furthermore, the CIBERSORT algorithm was employed to explore immune cell infiltration. The Drug Gene Interaction Database (DGIDB) was utilized to predict small-molecular compounds of CB and ANCA-GN. Result: A total of 963 DEGs were identified in the integrated CB dataset, and 610 DEGs were identified in the integrated ANCA-GN dataset. Totally, we identified 22 common DEGs, of which 10 hub genes (LYZ, IRF1, PIK3CG, IL2RG, NT5E, ARG2, HBEGF, NFATC2, ALPL, and FKBP5) were primarily involved in inflammation and immune responses. Focusing on hub genes, we constructed a ceRNA network composed of 323 miRNAs and 348 lncRNAs. Additionally, five TFs (SP1, RELA, NFKB1, HIF1A, and SP3) were identified to regulate the hub genes. Furthermore, immune cell infiltration results revealed immunoregulation in CB and ANCA-GN. Finally, some small-molecular compounds (Daclizumab, Aldesleukin, and NT5E) were predicted to predominantly regulate inflammation and immunity, especially IL-2. Conclusion: Our study explores the inflammatory-immune pathways underlying CB-associated ANCA-GN and emphasizes the importance of NETs and lymphocyte differentiation, providing novel insights into the shared pathogenesis and therapeutic targets.

Identification of cancer-associated fibroblast-related Ectodysplasin-A as a novel indicator for prognosis and immune response in gastric cancer

Studies have indicated cancer-associated fibroblasts (CAFs) could have a significant impact in gastric cancer (GC) progression and chemotherapy resistance. However, the gene related to cancer fibroblasts that can be used as biomarkers to judge the occurrence of gastric cancer has not been fully explored. Based on two Gene Expression Omnibus (GEO) datasets, we focus on differentially expressed genes which may act as CAFs markers related to GC. Through COX regression, LASSO regression and Kaplan-Meier survival analysis, we discovered three upregulated genes (GLT8D2, GNAS and EDA) associated with poor GC patients’ survival. By single-cell analysis and nomogram, we found that EDA may affect fibroblast production and disease prognosis in GC patients. EDA expression showed a positive correlation with 5-Fluorouracil IC50 values. Immunohistochemistry (IHC) and real time PCR indicated elevated EDA levels in GC tissues and cells. Enrichment analysis revealed that EDA was closely linked to immune system regulation. IHC and single-cell analysis indicated that EDA gene was associated with cancer fibroblasts marker FGF12 and influence cell interferon-gamma response, which may play a role in regulating immune-related characteristics. In summary, we concluded that EDA may be used as a new therapeutic CAFs marker for GC.

Bioinformatics analysis of the association between obesity and gastric cancer.

Obesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear. We obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein-protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes. A total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4, CXCL8, CXCL10, IL6, TNF, CCL4, CXCL2, CD4, and CCL2. IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p. Using bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC.

The prognostic value of immune escape-related genes in lung adenocarcinoma.

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

Identification of diagnostic biomarkers and potential therapeutic targets for biliary atresia via WGCNA and machine learning methods.

Biliary atresia (BA) is a severe and progressive biliary obstructive disease in infants that requires early diagnosis and new therapeutic targets. This study employed bioinformatics methods to identify diagnostic biomarkers and potential therapeutic targets for BA. Our analysis of mRNA expression from Gene Expression Omnibus datasets revealed 3,273 differentially expressed genes between patients with BA and those without BA (nBA). Weighted gene coexpression network analysis determined that the turquoise gene coexpression module, consisting of 298 genes, is predominantly associated with BA. The machine learning method then filtered out the top 2 important genes, CXCL8 and TMSB10, from the turquoise module. The area under receiver operating characteristic curves for TMSB10 and CXCL8 were 0.961 and 0.927 in the training group and 0.819 and 0.791 in the testing group, which indicated a high diagnostic value. Besides, combining TMSB10 and CXCL8, a nomogram with better diagnostic performance was built for clinical translation. Several studies have highlighted the potential of CXCL8 as a therapeutic target for BA, while TMSB10 has been shown to regulate cell polarity, which was related to BA progression. Our analysis with qRT PCR and immunohistochemistry also confirmed the upregulation of TMSB10 at mRNA and protein levels in BA liver samples. These findings highlight the sensitivity of CXCL8 and TMSB10 as diagnostic biomarkers and their potential as therapeutic targets for BA.

Development and validation of a novel disulfidptosis-related lncRNAs signature in patients with HPV-negative oral squamous cell carcinoma

Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan–Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

Identification of anoikis-related gene signatures and construction of the prognosis model in prostate cancer.

One of the primary reasons for tumor invasion and metastasis is anoikis resistance. Biochemical recurrence (BCR) of prostate cancer (PCa) serves as a harbinger of its distant metastasis. However, the role of anoikis in PCa biochemical recurrence has not been fully elucidated. Differential expression analysis was used to identify anoikis-related genes based on the TCGA and GeneCards databases. Prognostic models were constructed utilizing LASSO regression, univariate and multivariate Cox regression analyses. Moreover, Gene Expression Omnibus datasets (GSE70770 and GSE46602) were applied as validation cohorts. Gene Ontology, KEGG and GSVA were utilized to explore biological pathways and molecular mechanisms. Further, immune profiles were assessed using CIBERSORT, ssGSEA, and TIDE, while anti-cancer drugs sensitivity was analyzed by GDSC database. In addition, gene expressions in the model were examined using online databases (Human Protein Atlas and Tumor Immune Single-Cell Hub). 113 differentially expressed anoikis-related genes were found. Four genes (EEF1A2, RET, FOSL1, PCA3) were selected for constructing a prognostic model. Using the findings from the Cox regression analysis, we grouped patients into groups of high and low risk. The high-risk group exhibited a poorer prognosis, with a maximum AUC of 0.897. Moreover, larger percentage of immune infiltration of memory B cells, CD8 Tcells, neutrophils, and M1 macrophages were observed in the high-risk group than those in the low-risk group, whereas the percentage of activated mast cells and dendritic cells in the high-risk group were lower. An increased TIDE score was founded in the high-risk group, suggesting reduced effectiveness of ICI therapy. Additionally, the IC50 results for chemotherapy drugs indicated that the low-risk group was more sensitive to most of the drugs. Finally, the genes EEF1A2, RET, and FOSL1 were expressed in PCa cases based on HPA website. The TISCH database suggested that these four ARGs might contribute to the tumor microenvironment of PCa. We created a risk model utilizing four ARGs that effectively predicts the risk of BCR in PCa patients. This study lays the groundwork for risk stratification and predicting survival outcomes in PCa patients with BCR.

A comprehensive analysis of m6A/m7G/m5C/m1A-related gene expression and immune infiltration in liver ischemia–reperfusion injury by integrating bioinformatics and machine learning algorithms

BackgroundLiver ischemia–reperfusion injury (LIRI) is closely associated with immune infiltration, which commonly occurs after liver surgery, especially liver transplantation. Therefore, it is crucial to identify the genes responsible for LIRI and develop effective therapeutic strategies that target immune response. Methylation modifications in mRNA play various crucial roles in different diseases. This study aimed to identify potential methylation-related markers in patients with LIRI and evaluate the corresponding immune infiltration.MethodsTwo Gene Expression Omnibus datasets containing human liver transplantation data (GSE12720 and GSE151648) were downloaded for integrated analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to investigate the functional enrichment of differentially expressed genes (DEGs). Differentially expressed methylation-related genes (DEMRGs) were identified by overlapping DEG sets and 65 genes related to N6-methyladenosine (m6A), 7-methylguanine (m7G), 5-methylcytosine (m5C), and N1-methyladenosine (m1A). To evaluate the relationship between DEMRGs, a protein–protein interaction (PPI) network was utilized. The core DEMRGs were screened using three machine learning algorithms: least absolute shrinkage and selection operator, random forest, and support vector machine-recursive feature elimination. After verifying the diagnostic efficacy using the receiver operating characteristic curve, we validated the expression of the core DEMRGs in clinical samples and performed relative cell biology experiments. Additionally, the immune status of LIRI was comprehensively assessed using the single sample gene set enrichment analysis algorithm. The upstream microRNA and transcription factors of the core DEMRGs were also predicted.ResultsIn total, 2165 upregulated and 3191 downregulated DEGs were identified, mainly enriched in LIRI-related pathways. The intersection of DEGs and methylation-related genes yielded 28 DEMRGs, showing high interaction in the PPI network. Additionally, the core DEMRGs YTHDC1, METTL3, WTAP, and NUDT3 demonstrated satisfactory diagnostic efficacy and significant differential expression and corresponding function based on cell biology experiments. Furthermore, immune infiltration analyses indicated that several immune cells correlated with all core DEMRGs in the LIRI process to varying extents.ConclusionsWe identified core DEMRGs (YTHDC1, METTL3, WTAP, and NUDT3) associated with immune infiltration in LIRI through bioinformatics and validated them experimentally. This study may provide potential methylation-related gene targets for LIRI immunotherapy.

Identification of oxidative phosphorylation-related genes in moyamoya disease by combining bulk RNA-sequencing analysis and machine learning.

Introduction: Moyamoya disease (MMD) is a chronic cerebrovascular disease that can lead to ischemia and hemorrhagic stroke. The relationship between oxidative phosphorylation (OXPHOS) and MMD pathogenesis remains unknown. Methods: The gene expression data of 60 participants were acquired from three Gene Expression Omnibus (GEO) datasets, including 36 and 24 in the MMD and control groups. Differentially expressed genes (DEGs) between MMD patients MMD and control groups were identified. Machine learning was used to select the key OXPHOS-related genes associated with MMD from the intersection of DEGs and OXPHOS-related gene sets. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), gene set enrichment analysis (GSEA), Immune infiltration and microenvironments analysis were used to analyze the function of key genes. Machine learning selected four key OXPHOS-related genes associated with MMD: CSK, NARS2, PTPN6 and SMAD2 (PTPN6 was upregulated and the other three were downregulated). Results: Functional enrichment analysis showed that these genes were mainly enriched in the Notch signaling pathway, GAP junction, and RNA degradation, which are related to several biological processes, including angiogenesis, proliferation of vascular smooth muscle and endothelial cells, and cytoskeleton regulation. Immune analysis revealed immune infiltration and microenvironment in these MMD samples and their relationships with four key OXPHOS-related genes. APC co-inhibition (p = 0.032), HLA (p = 0.001), MHC I (p = 0.013), T cellco- inhibition (p = 0.032) and Type I IFN responses (p < 0.001) were significantly higher in the MMD groups than those in the control groups. The CSK positively correlated with APC co-inhibition and T cell-co-inhibition. The NARS2 negatively correlated with Type I IFN response. The SMAD2 negatively correlated with APC co-inhibition and Type I IFN response. The PTPN6 positively correlated with HLA, MHC I and Type I IFN responses. Discussion: This study provides a comprehensive understanding of the role of OXPHOS in MMD and will contribute to the development of new treatment methods and exploration of MMD pathogenesis.