Investigating the causal association between obesity and risk of hepatocellular carcinoma and underlying mechanisms.

Obesity is a global health concern and independent risk factor for cancers including hepatocellular carcinoma (HCC). However, evidence on the causal links between obesity and HCC is limited and inconclusive. This study aimed to investigate the causal relationship between obesity-related traits and HCC risk and explore underlying mechanisms using bioinformatics approaches. Two-sample Mendelian randomization analysis was conducted leveraging publicly available genome-wide association study summary data on obesity traits (body mass index, body fat percentage, waist circumference, waist-to-hip ratio, visceral adipose tissue volume) and HCC. Associations of obesity with primary mechanisms (insulin resistance, adipokines, inflammation) and their effects on HCC were examined. Differentially expressed genes in obesity and HCC were identified and functional enrichment analyses were performed. Correlations with tumor microenvironment (TME) and immunotherapy markers were analyzed. Genetically predicted higher body mass index and body fat percentage showed significant causal relationships with increased HCC risk. Overall obesity also demonstrated causal links with insulin resistance, circulating leptin levels, C-reactive protein levels and risk of severe insulin resistant type 2 diabetes. Four differentially expressed genes (ESR1, GCDH, FAHD2A, DCXR) were common in obesity and HCC. Enrichment analyses indicated their roles in processes like RNA capping, viral transcription, IL-17 signaling and endocrine resistance. They exhibited negative correlations with immune cell infiltration and immunotherapy markers in HCC. Overall obesity likely has a causal effect on HCC risk in Europeans, possibly via influencing primary mechanisms. The identified differentially expressed genes may be implicated in obesity-induced hepatocarcinogenesis through regulating cell cycle, inflammation and immune evasion. Further research on precise mechanisms is warranted.

An immune biomarker associated with EMT serves as a predictor for prognosis and drug response in bladder cancer.

BACKGROUND: Bladder cancer (BLCA), which develops from the upper endometrial of the bladder, is the sixth most prevalent cancer across the globe. WDHD1 (WD repeat and HMG-box DNA binding protein 1 gene) directly affects signaling, the cell cycle, and the development of the cell skeleton. Uncertainty surrounds WDHD1's function in BLCA immunity and prognosis, though. MATERIALS AND METHODS: Using weighed gene co-expression network analysis (WGCNA), initially, we first identified 32 risk factors in genes with differential expression for this investigation. Then, using a variety of bioinformatic techniques and experimental validation, we examined the connections between WDHD1 and BLCA expression, clinical pathological traits, WDHD1-related proteins, upper-skin-intermediate conversion (EMT), immune cell immersion, convergence factors, immune markers, and drug sensitivity. RESULT: The findings demonstrated that we constructed a 32-gene risk-predicting model where WDHD1 was elevated as a representative gene expression in BLCA and related to a range of clinical traits. Furthermore, high WDHD1 expression was a standalone predictor associated with a worse survival rate. The most commonly recruited cells and their evolutionary patterns were highlighted to better comprehend WDHD1's function in cancer. High WDHD1 expression was associated with many aspects of immunology. Finally, the study found that individuals with high expression of WDHD1 were drug-sensitive to four different broad-spectrum anti-cancer drugs. CONCLUSION: These results describe dynamic changes in the tumor microenvironment in BLCA and provide evidence for the hypothesis that WDHD1 is a novel biomarker of tumor development. WDHD1 may therefore be a useful target for the detection and management of BLCA.

Corrigendum: Identification of an immune-related gene prognostic index for predicting survival and immunotherapy efficacy in papillary renal cell carcinoma.

[This corrects the article DOI: 10.3389/fgene.2022.970900.].

Cuproptosis-Related Genes as Prognostic Biomarkers for Sepsis: Insights into Immune Function and Personalized Immunotherapy.

Background: This study aimed to discover diagnostic and prognostic biomarkers for sepsis immunotherapy through analyzing the novel cellular death process, cuproptosis. Methods: We used transcriptome data from sepsis patients to identify key cuproptosis-related genes (CuRGs). We created a predictive model and used the CIBERSORT algorithm to observe the link between these genes and the septic immune microenvironment. We segregated sepsis patients into three subgroups, comparing immune function, immune cell infiltration, and differential analysis. Single-cell sequencing and real-time quantitative PCR were used to view the regulatory effect of CuRGs on the immune microenvironment and compare the mRNA levels of these genes in sepsis patients and healthy controls. We established a sepsis forecast model adapted to heart rate, body temperature, white blood cell count, and cuproptosis key genes. This was followed by a drug sensitivity analysis of cuproptosis key genes. Results: Our results filtered three key genes (LIAS, PDHB, PDHA1) that impact sepsis prognosis. We noticed that the high-risk group had poorer immune cell function and lesser immune cell infiltration. We also discovered a significant connection between CuRGs and immune cell infiltration in sepsis. Through consensus clustering, sepsis patients were classified into three subgroups. The best immune functionality and prognosis was observed in subgroup B. Single-cell sequencing exposed that the key genes manage the immune microenvironment by affecting T cell activation. The qPCR results highlighted substantial mRNA level reduction of the three key genes in the SP compared to the HC. The prediction model, which combines CuRGs and traditional diagnostic indicators, performed better in accuracy than the other markers. The drug sensitivity analysis listed bisphenol A as highly sensitive to all the key genes. Conclusion: Our study suggests these CuRGs may offer substantial potential for sepsis prognosis prediction and personalized immunotherapy.

Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis.

OBJECTIVES: To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value. METHODS: Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed to on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored by Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts. RESULTS: 14 DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion. CONCLUSION: A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

Comprehensive analysis of clinical and biological value of ING family genes in liver cancer.

BACKGROUND: Liver cancer (LIHC) is a malignant tumor that occurs in the liver and has a high mortality in cancer. The ING family genes were identified as tumor suppressor genes. Dysregulated expression of these genes can lead to cell cycle arrest, senescence and/or apoptosis. ING family genes are promising targets for anticancer therapy. However, their role in LIHC is still not well understood. AIM: To have a better understanding of the important roles of ING family members in LIHC. METHODS: A series of bioinformatics approaches (including gene expression analysis, genetic alteration analysis, survival analysis, immune infiltration analysis, prediction of upstream microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) of ING1, and ING1-related gene functional enrichment analysis) was applied to study the expression profile, clinical relationship, prognostic significance and immune infiltration of ING in LIHC. The relationship between ING family genes expression and tumor associated immune checkpoints was investigated in LIHC. The molecular mechanism of ING1 mediated hepatocarcinogenesis was preliminarily discussed. RESULTS: mRNA/protein expression of different ING family genes in LIHC was analyzed in different databases, showing that ING family genes were highly expressed in LIHC. In 47 samples from 366 LIHC patients, the ING family genes were altered at a rate of 13%. By comprehensively analyzing the expression, clinical pathological parameters and prognostic value of ING family genes, ING1/5 was identified. ING1/5 was related to poor prognosis of LIHC, suggesting that they may play key roles in LIHC tumorigenesis and progression. One of the target miRNAs of ING1 was identified as hsa-miR-214-3p. Two upstream lncRNAs of hsa-miR-214-3p, U91328.1, and HCG17, were identified. At the same time, we found that the expression of ING family genes was correlated with immune cell infiltration and immune checkpoint genes. CONCLUSION: This study lays a foundation for further research on the potential mechanism and clinical value of ING family genes in the treatment and prognosis of LIHC.

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

Background and Aims: 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. Methods: 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. Results: 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. Conclusion: 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.

Signature identification based on immunogenic cell death-related lncRNAs to predict the prognosis and immune activity of patients with endometrial carcinoma.

Background: Endometrial carcinoma (EC) is one of the most prevalent gynecologic malignancies and requires further classification for treatment and prognosis. Long non-coding RNAs (lncRNAs) and immunogenic cell death (ICD) play a critical role in tumor progression. Nevertheless, the role of lncRNAs in ICD in EC remains unclear. This study aimed to explore the role of ICD related-lncRNAs in EC via bioinformatics and establish a prognostic risk model based on the ICD-related lncRNAs. We also explored immune infiltration and immune cell function across prognostic groups and made treatment recommendations. Methods: A total of 552 EC samples and clinical data of 548 EC patients were extracted from The Cancer Genome Atlas (TCGA) database and University of California Santa Cruz (UCSC) Xena, respectively. A prognostic-related feature and risk model was developed using the least absolute shrinkage and selection operator (LASSO). Subtypes were classified with consensus cluster analysis and validated with t-Distributed Stochastic Neighbor Embedding (tSNE). Kaplan-Meier analysis was conducted to assess differences in survival. Infiltration by immune cells was estimated by single sample gene set enrichment analysis (ssGSEA), Tumor IMmune Estimation Resource (TIMER) algorithm. Quantitative polymerase chain reaction (qPCR) was used to detect lncRNAs expression in clinical samples and cell lines. A series of studies was conducted in vitro and in vivo to examine the effects of knockdown or overexpression of lncRNAs on ICD. Results: In total, 16 ICD-related lncRNAs with prognostic values were identified. Using SCARNA9, FAM198B-AS1, FKBP14-AS1, FBXO30-DT, LINC01943, and AL161431.1 as risk model, their predictive accuracy and discrimination were assessed. We divided EC patients into high-risk and low-risk groups. The analysis showed that the risk model was an independent prognostic factor. The prognosis of the high- and low-risk groups was different, and the overall survival (OS) of the high-risk group was lower. The low-risk group had higher immune cell infiltration and immune scores. Consensus clustering analysis divided the samples into four subtypes, of which cluster 4 had higher immune cell infiltration and immune scores. Conclusions: A prognostic signature composed of six ICD related-lncRNAs in EC was established, and a risk model based on this signature can be used to predict the prognosis of patients with EC.

Unveiling the therapeutic prospects of IFNW1 and IFNA21: insights into glioma pathogenesis and clinical significance.

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients.

Identification of NR3C2 as a functional diagnostic and prognostic biomarker and potential therapeutic target in non-small cell lung cancer.

Background: Non-small cell lung cancer (NSCLC), including the lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes, is a malignant tumor type with a poor 5-year survival rate. The identification of new powerful diagnostic biomarkers, prognostic biomarkers, and potential therapeutic targets in NSCLC is urgently required. Methods: The UCSC Xena, UALCAN, and GEO databases were used to screen and analyze differentially expressed genes, regulatory modes, and genetic/epigenetic alterations in NSCLC. The UCSC Xena database, GEO database, tissue microarray, and immunohistochemistry staining analyses were used to evaluate the diagnostic and prognostic values. Gain-of-function assays were performed to examine the roles. The ESTIMATE, TIMER, Linked Omics, STRING, and DAVID algorithms were used to analyze potential molecular mechanisms. Results: NR3C2 was identified as a potentially important molecule in NSCLC. NR3C2 is expressed at low levels in NSCLC, LUAD, and LUSC tissues, which is significantly related to the clinical indexes of these patients. Receiver operating characteristic curve analysis suggests that the altered NR3C2 expression patterns have diagnostic value in NSCLC, LUAD, and especially LUSC patients. Decreased NR3C2 expression levels can help predict poor prognosis in NSCLC and LUAD patients but not in LUSC patients. These results have been confirmed both with database analysis and real-world clinical samples on a tissue microarray. Copy number variation contributes to low NR3C2 expression levels in NSCLC and LUAD, while promoter DNA methylation is involved in its downregulation in LUSC. Two NR3C2 promoter methylation sites have high sensitivity and specificity for LUSC diagnosis with clinical application potential. NR3C2 may be a key participant in NSCLC development and progression and is closely associated with the tumor microenvironment and immune cell infiltration. NR3C2 co-expressed genes are involved in many cancer-related signaling pathways, further supporting a potentially significant role of NR3C2 in NSCLC. Conclusions: NR3C2 is a novel potential diagnostic and prognostic biomarker and therapeutic target in NSCLC.

Identification of diagnostic signature and immune infiltration for ischemic cardiomyopathy based on cuproptosis-related genes through bioinformatics analysis and experimental validation.

BACKGROUND: Ischemic cardiomyopathy (IC) is primarily due to long-term ischemia/hypoxia of the coronary arteries, leading to impaired cardiac contractile or diastolic function. A new form of cell death induced by copper, called "cuproptosis" is related to the development and progression of multiple diseases. The cuproptosis-related gene (CuGs) plays an important role in acute myocardial infarction, while the specific mechanisms of CuGs in ischemic cardiomyopathy remain unclear. METHODS: The expressions of CuGs and their immune characteristics were analyzed with the IC datasets obtained from the Gene Expression Omnibus, namely GSE5406 and GSE57338, identifying core genes associated with IC development. By comparing RF, SVM, GLM and XGB models, the optimal machine learning model was selected. The expression of marker genes was validated based on the GSE57345, GSE48166 and GSE42955 datasets. Construct a CeRNA network based on core genes. Therapeutic chemiacals targeting core genes were acquired using the CTD database, and molecular docking was performed using Autodock vina software. By ligating the left anterior descending (LAD) coronary artery, an IC mouse model is established, and core genes were experimentally validated using Western blot (WB) and immunohistochemistry (IHC) methods. RESULTS: We identified 14 CuGs closely associated with the onset of IC. The SVM model exhibited superior discriminative power (AUC = 0.914), with core genes being DLST, ATP7B, FDX1, SLC31A1 and DLAT. Core genes were validated on the GSE42955, GSE48166 and GSE57345 datasets, showing excellent performance (AUC = 0.943, AUC = 0.800, and AUC = 0.932). The CeRNA network consists of 218 nodes and 264 lines, including 5 core diagnostic genes, 52 miRNAs, and 161 lncRNAs. Chemicals predictions indicated 8 chemicals have therapeutic effects on the core diagnostic genes, with benzo(a)pyrene molecular docking showing the highest affinity (-11.3 kcal/mol). Compared to the normal group, the IC group,which was established by LAD ligation, showed a significant decrease in LVEF as indicated by cardiac ultrasound, and increased fibrosis as shown by MASSON staining, WB results suggest increased expression of DLST and ATP7B, and decreased expression of FDX1, SLC31A1 and DLAT in the myocardial ischemic area (p < 0.05), which was also confirmed by IHC in tissue sections. CONCLUSION: In summary, this study comprehensively revealed that DLST, ATP7B, FDX1, SLC31A1 and DLAT could be identified as potential immunological biomarkers in IC, and validated through an IC mouse model, providing valuable insights for future research into the mechanisms of CuGs and its diagnostic value to IC.

Immune cell related signature predicts prognosis in esophageal squamous cell carcinoma based on single-cell and bulk-RNA sequencing.

Background: It has been reported that tumor immune microenvironment performs a vital role in tumor progress. However, acting mechanism of immune cell related genes (IRGs) in esophageal squamous cell carcinoma (ESCC) is uncertain. Methods: TCGA-ESCC, GSE23400, GSE26886, GSE75241, and GSE196756 datasets were gained via public databases. First, differentially expressed genes (DEGs) between ESCC and control samples from GSE23400, GSE26886, and GSE75241 were screened out by differential expression analysis, and overlapping DEGs were identified. Single-cell transcriptome data of GSE196756 were applied to explore immune cells that might be involved in regulation of ESCC. Then, weighted gene co-expression network analysis was applied to screen IRGs. Next, differentially expressed IRGs (DE-IRGs) were identified by overlapping IRGs and DEGs, and were incorporated into univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox to acquire prognosis-related genes, and ESCC samples were grouped into high-/low-risk groups on the basis of median risk score. Finally, the role of prognosis model in immunotherapy was analyzed. Results: Totally 248 DEGs were yielded by overlapping 3,915 DEGs in GSE26886, 459 DEGs in GSE23400, and 1,641 DEGs in GSE75241. Single-cell analysis found that B cells, dendritic cells, monocytes, neutrophils, natural killer cells, and T cells were involved in ESCC development. Besides, MEred, MEblack, MEpink, MEblue and MEbrown modules were considered as key modules because of their highest correlations with immune cell subtypes. A total of 154 DE-IRGs were yielded by taking intersection of DEGs and genes in key modules. Moreover, CTSC, ALOX12, and RMND5B were identified as prognosis-related genes in ESCC. Obviously, Exclusion and TIDE scores were notably lower in high-risk group than in the other one, indicating that high-risk group was more responsive to immunotherapy. Conclusions: Through bioinformatic analysis, we identified a prognosis model consisting of IRGs (CTSC, ALOX12, and RMND5B) in ESCC, providing new ideas for studies related to treatment and prognosis of ESCC.

Regulatory and memory T lymphocytes infiltrating prostate tumors predict long term clinical outcomes.

Introduction: The localization, density but mostly the phenotype of tumor infiltrating lymphocytes (TIL) provide important information on the initial interaction between the host immune system and the tumor. Our objective was to assess the prognostic significance of T (CD3+), T regulatory (Treg) (FoxP3+) and T memory (Tmem) (CD45RO+) infiltrating lymphocytes and of genes associated with TIL in prostate cancer (PCa). Methods: Immunohistochemistry (IHC) was used to assess the infiltration of CD3+, FoxP3+ and CD45RO+ cells in the tumor area, tumor margin and adjacent normal-like epithelium of a series of 98 PCa samples with long clinical follow-up. Expression of a panel of 31 TIL-associated genes was analyzed by Taqman Low-Density Array (TLDA) technology in another series of 50 tumors with long clinical follow-up. Kaplan-Meier and Cox proportional hazards regression analyses were performed to determine association of these markers with biochemical recurrence (BCR), need for definitive androgen deprivation therapy (ADT) or lethal PCa. Results: TIL subtypes were present at different densities in the tumor, tumor margin and adjacent normal-like epithelium, but their density and phenotype in the tumor area were the most predictive of clinical outcomes. In multivariate analyses, a high density of Treg (high FoxP3+/CD3+ cell ratio) predicted a higher risk for need of definitive ADT (HR=7.69, p=0.001) and lethal PCa (HR=4.37, p=0.04). Conversely, a high density of Tmem (high CD45RO+/CD3+ cell ratio) predicted a reduced risk of lethal PCa (HR=0.06, p=0.04). TLDA analyses showed that a high expression of FoxP3 was associated with a higher risk of lethal PCa (HR=5.26, p=0.02). Expression of CTLA-4, PD-1, TIM-3 and LAG-3 were correlated with that of FoxP3. Amongst these, only a high expression of TIM-3 was associated with a significant higher risk for definitive ADT in univariate Cox regression analysis (HR=3.11, p=0.01). Conclusion: These results show that the proportion of Treg and Tmem found within the tumor area is a strong and independent predictor of late systemic progression of PCa. Our results also suggest that inhibition of TIM-3 might be a potential approach to counter the immunosuppressive functions of Treg in order to improve the anti-tumor immune response against PCa.

Multidimensional biological characteristics of ground glass nodules.

The detection rate of ground glass nodules (GGNs) has increased in recent years because of their malignant potential but relatively indolent biological behavior; thus, correct GGN recognition and management has become a research focus. Many scholars have explored the underlying mechanism of the indolent progression of GGNs from several perspectives, such as pathological type, genomic mutational characteristics, and immune microenvironment. GGNs have different major mutated genes at different stages of development; EGFR mutation is the most common mutation in GGNs, and p53 mutation is the most abundant mutation in the invasive stage of GGNs. Pure GGNs have fewer genomic alterations and a simpler genomic profile and exhibit a gradually evolving genomic mutation profile as the pathology progresses. Compared to advanced lung adenocarcinoma, GGN lung adenocarcinoma has a higher immune cell percentage, is under immune surveillance, and has less immune escape. However, as the pathological progression and solid component increase, negative immune regulation and immune escape increase gradually, and a suppressive immune environment is established gradually. Currently, regular computer tomography monitoring and surgery are the main treatment strategies for persistent GGNs. Stereotactic body radiotherapy and radiofrequency ablation are two local therapeutic alternatives, and systemic therapy has been progressively studied for lung cancer with GGNs. In the present review, we discuss the characterization of the multidimensional molecular evolution of GGNs that could facilitate more precise differentiation of such highly heterogeneous lesions, laying a foundation for the development of more effective individualized treatment plans.

Comprehensive pan­cancer analysis of MTDH for human tumor prognosis and as an immunological biomarker including breast and kidney cancer.

Metadherin (MTDH), initially discovered in primary astrocytes of the human fetus through rapid subtraction hybridization and labeled as astrocyte elevated gene-1, represents a widely recognized oncogene present in multiple types of cancers. However, the role of MTDH in different types of cancer remains unclear. To address this, a comprehensive analysis of MTDH across various types of cancers was conducted by utilizing multiple databases such as The Cancer Genome Atlas. The present analysis discovered that MTDH exhibits differential expression in different types of cancer and is associated with important factors including tumor mutational burden and microsatellite instability. These findings highlighted the significance of MTDH in the tumor microenvironment and its involvement in the development of immune cells in specific cancers. Furthermore, the results of the present study indicated that the expression of MTDH is strongly correlated with clinical prognosis, mutations and immune cell infiltration. MTDH could serve as a potential indicator of patient prognosis and potentially play a role in modulating the immune system. Given its potential as a novel immunological checkpoint, MTDH may be a viable target for tumor immunotherapy.

Increased FGF2 expression promotes immune cell infiltration and correlates with an unfavorable prognosis in thyroid cancer.

To delve into the expression and functions of FGF2 in patient with thyroid cancer (THCA), we conducted a systematic analysis of the association of FGF2 with immune cell infiltration, and prognosis in THCA patients. The transcription and protein levels, methylation, and biological properties of FGF2 were examined, along with its correlation with prognosis and immune cell infiltration in THCA patients using online databases UALCAN, Human Protein Atlas, Kaplan-Meier Plotter, DNMIVD, cBioPortal, GEPIA, Metascape, Linkedomics and TIMER. Clinical samples were collected for Western blot analyses. FGF2 was substantially overexpressed in the tumor group and shown correlations with age, tumor histology, nodal metastasis, and cancer stages. Moreover, higher expression of FGF2 (HR = 3.42, 95 % CI:1.57-7.44, p = 0.00099) was greatly correlated with poorer relapse-free survival in THCA patients, particularly in female patients. FGF2 methylation level was increased in the tumor group (p = 1.29E-6), and higher methylation levels of FGF2 were positively correlated with the poorer progression-free interval in THCA patients (p = 0.015). FGF2 mutations were markedly associated with shorter disease-free survival, with a mutation rate of 6 % among the total 498 THCA patients. FGF2 functions were potentially related to cell adhesion, cytokine-cytokine receptor interaction and angiogenesis. FGF2 expression showed positive correlations with the infiltration of B cells (Cor = 0.569, p = 1.04e-42), CD4+ T cells (Cor = 0.555, p = 9.43e-41), macrophages (Cor = 0.438, p = 2.94e-42), neutrophils (Cor = 0.578, p = 9.354e-45), and dendritic cells (Cor = 0.591, p = 5.00e-47). FGF2 is a potential prognostic marker in THCA patients, with its functions possibly related to cell adhesion, interaction of the cytokine-cytokine receptor, angiogenesis, and the promotion of multiple immune cell infiltration.

[Differential expressions of endoplasmic reticulum stress-associated genes in aortic dissection and their correlation with immune cell infiltration].

OBJECTIVE: To explore differentially expressed endoplasmic reticulum stress-associated genes (ERSAGs) in aortic dissection (AD) and their correlations with immune cell infiltration to identify new therapeutic targets for AD. METHODS: Two AD mRNA expression datasets (GSE190635 and GSE98770) were downloaded from GEO database for analysis of differentially expressed genes between the aorta of AD patients and normal aorta using R software. ERSAGs dataset was downloaded from GeneCards website, and GeneMANIA database was used to analyze the protein-protein interaction network of the differentially expressed ERSAGs and the proteins interacting with these genes. Based on GSE98770 dataset we analyzed the distributions of 22 immune cells within the aortic wall of AD patients using CIBERSORT package of R software. Surgical aortic wall specimens were obtained from 10 AD patients and 10 non-AD patients for detecting AGER mRNA expression using qRTPCR, and the upstream transcriptional factors, miRNAs, and chemicals targeting AGER were analyzed using the TRRUST database and NetworkAnalyst database. RESULTS: Bioinformatic analysis suggested significant differential expression of AGER in AD, which interacted with 20 proteins involved in pattern recognition receptor signaling pathway, positive regulation of DNA-binding transcription factor activity, myeloid leukocyte migration, leukocyte migration, and regulation of the I-κB kinase/NF-κB signaling. In AD, AGER expression level was positively correlated with Treg cell abundance (r=0.59, P < 0.05). The results of qRT-PCR demonstrated significantly lower expression of AGER mRNA in AD than in non-AD patients (1.00±0.30 vs 1.76±0.68, P < 0.05). ROC curve analysis showed that at the cut-off value of 1.335, AGER had an AUC of 0.86 (95% CI: 0.67-1.00, P= 0.0073) for predicting AD. Three transcriptional factors, 3 miRNAs, and 27 chemicals were predicted in the AGER regulatory network. CONCLUSION: AGER is lowly expressed in the aorta of AD patients and may influence the occurrence of AD through Treg cells.

[An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes].

OBJECTIVE: To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes. METHODS: The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes, and the differential genes were identified by Random forest, LASSO regression and SVM algorithms. Based on these differential genes, an artificial neural network model was constructed, and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807. The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma. The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers. RESULTS: A total of 24 differential genes were obtained, including 11 up-regulated and 13 down-regulated genes. Seven most relevant mitochondria-related genes (POLB, GSR, KRAS, NT5DC2, NOX4, IGF1, and TGM2) were screened using 3 machine learning algorithms, and the artificial neural network diagnostic model was constructed. The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis (0.740 for the verification dataset and 0.980 for cross-over validation). RT-qPCR detected significant up-regulation of POLB, GSR, KRAS, NOX4, IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma. Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells, immature B cells, resting dendritic cells, memory activated CD4+T cells, M0 macrophages, monocytes, resting memory CD4+T cells and mast cell activation. CONCLUSION: The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

A comprehensive analysis of GAS2 family members identifies that GAS2L1 is a novel biomarker and promotes the proliferation of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common primary liver cancer with a high incidence and mortality. Members of the growth-arresting-specific 2 (GAS2) family are involved in various biological processes in human malignancies. To date, there is only a limited amount of information available about the expression profile and clinical importance of GAS2 family in HCC. In this study, we found that GAS2L1 and GAS2L3 were distinctly upregulated in HCC specimens compared to non-tumor specimens. Pan-cancer assays indicated that GAS2L1 and GAS2L3 were highly expressed in most cancers. The Pearson's correlation revealed that the expressions of GAS2, GAS2L1 and GAS2L2 were negatively associated with methylation levels. Survival assays indicated that GAS2L1 and GAS2L3 were independent prognostic factors for HCC patients. Immune cell infiltration analysis revealed that GAS2, GAS2L1 and GAS2L3 were associated with several immune cells. Finally, we confirmed that GAS2L1 was highly expressed in HCC cells and its knockdown suppressed the proliferation of HCC cells. Taken together, our findings suggested the expression patterns and prognostic values of GAS2 members in HCC, providing insights for further study of the GAS2 family as sensitive diagnostic and prognostic markers for HCC.

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma.

Background: Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechanism of co-morbidity. This study aims to explore the key genes, pathways, and immune cells that may link psoriasis and cervical squamous cell carcinoma (CESC). Methods: The cervical squamous cell carcinoma dataset (GSE63514) was downloaded from the Gene Expression Omnibus (GEO). Two psoriasis-related datasets (GSE13355 and GSE14905) were merged into one comprehensive dataset after removing batch effects. Differentially expressed genes were identified using Limma and co-expression network analysis (WGCNA), and machine learning random forest algorithm (RF) was used to screen the hub genes. We analyzed relevant gene enrichment pathways using GO and KEGG, and immune cell infiltration in psoriasis and CESC samples using CIBERSORT. The miRNA-mRNA and TFs-mRNA regulatory networks were then constructed using Cytoscape, and the biomarkers for psoriasis and CESC were determined. Potential drug targets were obtained from the cMAP database, and biomarker expression levels in hela and psoriatic cell models were quantified by RT-qPCR. Results: In this study, we identified 27 key genes associated with psoriasis and cervical squamous cell carcinoma. NCAPH, UHRF1, CDCA2, CENPN and MELK were identified as hub genes using the Random Forest machine learning algorithm. Chromosome mitotic region segregation, nucleotide binding and DNA methylation are the major enrichment pathways for common DEGs in the mitotic cell cycle. Then we analyzed immune cell infiltration in psoriasis and cervical squamous cell carcinoma samples using CIBERSORT. Meanwhile, we used the cMAP database to identify ten small molecule compounds that interact with the central gene as drug candidates for treatment. By analyzing miRNA-mRNA and TFs-mRNA regulatory networks, we identified three miRNAs and nine transcription factors closely associated with five key genes and validated their expression in external validation datasets and clinical samples. Finally, we examined the diagnostic effects with ROC curves, and performed experimental validation in hela and psoriatic cell models. Conclusions: We identified five biomarkers, NCAPH, UHRF1, CDCA2, CENPN, and MELK, which may play important roles in the common pathogenesis of psoriasis and cervical squamous cell carcinoma, furthermore predict potential therapeutic agents. These findings open up new perspectives for the diagnosis and treatment of psoriasis and squamous cell carcinoma of the cervix.