Identification of the Shared Gene Signatures and Biological Mechanism in Type 2 Diabetes and Pancreatic Cancer

Integrated Genomic Analysis of Lung Squamous Cell Carcinoma Subtypes Characterized by Immunogenic Cell Death-Relevant Gene Signature

Purpose

The objective of this study was to identify biomarkers associated with immunogenic cell death (ICD) in lung squamous cell carcinoma (LUSC), focusing on subtypes with distinct immunological characteristics and prognosis. Given the heterogeneous nature of LUSC, understanding ICD's role is crucial for developing tailored therapeutic strategies.

Patients and Methods

RNA sequencing data from 504 LUSC samples were analyzed using unsupervised clustering to identify ICD-related gene expression patterns. These patterns were linked to immune scores, immune cell infiltration, and clinical outcomes. A separate dataset was used to validate the association between ICD-related subtypes and immunotherapy efficacy.

Results

Unsupervised clustering revealed two distinct ICD-related subtypes with significantly different immune scores, immune cell infiltration levels, and prognosis. A prognostic model was developed based on differentially expressed ICD-related genes, which demonstrated strong predictive power for patient survival and immune response. This model may offer valuable insights for clinical decision-making, particularly for immunotherapy strategies.

Conclusion

This study identified key ICD-related biomarkers and developed a prognostic model that can enhance our understanding of ICD in LUSC, ultimately guiding personalized treatment approaches.

Gene Dysregulation and Islet Changes in PDAC-Associated Type 3c Diabetes

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy, often associated with new-onset diabetes. The relationship between PDAC and diabetes, particularly type 3c diabetes, remains poorly understood. This study investigates whether PDAC-associated diabetes represents a distinct subtype by integrating transcriptomic and histological analyses. Whole-tumour RNA sequencing data from The Cancer Genome Atlas (TCGA) were analysed to compare gene expression profiles between PDAC patients with and without diabetes. Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) deconvolution was employed to assess immune cell populations. Histopathological evaluations of pancreatic tissues were conducted to assess fibrosis and islet morphology. Histological analysis revealed perivascular fibrosis and islet basement membrane thickening in both PDAC cohorts. Transcriptomic data indicated significant downregulation of islet hormone genes insulin (INS) and glucagon (GCG) but not somatostatin (SST) in PDAC-associated diabetes, consistent with a type 3c diabetes phenotype. Contrary to previous reports, no distinct immunogenic signature was identified in PDAC with diabetes, as key immune checkpoint genes (Programmed Cell Death Protein 1 (PDCD1), Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), Programmed Death-Ligand 1(PD-L1)) were not differentially expressed. The findings suggest that PDAC-associated diabetes arises through neoplastic alterations in islet physiology rather than immune-mediated mechanisms. The observed reductions in endocrine markers reinforce the concept of PDAC-driven β-cell dysfunction as a potential early indicator of malignancy. Given the poor response of PDAC to PD-L1 checkpoint inhibitors, further research is needed to elucidate alternative therapeutic strategies targeting tumour-islet interactions.

Prospective Associations of Body Composition and Body Shape With the Risk of Developing Pancreatic Cancer in the UK Biobank Cohort

BACKGROUND

Obesity and diabetes are positively associated with pancreatic cancer risk. It is unclear, however, whether fat or fat-free mass plays a role in these relationships, whether abdominal obesity is more important than general obesity or whether the associations with anthropometric indices and diabetes are independent of each other.

METHODS

We used multivariable Cox proportional hazards models to examine the prospective associations of body composition (allometric fat-mass index (AFI) and allometric lean-mass index (ALI), based on bioelectrical impedance, uncorrelated with each other and with height), waist size (allometric waist-to-hip index (WHI), uncorrelated with weight and height) and diabetes with pancreatic cancer risk in UK Biobank. We tested heterogeneity by sex, age and follow-up time with the augmentation method (p_het).

RESULTS

During a mean follow-up of 10.4 years, 999 pancreatic cancer cases were ascertained in 427,939 participants. AFI was positively associated with pancreatic cancer risk in participants overall, independent of ALI, WHI, diabetes and covariates (hazard ratio HR = 1.102; 95% confidence interval CI = 1.033-1.176 per 1 standard deviation (SD) increase), more strongly in women aged under 55 years at recruitment (HR = 1.457; 95% CI = 1.181-1.797; p_het = 0.007) and in men only for follow-up 6 years or longer (HR = 1.159; 95% CI = 1.037-1.295; p_het = 0.075). ALI was positively associated with pancreatic cancer risk in participants overall (HR = 1.072; 95% CI = 1.005-1.145), more specifically in men (HR = 1.132; 95% CI = 1.035-1.238; p_het = 0.091). A positive association of WHI with pancreatic cancer risk was observed only in unadjusted models but was lost after adjustment for smoking status and diabetes. Independent of anthropometric indices, diabetes was associated positively with pancreatic cancer risk in participants overall (HR = 1.688; 95% CI = 1.365-2.087), but in women only for follow-up under 6 years (HR = 2.467; 95% CI = 1.477-4.121; p_het = 0.042).

CONCLUSIONS

General obesity (reflected in AFI and ALI) and diabetes but not abdominal obesity were associated positively with pancreatic cancer risk, independent of each other and covariates.

Identification of Shared Pathways and Molecules Between Type 2 Diabetes and Lung Adenocarcinoma and the Impact of High Glucose Environment on Lung Adenocarcinoma

Objective: This research focused on exploring the shared pathophysiological bases of lung adenocarcinoma (LUAD) and Type 2 diabetes mellitus (T2DM). Methods: The investigation into the molecular similarities between LUAD and T2DM involved querying the Gene Expression Omnibus for pertinent data. Upon pinpointing genes exhibiting differential expression, pathway enrichment analyses were executed to discern the molecular pathways shared by both conditions. In addition, GeneMANIA was employed to establish a protein interaction network, pinpointing STK26 as a critical gene. In addition, the influence of STK26 on the immune environment of the tumor was examined using tools such as the Microenvironment Cell Populations-counter to assess levels of stromal and immune cells in cancer tissues from expression profiles. Furthermore, a lung cancer cell model enriched in glucose was developed to facilitate the knockdown of STK26 using small interfering RNA. The influence of STK26 on A549 cell functionality was assessed using CCK-8, wound healing (scratch), and colony formation (cloning) assays. Results: This will help ensure accuracy and relevance in the revised version. TGF-β, HIF-1, AGE-RAGE, extracellular matrix (ECM) components and function regulation, and cell adhesion were activated in LUAD and T2DM. WGCNA identified two main modules in LUAD, three main modules in T2DM, and 44 shared genes. ClueGO and GeneMANIA analyses focused on pathways regulating cell growth and mitosis. Our analysis revealed STK26 as a central gene that exhibits elevated expression levels in tissues affected by LUAD. Elevated expression of STK26 correlates with a diminished prognosis for LUAD patients. In patients with LUAD characterized by elevated STK26 levels, gene set enrichment analysis identified a notable upregulation in numerous metabolic pathways. These include glycolysis-gluconeogenesis, oxidative phosphorylation, and the conversion pathways between pentose and glucuronic acid, as well as the pentose phosphate pathway. Gene set variation analysis suggested that a high STK26 expression was related to glycolysis, hypoxia, MYC, oxidative phosphorylation, cell cycle, and citric acid cycle pathways. In the group exhibiting elevated levels of STK26, a marked upregulation of glycolytic pathway genes, including HK2, RPIA, IDH3G, and SORD, was noted. This upregulation indicates a correlation between STK26 expression and these pivotal glycolytic genes. MCP-counter analysis suggested that the group with a high STK26 expression level had reduced immune infiltration. Laboratory studies have demonstrated that LUAD cells thrive in a high-glucose setting, where STK26 expression notably surpasses that observed under standard conditions. In addition, suppressing STK26 using siRNA significantly curtails both the growth and movement of LUAD cells. Conclusion: The research established a shared pathogenic basis between LUAD and T2DM. TGF-β, HIF-1, AGE-RAGE, ECM components and function regulation, cell adhesion, and additional signaling pathways are intricately linked with the pathophysiological mechanisms underlying both LUAD and T2DM. Thus, STK26 may affect the development of LUAD and T2DM by regulating glucose metabolism. Suppressing STK26 in a glucose-rich setting curtailed both the expansion and mobility of LUAD cells.

Sociodemographic characteristics associated with pancreatic cancer incidence and mortality among Blacks in the United States: a SEER-based study

Pancreatic cancer (PC) is the third leading cause of all cancer-related fatalities and accounts for approximately 3% of cancer cases in the United States. PC survival rates are lower in Blacks compared to other races, and this has been attributed to socioeconomic and genetic factors. In this study, we evaluated sociodemographic and genetic characteristics associated with PC incidence and mortality among Blacks. Data from the SEER 22 registries (2000-2020) were used to calculate the incidence rates and relative survival. County mortality rates from 2017 to 2021 were analyzed. Incidence rate ratios based on gender, age, primary disease site, stage, level of education, and poverty were calculated. Survival analysis was conducted using the Kaplan-Meier method. Mutant gene expression was obtained from the MSK-CHORD tumor registry. Overall, 48,606 Black patients were diagnosed with malignant PC between 2000 and 2020: females (53.53%) and males (46.47%). Both males and females experienced a slight increase in Annual Percent Change (APC) of PC incidence (0.24, 95% CI, -0.02-0.53) and (0.22, 95% CI, -0.05-0.51), respectively, from 2000 to 2020. Males aged 55 to 75 years were most frequently affected. Overall incidence risk from 2000-2020 by age was higher in Black males IRR > 1 (1.18, 95% CI, 1.16-1.21). The most common primary PC site for Black males and females was the head of the pancreas, 49.06% and 49.88%, respectively. By staging, distant PC had the highest frequency in Blacks. Poverty level was associated with PC incidence among females and PC mortality among both males and females. Stage was associated with survival among males with localized and regional PC. The 5-year relative survival was less than 11% across combined PC stages for both sexes. Black males had a relatively lower 5-year survival than Black females in localized (31.7 vs. 37.2%) and distant PC (2.6% vs. 2.90%). Mutant KRAS expression was higher in Black males. PC incidence and mortality were significantly higher in Black males. Our analysis points to the importance of poverty alleviation programs that target females are likely to reduce PC incidence. Furthermore, receiving recommended screening for PC and early-stage diagnostics is important to lower PC mortality.

Screening key genes for intracranial aneurysm rupture using LASSO regression and the SVM-RFE algorithm

Background

Although an intracranial aneurysm (IA) is widespread and fatal, few drugs can be used to prevent its rupture. This study explored the molecular mechanism and potential targets of IA rupture through bioinformatics methods.

Methods

The gene expression matrices of GSE13353, GSE122897, and GSE15629 were downloaded. Differentially expressed genes (DEGs) were screened using the limma package. Functional enrichment analysis was performed, and a PPI network was constructed. Furthermore, candidate key genes were identified using the least absolute shrinkage and selection operator (LASSO) regression model, support vector machine-recursive feature elimination (SVM-RFE) analysis, and PPI network analysis. ROC analysis was conducted to further verify the diagnostic value of the key genes.

Results

A total of 334 DEGs were screened, including 175 upregulated genes and 159 downregulated genes. Further functional analysis suggested that the DEGs were enriched in inflammation and immune response pathways. Fourteen hub genes were identified using the two algorithms. The PPI networks of the hub genes were analyzed using the Cytoscape plugin CytoNCA to obtain two key genes (IL10 and Integrin α5 (ITGA5)). The ROC curve analysis showed that the AUC values of IL10 and ITGA5 were 0.801, and 0.786, respectively. In addition, the two key genes were significantly positively correlated with macrophages and Treg (T) cells. The immune score and ESTIMATE score of the ruptured IA group were significantly higher than those of the unruptured IA group.

Conclusion

The increase in IL-10 and ITGA5 may weaken the vascular wall by promoting inflammation in blood vessels and immune cells, which could have a harmful effect on the rupture of IAs.

Identification of hub genes associated with pyroptosis in diabetic nephropathy patients using integrated bioinformatic analysis

OBJECTIVES

To investigate the role of pyroptosis in diabetic nephropathy (DN) and identify potential biomarkers for diagnosis.

METHODS

We analyzed the GEO dataset GSE96804 to identify differentially expressed genes (DEGs) related to pyroptosis in DN. The CIBERSORT method was used to assess M1 macrophage infiltration in the samples. Using weighted gene co-expression network analysis (WGCNA), we identified gene modules associated with M1 macrophages. The least absolute shrinkage and selection operator (LASSO) method was then applied to screen for key genes. The intersection of key genes identified by LASSO and the gene modules obtained from WGCNA resulted in the identification of ten hub genes as potential biomarkers for DN.

RESULTS

A total of 366 DEGs were identified, with 310 genes associated with pyroptosis. Increased M1 macrophage infiltration was observed in DN patients. Ten hub genes were identified as potential DN biomarkers: ECM1, LRP2BP, ALKBH7, CDH10, DUSP1, HSPA1A, LPL, NFIL3, PDK4, and TMEM150C.

CONCLUSIONS

This study highlights the importance of pyroptosis in DN pathophysiology and identifies 10 hub genes as potential biomarkers. These findings may contribute to improved diagnosis and treatment of DN.

A review of the last decade: pancreatic cancer and type 2 diabetes

Pancreatic cancer (PC) is a prevalent gastrointestinal tumour known for its high degree of malignancy, resulting in a mere 10% five-year survival rate for most patients. Over the past decade, a growing body of research has shed light on the intricate bidirectional association between PC and Type 2 diabetes (T2DM). The collection of PC- and T2DM-related articles is derived from two comprehensive databases, namely WOS (Web of Science Core Collection) and CNKI (China National Knowledge Infrastructure). This article discusses the last 10 years of research trends in PC and T2DM and explores their potential regulatory relationship as well as related medications.

Exploration of a miRNA-mRNA network shared between acute pancreatitis and Epstein-Barr virus infection by integrated bioinformatics analysis

Acute pancreatitis (AP) stands out as a primary cause of hospitalization within gastrointestinal ailments, attributed to diverse factors, including Epstein-Barr virus (EBV) infection. Nevertheless, the common miRNAs and genes shared between AP and EBV infection remain unclear. In the present study, four datasets GSE194331, GSE42455, GSE45918 and GSE109220 were selected and downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to screen for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Target genes of overlapping DEMs were predicted, and intersections with overlapping DEGs were used to construct a miRNA-mRNA network. In addition, the enrichment analysis, drug prediction, diagnostic accuracy assessment, competitive endogenous RNA (ceRNA) network construction, transcription factor (TF)-miRNA-mRNA network construction, and immune cell infiltration analysis were also carried out. We found a total of 111 genes and 8 miRNAs shared between AP and EBV infection. A miRNA-mRNA network was constructed, which comprised 5 miRNAs and 10 genes exhibiting robust diagnostic performance. Histone deacetylase (HDAC) inhibitor was identified as a novel therapeutic intervention from drug prediction analysis. The results of immune cell infiltration analysis revealed that a consistent and significant difference could be found on activated B cell in AP and EBV-infected individuals in comparison to the controls. Taken together, our work, for the first time, revealed a miRNA-mRNA network shared between AP and EBV infection, thereby enriching a deeper comprehension of the intricate molecular mechanisms and potential therapeutic targets entwined in these two pathological conditions.

Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning

Background

Non-alcoholic fatty liver disease (NAFLD) and ulcerative colitis (UC) are two common health issues that have gained significant global attention. Previous studies have suggested a possible connection between NAFLD and UC, but the underlying pathophysiology remains unclear. This study investigates common genes, underlying pathogenesis mechanisms, identification of diagnostic markers applicable to both conditions, and exploration of potential therapeutic targets shared by NAFLD and UC.

Methods

We obtained datasets for NAFLD and UC from the GEO database. The DEGs in the GSE89632 dataset of the NAFLD and GSE87466 of the UC dataset were analyzed. WGCNA, a powerful tool for identifying modules of highly correlated genes, was employed for both datasets. The DEGs of NAFLD and UC and the modular genes were then intersected to obtain shared genes. Functional enrichment analysis was conducted on these shared genes. Next, we utilize the STRING database to establish a PPI network. To enhance visualization, we employ Cytoscape software. Subsequently, the Cytohubba algorithm within Cytoscape was used to identify central genes. Diagnostic biomarkers were initially screened using LASSO regression and SVM methods. The diagnostic value of ROC curve analysis was assessed to detect diagnostic genes in both training and validation sets for NAFLD and UC. A nomogram was also developed to evaluate diagnostic efficacy. Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. Finally, we investigated the correlation between hub gene expression, diagnostic gene expression, and immune infiltration levels.

Results

We identified 34 shared genes that were found to be associated with both NAFLD and UC. These genes were subjected to enrichment analysis, which revealed significant enrichment in several pathways, including the IL-17 signaling pathway, Rheumatoid arthritis, and Chagas disease. One optimal candidate gene was selected through LASSO regression and SVM: CCL2. The ROC curve confirmed the presence of CCL2 in both the NAFLD and UC training sets and other validation sets. This finding was further validated using a nomogram in the validation set. Additionally, the expression levels of CCL2 for NAFLD and UC showed a significant correlation with immune cell infiltration.

Conclusion

This study identified a gene (CCL2) as a biomarker for NAFLD and UC, which may actively participate in the progression of NAFLD and UC. This discovery holds significant implications for understanding the progression of these diseases and potentially developing more effective diagnostic and treatment strategies.

Establishment and assessment of an oral squamous cell carcinoma N7-methylguanosine methyltransferase associated microRNA prognostic model

Background: N7-methylguanosine (m7G) methyltransferases and microRNAs (miRNAs) are closely associated with tumor progression. However, the role of m7G methyltransferase-related miRNAs as prognostic markers in oral squamous cell carcinoma (OSCC) has not been studied. This study aimed to explore the m7G methyltransferase-related miRNAs in OSCC, establish a prognostic model based on m7G methyltransferase-related miRNAs, investigate their correlation with immune cell infiltration, and assess their potential prognostic value. Methods: Transcriptional and clinical data of patients with OSCC were obtained from The Cancer Genome Atlas (TCGA) database. TargetScan and miRWalk were used to predict m7G methyltransferase-related miRNAs. Subsequently, differentially expressed m7G methyltransferase-related miRNAs in TCGA-OSCC were selected. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to build an m7G methyltransferase-related miRNA risk prognostic model for TCGA-OSCC. Patients were stratified into high- and low-risk groups. The predictive and diagnostic accuracies of the risk prognostic model were further validated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, independent prognosis analysis, and nomogram plots. Finally, quantitative real-time polymerase chain reaction (qPCR) was used to validate the expression levels of m7G methyltransferase-related miRNAs in postoperative cancer and adjacent normal tissues from 60 patients with OSCC. Results: Through Cox and LASSO regression analysis, six candidate miRNAs (hsa-miR-338-3p, hsa-miR-1251-3p, hsa-miR-3129-5p, hsa-miR-4633-3p, hsa-miR-216a-3p, and hsa-miR-6503-3p) most relevant to the prognosis of patients with OSCC were identified to construct an m7G methyltransferase-related miRNA risk prognostic model. In this model, the overall survival (OS) of the high-risk group was significantly shorter than that of the low-risk group (P < 0.001). The model effectively predicted prognosis and served as an independent prognostic indicator for patients with OSCC. Compared with the low-risk group, the high-risk group exhibited a significantly increased capacity for immune cell infiltration (P < 0.05), while the activation and initiation abilities of immune cells were decreased. Finally, six m7G methyltransferase-related miRNAs were validated in OSCC tissue samples. Conclusion: The risk prognostic model based on six m7G methyltransferase-related miRNAs can predict the OS rate of patients with OSCC and has the potential to guide individualized treatment. This prognostic model is closely associated with immune cell infiltration in patients with OSCC.

Identification of the shared gene MXD3 signatures and biological mechanism in patients with hip pain and prostate cancer

Prostate cancer (PRAD) is recognized as having a significant effect on systemic illnesses. This study examined possible immune cells, metabolic pathways, and genes that may explain the interaction between PRAD and hip pain. We used information retrieved from the Cancer Genome Atlas and the Gene Expression Omnibus databases. To find common genes, we utilized differential expression analysis and weighted gene co-expression network analysis. The genes that were shared were subjected to pathway enrichment studies using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Additionally, hub genes were analyzed using LASSO regression, and a receiver operating characteristic curve was generated based on the screening outcomes. The genes for the nodes were chosen in a protein-protein interaction network that was built. Single-sample gene-set enrichment analysis was performed to identify the differentially expressed genes. Immunohistochemistry staining confirmed hub gene expression, and single-sample gene-set enrichment analysis assessed immune cell infiltration. We concluded by comparing MAX dimerization protein 3 (MXD3) and MAX interactor 1 (MXI1) expression in tumor tissues using Uniform Manifold Approximation and Projection and violin plots in the Tumor lmmune Single-cell Hub database. After analyzing the intersection of the differentially expressed genes and weighted gene co-expression network analysis-significant module genes, we determined that MXD3 was the best shared diagnostic biomarker for PRAD and hip pain. One potential predictor of PRAD development was the MXI1 node gene, which was found in the protein-protein interaction network. The analyses revealed that MXD3 had a relatively positive correlation with neutrophil and T-helper cell infiltration levels, whereas MXI1 had a negative correlation with mast and Tgd cell levels. Tumors had lower levels of MXI1 expression and higher levels of MXD3 expression compared to normal tissues. Endothelial cells, induced pluripotent stem cells, and smooth muscle cells were all found to express MXI1. This is the first study to investigate the close genetic link between hip pain and PRAD using bioinformatics technologies. The 2 most significant genes involved in crosstalk between PRAD and hip pain were MXD3 and MXI1. The immunological responses triggered by T cells, mast cells, and neutrophils may be crucial in the relationship between PRAD and hip pain.

Unraveling the interplay: exploring signaling pathways in pancreatic cancer in the context of pancreatic embryogenesis

Pancreatic cancer continues to be a deadly disease because of its delayed diagnosis and aggressive tumor biology. Oncogenes and risk factors are being reported to influence the signaling pathways involved in pancreatic embryogenesis leading to pancreatic cancer genesis. Although studies using rodent models have yielded insightful information, the scarcity of human pancreatic tissue has made it difficult to comprehend how the human pancreas develops. Transcription factors like IPF1/PDX1, HLXB9, PBX1, MEIS, Islet-1, and signaling pathways, including Hedgehog, TGF-β, and Notch, are directing pancreatic organogenesis. Any derangements in the above pathways may lead to pancreatic cancer. TP53: and CDKN2A are tumor suppressor genes, and the mutations in TP53 and somatic loss of CDKN2A are the drivers of pancreatic cancer. This review clarifies the complex signaling mechanism involved in pancreatic cancer, the same signaling pathways in pancreas development, the current therapeutic approach targeting signaling molecules, and the mechanism of action of risk factors in promoting pancreatic cancer.

A CLRN3-Based CD8+ T-Related Gene Signature Predicts Prognosis and Immunotherapy Response in Colorectal Cancer

BACKGROUND

Colorectal cancer (CRC) ranks among the most prevalent malignancies affecting the gastrointestinal tract. The infiltration of CD8+ T cells significantly influences the prognosis and progression of tumor patients.

METHODS

This study establishes a CRC immune risk model based on CD8+ T cell-related genes. CD8+ T cell-related genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA), and the enriched gene sets were annotated via Gene Ontology (GO) and Reactome pathway analysis. Employing machine learning methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Random Forest (RF), we identified nine genes associated with CD8+ T-cell infiltration. The infiltration levels of immune cells in CRC tissues were assessed using the ssGSEA algorithm.

RESULTS

These genes provide a foundation for constructing a prognostic model. The TCGA-CRC sample model's prediction scores were categorized, and the prediction models were validated through Cox regression analysis and Kaplan-Meier curve analysis. Notably, although CRC tissues with higher risk scores exhibited elevated levels of CD8+ T-cell infiltration, they also demonstrated heightened expression of immune checkpoint genes. Furthermore, comparison of microsatellite instability (MSI) and gene mutations across the immune subgroups revealed notable gene variations, particularly with APC, TP53, and TNNT1 showing higher mutation frequencies. Finally, the predictive model's efficacy was corroborated through the use of Tumor Immune Dysfunction and Exclusion (TIDE), Immune Profiling Score (IPS), and immune escape-related molecular markers. The predictive model was validated through an external cohort of CRC and the Bladder Cancer Immunotherapy Cohort. CLRN3 expression levels in tumor and adjacent normal tissues were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Subsequent in vitro and in vivo experiments demonstrated that CLRN3 knockdown significantly attenuated the malignant biological behavior of CRC cells, while overexpression had the opposite effect.

CONCLUSIONS

This study presents a novel prognostic model for CRC, providing a framework for enhancing the survival rates of CRC patients by targeting CD8+ T-cell infiltration.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

Study of hub nodes of transcription factor-target gene regulatory network and immune mechanism for type 2 diabetes based on chip analysis of GEO database

Identification of novel therapeutic targets for type 2 diabetes is a key area of contemporary research. In this study, we screened differentially expressed genes in type 2 diabetes through the GEO database and sought to identify the key virulence factors for type 2 diabetes through a transcription factor regulatory network. Our findings may help identify new therapeutic targets for type 2 diabetes. Data pertaining to the humoral (whole blood) gene expression profile of diabetic patients were obtained from the NCBI's GEO Datasets database and gene sets with differential expression were identified. Subsequently, the TRED transcriptional regulatory element database was integrated to build a gene regulatory network for type 2 diabetes. Functional analysis (GO-Analysis) and Pathway-analysis of differentially expressed genes were performed using the DAVID database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, gene-disease correlation analysis was performed using the DAVID online annotation tool. A total of 236 pathogenic genes, four transcription factors related to the pathogenic genes, and 261 corresponding target genes were identified. A transcription factor-target gene regulatory network for type 2 diabetes was constructed. Most of the key factors of the transcription factor-target gene regulatory network for type 2 diabetes were found closely related to the immune metabolic system and the functions of cell proliferation and transformation.

Weighted gene co-expression network analysis and whole genome sequencing identify potential lung cancer biomarkers

Background

Tuberculosis (TB) leads to an increased risk of lung cancer (LC). However, the carcinogenetic mechanism of TB remains unclear. We constructed gene co-expression networks and carried out whole-exome sequencing (WES) to identify key modules, hub genes, and the most recurrently mutated genes involved in the pathogenesis of TB-associated LC.

Methods

The data used in this study were obtained from the Gene Expression Omnibus (GEO) and WES. First, we screened LC-related genes in GSE43458 and TB-related genes in GSE83456 by weighted gene co-expression network analysis (WGCNA). Subsequently, we screened differentially expressed genes related to LC and TB in GSE42834. We also performed WES of 15 patients (TB, n = 5; LC, n = 5; TB+LC, n = 5), constructed mutational profiles, and identified differences in the profiles of the three groups for further investigation.

Results

We identified 278 hub genes associated with tumorigenesis of pulmonary TB. Moreover, WES identified 112 somatic mutations in 25 genes in the 15 patients. Finally, four common genes (EGFR, HSPA2, CECR2, and LAMA3) were confirmed in a Venn diagram of the 278 hub genes and the mutated genes from WES. KEGG analysis revealed various pathway changes. The PI3K-AKT signaling pathway was the most enriched pathway, and all four genes are included in this pathway. Thus, these four genes and the PI3K-AKT signaling pathway may play important roles in LC.

Conclusion

Several potential genes and pathways related to TB-associated LC were identified, including EGFR and three target genes not found in previous studies. These genes are related to cell proliferation, colony formation, migration, and invasion, and provide a direction for future research into the mechanisms of LC co-occurring with TB. The PI3K-AKT signaling pathway was also identified as a potential key pathway involved in LC development.

Integrated Pleiotropic Gene Set Unveils Comorbidity Insights across Digestive Cancers and Other Diseases

Comorbidities are prevalent in digestive cancers, intensifying patient discomfort and complicating prognosis. Identifying potential comorbidities and investigating their genetic connections in a systemic manner prove to be instrumental in averting additional health challenges during digestive cancer management. Here, we investigated 150 diseases across 18 categories by collecting and integrating various factors related to disease comorbidity, such as disease-associated SNPs or genes from sources like MalaCards, GWAS Catalog and UK Biobank. Through this extensive analysis, we have established an integrated pleiotropic gene set comprising 548 genes in total. Particularly, there enclosed the genes encoding major histocompatibility complex or related to antigen presentation. Additionally, we have unveiled patterns in protein-protein interactions and key hub genes/proteins including TP53, KRAS, CTNNB1 and PIK3CA, which may elucidate the co-occurrence of digestive cancers with certain diseases. These findings provide valuable insights into the molecular origins of comorbidity, offering potential avenues for patient stratification and the development of targeted therapies in clinical trials.

Prediction of shared gene signatures and biological mechanisms between polycystic ovary syndrome and asthma: Based on weighted gene coexpression network analysis

OBJECTIVE

Several clinical studies have shown an association between polycystic ovary syndrome (PCOS) and asthma; however, the molecular link between these conditions remains unclear. In this study, we conducted a reanalysis and repurposing of existing databases in order to depict the common key genes, related signaling pathways, and similarity of the immune microenvironment between PCOS and asthma.

METHODS

PCOS and asthma data sets were downloaded, and common signal pathways were identified by using gene set enrichment analysis. Identified common susceptibility genes were explored by intersecting the weighted gene coexpression network analysis module genes for both diseases. Then, we performed protein-protein interaction, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses of the common susceptibility genes. Finally, we analyzed the immune environment of PCOS and asthma.

RESULTS

We identified five hub genes, namely, MMP9, CDC42, CD44, CD19, and BCL2L1, and uncovered that these five hub genes showed a tendency to be upregulated in both PCOS and asthma and possessed good diagnostic ability. In addition, we revealed that both PCOS and asthma were significantly enriched in the FcεRI-mediated signaling pathway. Moreover, we found that both PCOS and asthma exhibited infiltration of similar types of immune cells, such as monocytes, suggesting that the two diseases have similar pathological features.

CONCLUSION

PCOS and asthma share common causative genes with a similar immune environment. Taken together, we uncovered previously unsuspected traits for comprehensive diagnosis and treatment of PCOS and asthma in the future.

Biomarker screening using integrated bioinformatics for the development of "normal-impaired glucose intolerance-type 2 diabetes mellitus"

Type 2 diabetes mellitus (T2DM) is a progressive disease. We utilized bioinformatics analysis and experimental research to identify biomarkers indicative of the progression of T2DM, aiming for early detection of the disease and timely clinical intervention. Integrating Mfuzz analysis with differential expression analysis, we identified 76 genes associated with the progression of T2DM, which were primarily enriched in signaling pathways such as apoptosis, p53 signaling, and necroptosis. Subsequently, using various analytical methods, including machine learning, we further narrowed down the hub genes to STK17A and CCT5. Based on the hub genes, we calculated the risk score for samples and interestingly found that the score correlated with multiple programmed cell death (PCD) pathways. Animal experiments revealed that the diabetes model exhibited higher levels of MDA and LDH, with lower expression of SOD, accompanied by islet cell apoptosis. In conclusion, our study suggests that during the progression of diabetes, STK17A and CCT5 may contribute to the advancement of the disease by regulating oxidative stress, programmed cell death pathways, and critical signaling pathways such as p53 and MAPK, thereby promoting the death of islet cells. This provides substantial evidence in support of further disease prevention and treatment strategies.

n6-methyladenosine-modified circular RNA family with sequence similarity 126, member A affects cholesterol synthesis and malignant progression of prostate cancer cells by targeting microRNA-505-3p to mediate calnexin

Prostate cancer (PCa) is the most commonly diagnosed malignancy in men. In tumor biology, n6-methyladenosine (m6A) can mediate the production of circular RNAs (circRNAs). This study focused on the mechanism of m6A-modified circRNA family with sequence similarity 126, member A (FAM126A) in PCa. Cell counting kit-8 assay, colony formation assay, 5-ethynyl-2'-deoxyuridine assay, transwell assay, and xenograft mouse models were applied to study the role of circFAM126A in PCa cell growth and tumor metastasis, and cellular triglyceride and cholesterol levels were measured to assess cholesterol synthesis. RNA immunoprecipitation, RNA pull-down, luciferase reporter gene assay, and western blot were adopted to explore the underlying molecular mechanism. Data showed that circFAM126A was upregulated in PCa and promoted PCa progression in vitro. m6A modification of circFAM126A enhanced transcriptional stability. CircFAM126A targeted microRNA (miR)-505-3p to mediate calnexin (CANX). Up-regulating miR-505-3p or inhibiting CANX suppressed cholesterol synthesis and malignant progression in PCa cells. Overexpressing CANX suppressed the inhibitory effect of circFAM126A silencing or miR-505-3p upregulation on PCa cells. Our current findings provide a new therapeutic strategy for the treatment of PCa.

ANGPTL4 May Regulate the Crosstalk Between Intervertebral Disc Degeneration and Type 2 Diabetes Mellitus: A Combined Analysis of Bioinformatics and Rat Models

Introduction

The crosstalk between intervertebral disc degeneration (IVDD) and type 2 diabetes mellitus (T2DM) has been investigated. However, the common mechanism underlying this phenomenon has not been clearly elucidated. This study aimed to explore the shared gene signatures of IVDD and T2DM.

Methods

The expression profiles of IVDD (GSE27494) and T2DM (GSE20966) were acquired from the Gene Expression Omnibus database. Five hub genes including ANGPTL4, CCL2, CCN3, THBS2, and INHBA were preliminarily screened. GO (Gene Ontology) enrichment analysis, functional correlation analysis, immune filtration, Transcription factors (TFs)-mRNA-miRNA coregulatory network, and potential drugs prediction were performed following the identification of hub genes. RNA sequencing, in vivo and in vitro experiments on rats were further performed to validate the expression and function of the target gene.

Results

Five hub genes (ANGPTL4, CCL2, CCN3, THBS2, and INHBA) were identified. GO analysis demonstrated the regulation of the immune system, extracellular matrix (ECM), and SMAD protein signal transduction. There was a strong correlation between hub genes and different functions, including lipid metabolism, mitochondrial function, and ECM degradation. The immune filtration pattern grouped by disease and the expression of hub genes showed significant changes in the immune cell composition. TFs-mRNA-miRNA co-expression networks were constructed. In addition, pepstatin showed great drug-targeting relevance based on potential drugs prediction of hub genes. ANGPTL4, a gene that mediates the inhibition of lipoprotein lipase activity, was eventually determined after hub gene screening, validation by different datasets, RNA sequencing, and experiments.

Discussion

This study screened five hub genes and ANGPTL4 was eventually determined as a potential target for the regulation of the crosstalk in patients with IVDD and T2DM.

S100A6 is a potential diagnostic and prognostic biomarker for human glioma

S100 calcium-binding protein A6 (S100A6) is a protein that belongs to the S100 family. The present study aimed to investigate the function of S100A6 in the diagnosis and survival prediction of glioma and elucidated the potential processes affecting glioma development. The Cancer Genome Atlas database was searched to identify the relationship among S100A6 expression, immune cell infiltration, clinicopathological parameters and glioma prognosis. Several clinical cases were used to verify these findings. S100A6 gene expression was high in glioma tissues, suggesting its diagnostic significance. In particular, S100A6 upregulation in glioma tissues exhibited a significant and positive correlation with the World Health Organization (WHO) grade, histological type, age, sex, primary treatment outcomes, 1p/19q codeletion, isocitrate dehydrogenase (IDH) status, overall survival (OS), progression-free interval and disease-specific survival. Kaplan-Meier and Cox regression analyses revealed that S100A6 gene expression can independently function as a risk factor affecting the prognosis of patients with glioma. Furthermore, Gene Ontology functional enrichment analysis revealed that S100A6 is implicated in immune responses and that the expression profiles of S100A6 are linked to the immune microenvironment. Furthermore, immunohistochemistry revealed that increased S100A6 protein levels are correlated with age, 1p/19q codeletion, IDH status, WHO grade and OS. The present findings suggest that increased S100A6 expression is an indicator of the dismal prognosis of patients with glioma and that it can be used as a potential diagnostic biomarker for this condition.

Pan-cancer analysis and experimental validation identify ndc1 as a potential immunological, prognostic and therapeutic biomarker in pancreatic cancer

NDC1 is a transmembrane nucleoporin that participates in cell mitosis. In the field of oncology, NDC1 has shown its potential as a prognostic marker for multiple tumors. However, pan-cancer analysis of NDC1 to fully explore its role in tumors has not been performed and little is reported on its role in pancreatic cancers. In the present study, a pan-cancer analysis of NDC1 was performed using a bioinformatic approach. Survival analysis was performed by univariate Cox regression analysis and Kaplan-Meier survival analysis. Subsequently, the relationship between NDC1 and immune cell infiltration, TMB/MSI and drug sensitivity was analyzed. Moreover, the mechanism of NDC1 in pancreatic cancer were further analyzed by GSEA, GSVA. Finally, we conducted in vitro experiments including MTT, scratch, EdU, and apoptosis assays to explore the function of NDC1 in pancreatic cancer cells. High expression of NDC1 was demonstrated in 28 cancer types. Univariate Cox regression analysis revealed that NDC1 expression was closely associated with the survival outcome of 15 cancer types, and further Kaplan-Meier survival analysis showed negative associations with the progression-free survival in 14 cancers. In addition, a significant association between the NDC1 expression and immune cell infiltration in tumor microenvironment, immune-related genes, common tumor-regulatory and drug sensitivity was observed. Furthermore, NDC1 is abnormally expressed in pancreatic cancer, and is closely related to the prognosis of pancreatic cancer patients and chemosensitivity. The study reveals that NDC1 could be used as a potential immunological, prognostic and therapeutic target for pancreatic cancer.

The S100 calcium-binding protein A6 plays a crucial role in hepatic steatosis by mediating lipophagy

BACKGROUND

S100 calcium-binding protein A6 (S100A6) is a calcium-binding protein that is involved in a variety of cellular processes, such as proliferation, apoptosis, and the cellular response to various stress stimuli. However, its role in NAFLD and associated metabolic diseases remains uncertain.

METHODS AND RESULTS

In this study, we revealed a new function and mechanism of S100A6 in NAFLD. S100A6 expression was upregulated in human and mouse livers with hepatic steatosis, and the depletion of hepatic S100A6 remarkably inhibited lipid accumulation, insulin resistance, inflammation, and obesity in a high-fat, high-cholesterol (HFHC) diet-induced murine hepatic steatosis model. In vitro mechanistic investigations showed that the depletion of S100A6 in hepatocytes restored lipophagy, suggesting S100A6 inhibition could alleviate HFHC-induced NAFLD. Moreover, S100A6 liver-specific ablation mediated by AAV9 alleviated NAFLD in obese mice.

CONCLUSIONS

Our study demonstrates that S100A6 functions as a positive regulator of NAFLD, targeting the S100A6-lipophagy axis may be a promising treatment option for NAFLD and associated metabolic diseases.

Exploring the potential relationship between frozen shoulder and Dupuytren's disease through bioinformatics analysis and machine learning

Background

Frozen shoulder (FS) and Dupuytren's disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions.

Methods

GSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein-protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS.

Results

We identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration.

Conclusion

We identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.

Identification of Hub Genes and Biological Mechanisms Associated with Non-Alcoholic Fatty Liver Disease and Triple-Negative Breast Cancer

The relationship between non-alcoholic fatty liver disease (NAFLD) and triple-negative breast cancer (TNBC) has been widely recognized, but the underlying mechanisms are still unknown. The objective of this study was to identify the hub genes associated with NAFLD and TNBC, and to explore the potential co-pathogenesis and prognostic linkage of these two diseases. We used GEO, TCGA, STRING, ssGSEA, and Rstudio to investigate the common differentially expressed genes (DEGs), conduct functional and signaling pathway enrichment analyses, and determine prognostic value between TNBC and NAFLD. GO and KEGG enrichment analyses of the common DEGs showed that they were enriched in leukocyte aggregation, migration and adhesion, apoptosis regulation, and the PPAR signaling pathway. Fourteen candidate hub genes most likely to mediate NAFLD and TNBC occurrence were identified and validation results in a new cohort showed that ITGB2, RAC2, ITGAM, and CYBA were upregulated in both diseases. A univariate Cox analysis suggested that high expression levels of ITGB2, RAC2, ITGAM, and CXCL10 were associated with a good prognosis in TNBC. Immune infiltration analysis of TNBC samples showed that NCF2, ICAM1, and CXCL10 were significantly associated with activated CD8 T cells and activated CD4 T cells. NCF2, CXCL10, and CYBB were correlated with regulatory T cells and myeloid-derived suppressor cells. This study demonstrated that the redox reactions regulated by the NADPH oxidase (NOX) subunit genes and the transport and activation of immune cells regulated by integrins may play a central role in the co-occurrence trend of NAFLD and TNBC. Additionally, ITGB2, RAC2, and ITGAM were upregulated in both diseases and were prognostic protective factors of TNBC; they may be potential therapeutic targets for treatment of TNBC patients with NAFLD, but further experimental studies are still needed.

Roles of Pyroptosis-Related Genes in the Diagnosis and Subtype Classification of Periodontitis

Pyroptosis is widely involved in many diseases, including periodontitis. Nonetheless, the functions of pyroptosis-related genes (PRGs) in periodontitis are still not fully elucidated. Therefore, we aimed to investigate the role of PRGs in periodontitis. Three datasets (GSE10334, GSE16134, and GSE173078) from the Gene Expression Omnibus (GEO) were selected to analyze the differences in expression values of the PRGs between nonperiodontitis and periodontitis tissue samples using difference analysis. Following this, five hub PRGs (charged multivesicular body protein 2B, granzyme B, Z-DNA-binding protein 1, interleukin-1β, and interferon regulatory factor 1) predicting periodontitis susceptibility were screened by establishing a random forest model, and a predictive nomogram model was constructed on the basis of these genes. Decision curve analysis suggested that the PRG-based predictive nomogram model could provide clinical benefits to patients. Three distinct PRG patterns (cluster A, cluster B, and cluster C) in the periodontitis samples were revealed according to the 48 significant PRGs, and the difference in the immune cell infiltration among the three patterns was explored. We observed that all infiltrating immune cells, except type 2 T helper cells, differ significantly among the three patterns. To quantify the PRG patterns, the PRG score was calculated by principal component analysis. According to the results, cluster B had the highest PRG score, followed by cluster A and cluster C. In conclusion, PRGs significantly contribute to the development of periodontitis. Our study of PRG patterns might open up a new avenue to guide individualized treatment plans for patients with periodontitis.

Common gene signatures and molecular mechanisms of diabetic nephropathy and metabolic syndrome

Background

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. Multiple metabolic toxicities, redox stress, and endothelial dysfunction contribute to the development of diabetic glomerulosclerosis and DN. Metabolic syndrome (MetS) is a pathological state in which the body's ability to process carbohydrates, fats, and proteins is compromised because of metabolic disorders, resulting in redox stress and renal remodeling. However, a causal relationship between MetS and DN has not been proven. This study aimed to provide valuable information for the clinical diagnosis and treatment of MetS with DN.

Methods

Here, transcriptome data of DN and MetS patients were obtained from the Gene Expression Omnibus database, and seven potential biomarkers were screened using bioinformatics analysis. In addition, the relationship between these marker genes and metabolism and immune infiltration was explored. Among the identified marker genes, the relationship between PLEKHA1 and the cellular process, oxidative phosphorylation (OXPHOS), in DN was further investigated through single-cell analysis.

Results

We found that PLEKHA1 may represent an important biomarker that perhaps initiates DN by activating B cells, proximal tubular cells, distal tubular cells, macrophages, and endothelial cells, thereby inducing OXPHOS in renal monocytes.

Conclusion

Overall, our findings can aid in further investigation of the effects of drug treatment on single cells of patients with diabetes to validate PLEKHA1 as a therapeutic target and to inform the development of targeted therapies.

New insights into the inter-organ crosstalk mediated by ChREBP

Carbohydrate response element binding protein (ChREBP) is a glucose responsive transcription factor recognized by its critical role in the transcriptional control of glycolysis and de novo lipogenesis. Substantial advances in the field have revealed novel ChREBP functions. Indeed, due to its actions in different tissues, ChREBP modulates the inter-organ communication through secretion of peptides and lipid factors, ensuring metabolic homeostasis. Dysregulation of these orchestrated interactions is associated with development of metabolic diseases such as type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD). Here, we recapitulate the current knowledge about ChREBP-mediated inter-organ crosstalk through secreted factors and its physiological implications. As the liver is considered a crucial endocrine organ, we will focus in this review on the role of ChREBP-regulated hepatokines. Lastly, we will discuss the involvement of ChREBP in the progression of metabolic pathologies, as well as how the impairment of ChREBP-dependent signaling factors contributes to the onset of such diseases.

An immunogenic cell death-related regulators classification patterns and immune microenvironment infiltration characterization in intracranial aneurysm based on machine learning

Background

Immunogenic Cell Death (ICD) is a novel way to regulate cell death and can sufficiently activate adaptive immune responses. Its role in immunity is still emerging. However, the involvement of ICD in Intracranial Aneurysms (IA) remains unclear. This study aimed to identify biomarkers associated with ICDs and determine the relationship between them and the immune microenvironment during the onset and progression of IA

Methods

The IA gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IA were identified and the effects of the ICD on immune microenvironment signatures were studied. Techniques like Lasso, Bayes, DT, FDA, GBM, NNET, RG, SVM, LR, and multivariate analysis were used to identify the ICD gene signatures in IA. A consensus clustering algorithm was used for conducting the unsupervised cluster analysis of the ICD patterns in IA. Furthermore, enrichment analysis was carried out for investigating the various immune responses and other functional pathways. Along with functional annotation, the weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network and module construction, identification of the hub gene, and co-expression analysis were also carried out.

Results

The above techniques were used for establishing the ICD gene signatures of HMGB1, HMGN1, IL33, BCL2, HSPA4, PANX1, TLR9, CLEC7A, and NLRP3 that could easily distinguish IA from normal samples. The unsupervised cluster analysis helped in identifying three ICD gene patterns in different datasets. Gene enrichment analysis revealed that the IA samples showed many differences in pathways such as the cytokine-cytokine receptor interaction, regulation of actin cytoskeleton, chemokine signaling pathway, NOD-like receptor signaling pathway, viral protein interaction with the cytokines and cytokine receptors, and a few other signaling pathways compared to normal samples. In addition, the three ICD modification modes showed obvious differences in their immune microenvironment and the biological function pathways. Eight ICD-regulators were identified and showed meaningful associations with IA, suggesting they could severe as potential prognostic biomarkers.

Conclusions

A new gene signature for IA based on ICD features was created. This signature shows that the ICD pattern and the immune microenvironment are closely related to IA and provide a basis for optimizing risk monitoring, clinical decision-making, and developing novel treatment strategies for patients with IA.

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

Ovarian cancer (OvCa) is one of the most widespread malignant tumors, which has the highest morbidity and unsatisfactory clinical outcomes among all gynecological malignancies in the world. Previous studies found that cancer-associated fibroblasts (CAFs) play significant roles in tumor growth, progression, and chemoresistance. In the current research, weighted gene co-expression network analysis (WGCNA), univariable COX regression, and the least absolute shrinkage and selection operator (LASSO) analysis were applied to recognize CAF-specific genes. After multiple bioinformatic analyses, four genes (AXL, GPR176, ITGBL1, and TIMP3) were identified as OvCa-specific CAF markers and used to construct the prognostic signature (CAFRS). Furthermore, the specificity of the four genes' expression was further validated at the single-cell level, which was high-selectively expressed in CAFs. In addition, our results showed that CAFRS is an independent significant risk factor affecting the clinical outcomes of OvCa patients. Meanwhile, patients with higher CAFRS were more likely to establish chemoresistance to platinum. Besides, the CAFRS were notably correlated with well-known signal pathways that were related to tumor progression. In summary, our study identifies four CAF-specific genes and constructs a novel prognostic signature, which may provide more insights into precise prognostic assessment in OvCa.

Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease

Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis.

Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells.

Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells.

Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.

Exploration of the molecular mechanisms, shared gene signatures, and MicroRNAs between systemic lupus erythematosus and diffuse large B cell lymphoma by bioinformatics analysis

BACKGROUND

Systemic lupus erythematosus (SLE) is a complex heterogeneous systemic autoimmune disease. Previous studies have shown that SLE may be related to diffuse large B cell lymphoma (DLBCL), but the mechanism of their relationship is still unclear. The present study aimed to explore the common genetic molecular mechanisms, core shared genes, and miRNAs between SLE and DLBCL as well as to investigate the diagnostic markers of DLBCL.

METHODS

The SLE and DLBCL microarray data were downloaded from the comprehensive Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules. Four core shared genes were screened out by various algorithms and validated in other cohorts. Finally, we constructed a common core gene-miRNA network using the human microRNA disease database (HMDD) and TarBase.

RESULTS

Using WGCNA, four modules were identified as important modules for SLE and DLBCL. Enrichment analysis of the shared genes showed that the highly activated NF-κB pathway was a common feature of the pathophysiology. Four core shared genes, namely, PSMB10, PSMB4, TAF10, and NFΚBIA, were screened out. These core shared genes were significantly upregulated in both diseases, and they may be potential diagnostic markers of DLBCL. The core gene-miRNA network showed that miR-155-5p, regulating the shared NF-κB pathway, may play an important role in the susceptibility of SLE patients to DLBCL.

CONCLUSION

The present study revealed that NF-κB pathway in SLE may be a crucial susceptible factor for DLBCL. In addition, we identified PSMB10, PSMB4, TAF10, NFΚBIA and miR-155 involved in the common pathogenesis as potential biomarkers and therapeutic targets for DLBCL.

N(6)-methyladenosine-mediated miR-380-3p maturation and upregulation promotes cancer aggressiveness in pancreatic cancer

N(6)-methyladenosine (m6A)-modified microRNAs (miRNAs) are relevant to cancer progression. Also, although the involvement of miR-380-3p in regulating cancer progression in bladder cancer and neuroblastoma has been preliminarily explored, its role in other types of cancer, such as pancreatic cancer (PC), has not been studied. Thus, this study aimed to investigate the role of miR-380-3p in regulating PC progression. Here, through performing Real-Time qPCR, we evidenced that miR-380-3p was significantly upregulated in the clinical pancreatic cancer tissues and cells compared to their normal counterparts. Interestingly, miR-380-3p was enriched with m6A modifications, and elimination of m6A modifications by deleting METTL3 and METTL14 synergistically suppressed miR-380-3p expressions in PC cells. Next, the gain and loss-of-function experiments verified that knockdown of miR-380-3p suppressed cell proliferation, epithelial-mesenchymal transition (EMT), and tumorigenesis in PC cells in vitro and in vivo, whereas miR-380-3p overexpression had opposite effects. Furthermore, the underlying mechanisms were uncovered, and our data suggested that miR-380-3p targeted the 3' untranslated regions (3'UTRs) of PTEN for its inhibition and degradation, resulting in the activation of the downstream Akt signal pathway. Moreover, the rescuing experiments validated that both PTEN overexpression and Akt pathway inhibitor LY294002 abrogated the promoting effects of miR-380-3p overexpression on cancer aggressiveness in PC cells. Collectively, this study firstly investigated the role of the m6A-associated miR-380-3p/PTEN/Akt pathway in regulating PC progression, which provided novel therapeutic and diagnostic biomarkers for this cancer.

Construction and Analysis of a Joint Diagnosis Model of Random Forest and Artificial Neural Network for Obesity

Obesity is a significant global health concern since it is connected to a higher risk of several chronic diseases. As a consequence, obesity may be described as a condition that reduces human life expectancy and significantly impacts life quality. Because traditional obesity diagnosis procedures have several flaws, it is vital to design new diagnostic models to enhance current methods. More obesity-related markers have been discovered in recent years as a result of improvements and enhancements in gene sequencing technology. Using current gene expression profiles from the Gene Expression Omnibus (GEO) collection, we identified differentially expressed genes (DEGs) associated with obesity and found 12 important genes (CRLS1, ANG, ALPK3, ADSSL1, ABCC1, HLF, AZGP1, TSC22D3, F2R, FXN, PEMT, and SPTAN1) using a random forest classifier. ALPK3, HLF, FXN, and SPTAN1 are the only genes that have never been linked to obesity. We also used an artificial neural network to build a novel obesity diagnosis model and tested its diagnostic effectiveness using public datasets.