Statistical predictions with glmnet

A hypoxia-derived gene signature to suggest cisplatin-based therapeutic responses in patients with cervical cancer

Cervical cancer remains a significant global public health concern, often exhibits cisplatin resistance in clinical settings. Hypoxia, a characteristic of cervical cancer, substantially contributes to cisplatin resistance. To evaluate the therapeutic efficacy of cisplatin in patients with cervical cancer and to identify potential effective drugs against cisplatin resistance, we established a hypoxia-inducible factor-1 (HIF-1)-related risk score (HRRS) model using clinical data from patients treated with cisplatin. Cox and LASSO regression analyses were used to stratify patient risks and prognosis. Through qRT-PCR, we validated nine potential prognostic HIF-1 genes that successfully predict cisplatin responsiveness in patients and cell lines. Subsequently, we identified fostamatinib, an FDA-approved spleen tyrosine kinase inhibitor, as a promising drug for targeting the HRRS-high group. We observed a positive correlation between the IC50 values of fostamatinib and HRRS in cervical cancer cell lines. Moreover, fostamatinib exhibited potent anticancer effects on high HRRS groups in vitro and in vivo. In summary, we developed a hypoxia-related gene signature that suggests cisplatin response prediction in cervical cancer and identified fostamatinib as a potential novel treatment approach for resistant cases.

Identification of COL3A1 as a candidate protein involved in the crosstalk between obesity and diarrhea using quantitative proteomics and machine learning

BACKGROUND

Increasing epidemiologic studies have shown a positive correlation between obesity and chronic diarrhea. Nevertheless, the precise etiology remains uncertain.

METHODS

We performed a comprehensive proteomics analysis utilizing the data-independent acquisition (DIA) technique on jejunal tissues from patients with obesity and chronic diarrhea (OD, n = 33), obese patients (OB, n = 10), and healthy controls (n = 8). Differentially expressed proteins (DEPs) in OD vs. control and OD vs. OB comparisons were subjected to pathway enrichment and protein-protein interaction (PPI) network analysis. Machine learning algorithms were adopted on overlapping DEPs in both comparisons. The candidate protein was further validated using Western blot, immunohistochemistry (IHC), and in vitro experiments.

RESULTS

We identified 189 and 228 DEPs in OD vs. control and OD vs. OB comparisons, respectively. DEPs in both comparisons were co-enriched in extracellular matrix (ECM) organization. Downregulated DEPs were associated with tight junction and ECM-receptor interaction in OD vs. control and OD vs. OB comparisons, respectively. Machine learning algorithms selected 3 proteins from 14 overlapping DEPs in both comparisons, among which collagen alpha-1(III) chain (COL3A1) was identified as a core protein in PPI networks. Western blot and IHC verified the expression of COL3A1. Moreover, the tight junction-related proteins decreased after the knockdown of COL3A1 in Caco2 intestinal cells upon PA challenge, consistent with the proteomics results.

CONCLUSIONS

We generated in-depth profiling of a proteomic dataset from samples of OD patients and provided unique insights into disease pathogenesis. COL3A1 was involved in the crosstalk between obesity and intestinal homeostasis via the ECM-receptor interaction pathway.

The Use of Identified Hypoxia-related Genes to Generate Models for Predicting the Prognosis of Cerebral Ischemia‒reperfusion Injury and Developing Treatment Strategies

Cerebral ischemia‒reperfusion injury (CIRI) is a type of secondary brain damage caused by reperfusion after ischemic stroke due to vascular obstruction. In this study, a CIRI diagnostic model was established by identifying hypoxia-related differentially expressed genes (HRDEGs) in patients with CIRI. The ischemia‒reperfusion injury (IRI)-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and hypoxia-related genes in the Gene Cards database were identified. After the datasets were combined, hypoxia-related differentially expressed genes (HRDEGs) expressed in CIRI patients were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the HRDEGs were performed using online tools. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed with the combined gene dataset. CIRI diagnostic models based on HRDEGs were constructed via least absolute shrinkage and selection operator (LASSO) regression analysis and a support vector machine (SVM) algorithm. The efficacy of the 9 identified hub genes for CIRI diagnosis was evaluated via mRNA‒microRNA (miRNA) interaction, mRNA-RNA-binding protein (RBP) network interaction, immune cell infiltration, and receiver operating characteristic (ROC) curve analyses. We then performed logistic regression analysis and constructed logistic regression models based on the expression of the 9 HRDEGs. We next established a nomogram and calibrated the prediction data. Finally, the clinical utility of the constructed logistic regression model was evaluated via decision curve analysis (DCA). This study revealed 9 critical genes with high diagnostic value, offering new insights into the diagnosis and selection of therapeutic targets for patients with CIRI. : Not applicable.

Gandouling ameliorates liver injury in Wilson's disease through the inhibition of ferroptosis by regulating the HSF1/HSPB1 pathway

Ferroptosis, an iron-dependent form of cell death, plays a crucial role in the progression of liver injury in Wilson's disease (WD). Gandouling (GDL) has emerged as a potential therapeutic agent for preventing and treating liver injury in WD. However, the precise mechanisms by which GDL mitigates ferroptosis in WD liver injury remain unclear. In this study, we discovered that treating Toxic Milk (TX) mice with GDL effectively decreased liver copper content, corrected iron homeostasis imbalances, and lowered lipid peroxidation levels, thereby preventing ferroptosis and improving liver injury. Bioinformatics analysis and machine learning algorithms identified Hspb1 as a pivotal regulator of ferroptosis. GDL treatment significantly upregulated the expression of HSPB1 and its upstream regulatory factor HSF1, thereby activating the HSF1/HSPB1 pathway. Importantly, inhibition of this pathway by NXP800 reversed the protective effects of GDL on ferroptosis in the liver of TX mice. In conclusion, GDL shows promise in alleviating liver injury in WD by inhibiting ferroptosis through modulation of the HSF1/HSPB1 pathway, suggesting its potential as a novel therapeutic agent for treating liver ferroptosis in WD.

Identification and Validation of IFI44 as a Novel Biomarker for Primary Sjögren's Syndrome

Background

Primary Sjögren's syndrome (pSS) is an autoimmune condition marked by lymphocyte infiltration in the exocrine glands. Our study aimed to identify a novel biomarker for pSS to improve its diagnosis and treatment.

Methods

The gene expression profiles of pSS were obtained from the Gene Expression Omnibus (GEO) database. The specific differentially expressed genes (DEGs) were screened by the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Recursive Feature Elimination with Support Vector Machines (SVM-RFE). A biomarker was picked out based on correlation and diagnostic performance, the connection between the biomarker and clinical traits and immune infiltrating cells was explored, and the biomarker's protein expression level in the serum of pSS patients was detected by enzyme-linked immunosorbent assay (ELISA). The competitive endogenous RNA (ceRNA) network regulated by the biomarker was predicted to verify the reliability of the biomarker in diagnosing pSS.

Results

IFI44, XAF1, GBP1, EIF2AK2, IFI27, and IFI6 showed prominent diagnostic ability, with the high accuracy (AUC = 0.859) and significance (R ≥ 0.8) of IFI44 within the training dataset. IFI44 strongly exhibited a negative correlation with resting NK cells, macrophages M0, and eosinophils, and a positive correlation with activated dendritic cells, naive B cells, and activated CD4 memory T cells. Furthermore, IFI44 was significantly positively correlated with clinical traits such as IgG, SSA, SSB, ANA, and ESSDAI, with its protein expression level in the serum of pSS patients being notably elevated compared to controls (p < 0.001). Finally, the ceRNA regulatory network showed that hsa-miR-944, hsa-miR-9-5p, hsa-miR-126-5p, and hsa-miR-335-3p were significantly targeted IFI44, suggesting that IFI44 may serve as a dependable biomarker for pSS.

Conclusion

In this study, we dug out IFI44 as a biomarker for pSS, systematically studied the potential regulatory mechanism of IFI44, and verified its reliability as a biomarker for pSS.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted the critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, NETotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis, in tumor development. Therefore, by investigating these pathways, we aimed to develop a predictive model for HCC prognosis and drug sensitivity. We analyzed transcriptome, single-cell transcriptome, genomic, and clinical information using data from the TCGA-LIHC, GSE14520, GSE45436, and GSE166635 datasets. Machine learning algorithms were used to establish a cell death index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates with poorer prognosis. Unsupervised clustering revealed three molecular subtypes of HCC with distinct biological processes. Furthermore, a nomogram integrating CDI and clinical information demonstrated good predictive performance. CDI was associated with immune checkpoint genes and tumor microenvironment components using single-cell transcriptome analysis. Drug sensitivity analysis indicated that patients with high CDI may be resistant to oxaliplatin and cisplatin but sensitive to axitinib and sorafenib. In summary, our model offers a precise prediction of clinical outcomes and drug sensitivity for patients with HCC, providing valuable insights for personalized treatment strategies.

Molecular classification to refine surgical and radiotherapeutic decision-making in meningioma

Treatment of the tumor and dural margin with surgery and sometimes radiation are cornerstones of therapy for meningioma. Molecular classifications have provided insights into the biology of disease; however, response to treatment remains heterogeneous. In this study, we used retrospective data on 2,824 meningiomas, including molecular data on 1,686 tumors and 100 prospective meningiomas, from the RTOG-0539 phase 2 trial to define molecular biomarkers of treatment response. Using propensity score matching, we found that gross tumor resection was associated with longer progression-free survival (PFS) across all molecular groups and longer overall survival in proliferative meningiomas. Dural margin treatment (Simpson grade 1/2) prolonged PFS compared to no treatment (Simpson grade 3). Molecular group classification predicted response to radiotherapy, including in the RTOG-0539 cohort. We subsequently developed a molecular model to predict response to radiotherapy that discriminates outcome better than standard-of-care classification. This study highlights the potential for molecular profiling to refine surgical and radiotherapy decision-making.

Prognosis and immunotherapy in melanoma based on selenoprotein k-related signature

Selenium and selenoproteins are closely related to melanoma progression. However, it is unclear how SELENOK affects lipid metabolism, endoplasmic reticulum stress (ERS), immune cell infiltration, survival, and prognosis in melanoma patients. Transcriptome data from melanoma patients was used to investigate SELENOK levels and their effect on prognosis, followed by an investigation of SELENOK's effects on immune cell infiltration. Furthermore, a risk model based on ERS, lipid metabolism, and immune-related genes was constructed, and its utility in melanoma prognosis was evaluated. Finally, the drug sensitivity of the risk model was analyzed to provide a reference for melanoma therapy. The results showed that melanoma with a high SELENOK level had a greater degree of immune cell infiltration and a better prognosis. Additionally, SELENOK was found to regulate ERS, lipid metabolism, and immune cell infiltration in melanoma. The risk model based on SELENOK signature genes successfully predicted the prognosis of melanoma, and the low-risk group exhibited a favorable immunological microenvironment. Furthermore, high-risk patients with melanoma were candidates for chemotherapy with RAS pathway inhibitors, whereas low-risk patients were more susceptible to routinely used chemotherapy medicines. In summary, SELENOK was shown to regulate ERS, lipid metabolism, and immune cell infiltration in melanoma, and SELENOK was positively associated with the prognosis of melanoma. The risk model based on SELENOK signature genes was valuable for melanoma prognosis and therapy.

Construction of a mitophagy-related prognostic signature for predicting prognosis and tumor microenvironment in lung adenocarcinoma

Background

Mitophagy is the selective degradation of mitochondria by autophagy. It becomes increasingly clear that mitophagy pathways are important for cancer cells to adapt to their high-energy needs. However, which genes associated with mitophagy could be used to prognosis cancer is unknown.

Methods

We created a clinical prognostic model using mitophagy-related genes (MRGs) in lung adenocarcinoma (LUAD) patients for the first time, and we employed bioinformatics methods to search for biomarkers that affect the progression and prognosis of LUAD. Transcriptome data for LUAD were obtained from The Cancer Genome Atlas (TCGA) database, and additional expression data from LUAD patients were sourced from the Gene Expression Omnibus (GEO) database. Furthermore, 25 complete MRGs were identified based on annotations from the MSigDB database.

Results

A comparison of the mitophagy scores between the groups with high and low scores was done using receiver operating characteristic (ROC) curves, which also revealed the differential gene expression patterns between the two groups. Using Kaplan-Meier analysis, two prognostic MRGs from the groups with high and low mitophagy scores were identified: TOMM40 and VDAC1. Using univariate and multivariate Cox regression, the relationship between the expression levels of these two genes and prognostic clinical features of LUAD was examined further.The prognosis of LUAD patients was shown to be significantly correlated (P < 0.05) with the expression levels of these two genes.

Conclusions

Our prognostic model would improve the prognosis of LUAD and guide clinical treatments.

Unraveling the relationship between anoikis-related genes and cancer-associated fibroblasts in liver hepatocellular carcinoma

This study intended to determine the molecular subtypes of liver hepatocellular carcinoma (LIHC) on the strength of anoikis-related genes (ARGs) and to assess their prognostic value and prospective relationship with immune cell infiltration and cancer-associated fibroblasts (CAFs). Univariate Cox regression analysis yielded 66 prognosis-related ARGs and classified LIHC into two distinct subtypes, with subtype A demonstrating overexpression of most prognosis-related ARGs and a significant survival disadvantage. Furthermore, a reliable prediction model was developed using ARGs to evaluate the risk of LIHC patients. This model served as an independent prognostic indicator and a quantitative tool for clinical prognostic prediction. Additionally, subtype-specific differences in immune cell infiltration were observed, and the risk score was potentially linked to immune-related characteristics. Moreover, the study identified a significant association between CAF score and LIHC prognosis, with a low CAF score indicating a favorable patient prognosis. In conclusion, this study reveals the molecular mechanisms underlying the development and progression of LIHC and identifies potential therapeutic targets for the disease.

Integrating bulk and single-cell sequencing reveals metastasis-associated CAFs in head and neck squamous cell carcinoma

AIMS

Cancer-associated fibroblasts (CAFs) have been shown to promote the metastasis of head and neck squamous cell carcinoma (HNSCC), but the underlying mechanisms remain unclear. The purpose of this study is to identify gene in CAFs that are associated with metastasis and to preliminarily validate its impact on the metastasis of HNSCC.

MATERIALS AND METHODS

Scissor analysis was utilized to process single-cell and bulk RNA sequencing datasets, identifying genes associated with the metastasis of HNSCC through differential gene expression analysis. A risk model was constructed using LASSO regression analysis. Quantitative real time-PCR and Western blot were employed to measure mRNA and protein expressions, respectively. Multiplex immunohistochemistry (mIHC) was used to assess protein expression in CAFs. siRNA was utilized to achieve gene knockdown. CCK-8 and Transwell assays were employed to evaluate the biological characteristics of HNSCC cells.

KEY FINDINGS

Compare to the nonmetastatic primary CAFs (nmCAFs), tissue inhibitors of metalloproteinase-1 (TIMP1) was founded to be overexpressed in the cells and tissues of metastatic primary CAFs (mCAFs). Knocking down TIMP1 in CAFs can signifucantly inhibit the proliferation, invasion, and migration of HNSCC cells.

SIGNIFICANCE

CAFs facilitate HNSCC cell metastasis by upregulating TIMP1, highlighting TIMP1 as a potential therapeutic target in HNSCC metastasis management.

Immune infiltration analysis based on pyroptosis-related gene in metabolic dysfunction-associated fatty liver disease

Introduction

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a prevalent chronic disease that can involve pyroptosis. The primary objective of this study was to conduct a thorough and comprehensive analysis the pyroptosis-related genes in MAFLD.

Methods

We identified pyroptosis-related differentially expressed genes (PRDEGs) in both healthy individuals and MAFLD patients. Using various bioinformatic approaches, we conducted an immune infiltration analysis from multiple perspectives.

Results

A total of 20 pyroptosis-related LASSO genes were obtained, and 10 hub genes were used to do immune infiltration analysis. The hub genes were utilized in the construction of interaction networks between mRNA-miRNA and mRNA-TF. Immune characteristics analysis revealed multiple immune cell types significantly related to PRDEG expression, particularly genes HSP90AA1, TSLP, CDK9, and BRD4.

Conclusion

Pyroptosis-related immune infiltration might be a mechanism of MAFLD progression and offers a research direction for potential treatment techniques.

Identification and validation of Rab GTPases RAB13 as biomarkers for peritoneal metastasis and immune cell infiltration in colorectal cancer patients

Background

As one of the most common cancer, colorectal cancer (CRC) is with high morbidity and mortality. Peritoneal metastasis (PM) is a fatal state of CRC, and few patients may benefit from traditional therapies. There is a complex interaction between PM and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with colorectal cancer peritoneal metastasis (CRCPM) and their relationship with immune cell infiltration.

Methods

By informatic analysis, differently expressed genes (DEGs) were selected and hub genes were screened out. RAB13, one of the hub genes, was identificated from public databases and validated in CRC tissues. The ESTIMATE, CEBERSORT and TIMER algorithms were applied to analyze the correlation between RAB13 and immune infiltration in CRC. RAB13's expression in different cells were analyzed at the single-cell level in scRNA-Seq. The Gene Set Enrichment Analysis (GSEA) was performed for RAB13 enrichment and further confirmed. Using oncoPredict algorithm, RAB13's impact on drug sensitivity was evaluated.

Results

High RAB13 expression was identified in public databases and led to a poor prognosis. RAB13 was found to be positively correlated with the macrophages and other immune cells infiltration and from scRNA-Seq, RAB13 was found to be located in CRC cells and macrophages. GSEA revealed that high RAB13 expression enriched in a various of biological signaling, and oncoPredict algorithm showed that RAB13 expression was correlated with paclitaxel sensitivity.

Conclusion

Our study indicated clinical role of RAB13 in CRC-PM, suggesting its potential as a therapeutic target in the future.

Development and validation of a hypoxia- and mitochondrial dysfunction- related prognostic model based on integrated single-cell and bulk RNA sequencing analyses in gastric cancer

Introduction

Gastric cancer (GC) remains a major global health threat ranking as the fifth most prevalent cancer. Hypoxia, a characteristic feature of solid tumors, significantly contributes to the malignant progression of GC. Mitochondria are the major target of hypoxic injury that promotes mitochondrial dysfunction during the development of cancers including GC. However, the gene signature and prognostic model based on hypoxia- and mitochondrial dysfunction-related genes (HMDRGs) in the prediction of GC prognosis have not yet been established.

Methods

The gene expression profile datasets of stomach cancer patients were retrieved from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Prognostic genes were selected using Least Absolute Shrinkage and Selection Operator Cox (LASSO-Cox) regression analysis to construct a prognostic model. Immune infiltration was evaluated through ESTIMATE, CIBERSORT, and ssGSEA analyses. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) were utilized to explore implications for immunotherapy. Furthermore, in vitro experiments were conducted to validate the functional roles of HMDRGs in GC cell malignancy.

Results

In this study, five HMDRGs (ZFP36, SERPINE1, DUSP1, CAV1, and AKAP12) were identified for developing a prognostic model in GC. This model stratifies GC patients into high- and low-risk groups based on median risk scores. A nomogram predicting overall survival (OS) was constructed and showed consistent results with observed OS. Immune infiltration analysis indicated that individuals in the high-risk group tend to exhibit increased immune cell infiltration. Additionally, analysis of cancer immunotherapy responses revealed that high-risk group patients exhibit poorer responses to cancer immunotherapy compared to the low-risk group. Immunohistochemistry (IHC) staining indicated that the expression levels of HMDRGs were remarkably correlated with GC, of which, SERPINE1 displayed the most pronounced up-regulation, while ZFP36 exhibited the most notable down-regulation in GC patients. Furthermore, in vitro investigation validated that SERPINE1 and ZFP36 contribute to the malignant processes of GC cells correlated with mitochondrial dysfunction.

Conclusions

This study presents a novel and efficient approach to evaluate GC prognosis and immunotherapy efficacy, and also provides insights into understanding the pathogenesis of GC.

Plasma Proteomics to Identify Drug Targets and Potential Drugs for Retinal Artery Occlusion: An Integrated Analysis in the UK Biobank

Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.

Computational identification of mitochondrial dysfunction biomarkers in severe SARS-CoV-2 infection: Facilitating therapeutic applications of phytomedicine

BACKGROUND

Currently, SARS-CoV-2 has not disappeared and continues to prevail worldwide, with the ongoing risk of mutations and the potential for severe COVID-19. The impairment of monocyte mitochondrial function caused by SARS-CoV-2, leading to a metabolic and immune dysregulation, is a crucial factor in the development of severe COVID-19.

PURPOSE

Discover effective phytomedicines based on mitochondrial-related biomarkers in severe SARS-CoV-2 infection.

METHODS

Firstly, differential gene analysis and gene set enrichment analysis (GSEA) were conducted on monocytes datasets to identify genes and pathways distinguishing severe patients from uninfected individuals. Then, GO and KEGG enrichment analysis on the differentially expressed genes (DEGs) obtained. Take the DEGs and intersect them with the MitoCarta 3.0 gene set to obtain the differentially expressed mitochondrial-related genes (DE-MRGs). Subsequently, machine learning algorithms were employed to screen potential mitochondrial dysfunction biomarkers for severe COVID-19 based on score values. ROC curves were then plotted to assess the distinguish capability of the biomarkers, followed by validation using two additional independent datasets. Next, the effects of the identified biomarkers on metabolic pathways and immune cells were explored through Gene Set Variation Analysis (GSVA) and CIBERSORT. Finally, potential nature products for severe COVID-19 were screened from the expression profile dataset based on dysregulated mitochondrial-related genes, followed by in vitro experimental validation.

RESULTS

There are 1812 DEGs and 17 dysregulated mitochondrial processes between severe COVID-19 patients and uninfected individuals. A total of 77 DE-MRGs were identified, and the potential biomarkers were identified as RECQL4, PYCR1, PIF1, POLQ, and GLDC. In both the training and validation sets, the area under the ROC curve (AUC) for these five biomarkers was greater than 0.9. And they did not show significant changes in mild to moderate patients (p > 0.05), indicating their ability to effectively distinguish severe COVID-19. These biomarkers exhibit a highly significant correlation with the dysregulated metabolic processes (p < 0.05) and immune cell imbalance (p < 0.05) in severe patients, as demonstrated by GSVA and CIBERSORT algorithms. Curcumin has the highest score in the predictive model based on transcriptomic data from 496 natural compounds (p = 0.02; ES = 0.90). Pre-treatment with curcumin for 8 h has been shown to alleviate mitochondrial membrane potential damage caused by the SARS-CoV-2 S1 protein (p < 0.05) and reduce elevated levels of reactive oxygen species (ROS) (p < 0.01).

CONCLUSION

The results of this study indicate a significant correlation between severe SARS-CoV-2 infection and mitochondrial dysfunction. The proposed mitochondrial dysfunction biomarkers identified in this study are associated with the disease progression, metabolic and immune changes in severe SARS-CoV-2 infected patients. Curcumin has a potential role in preventing severe COVID-19 by protecting mitochondrial function. Our findings provide new strategies for predicting the prognosis and enabling early intervention in SARS-CoV-2 infection.

Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis

BACKGROUND

Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.

METHOD

We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.

RESULTS

Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.

CONCLUSIONS

Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.

STAT3-Mediated Ferroptosis is Involved in Sepsis-Associated Acute Respiratory Distress Syndrome

Sepsis-induced acute respiratory distress syndrome (ARDS) poses a grave danger to life, resulting from sepsis-induced multi-organ failure. Although ferroptosis, a form of iron-dependent lipid peroxidative cell death, has been associated with sepsis-induced ARDS, the specific mechanisms are not fully understood. In this study, we utilized WGCNA, PPI, friends analysis, and six machine learning techniques (Lasso, SVM, RFB, XGBoost, AdaBoost, and LightGBM) to pinpoint STAT3 as a potential diagnostic marker. A significant increase in monocyte and neutrophil levels was observed in patients with sepsis-induced ARDS, as revealed by immune infiltration analyses, when compared to controls. Moreover, there was a positive correlation between STAT3 expression and the level of infiltration. Single-cell analysis uncovered a notable disparity in B-cell expression between sepsis and sepsis-induced ARDS. Furthermore, in vitro experiments using LPS-treated human bronchial epithelial cells (BEAS-2B) and THP1 cells demonstrated a significant increase in STAT3 phosphorylation expression. Additionally, the inhibition of STAT3 phosphorylation by Stattic effectively prevented LPS-induced ferroptosis in both BEAS-2B and THP1 cells. This indicates that the activation of STAT3 phosphorylation promotes ferroptosis in human bronchial epithelial cells in response to LPS. In summary, this research has discovered and confirmed STAT3 as a potential biomarker for the diagnosis and treatment of sepsis-induced ARDS.

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning

OBJECTIVE

Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC.

METHODS

Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm.

RESULTS

The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways.

CONCLUSION

In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

Telomere-related gene risk model for prognosis prediction in colorectal cancer

Background

Colorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.

Methods

We retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.

Results

The model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4+ T cells, effector memory CD4+ T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature's capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (ACACB, TPX2, SRPX, PPARGC1A, CD36, MMP3, NAT2, MMP10, HIGD1A, and MMP1) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of ACACB, HIGD1A, NAT2, PPARGC1A, and TPX2 in CRC cells were elevated, whereas those of CD36, SRPX, MMP1, MMP3, and MMP10 were reduced.

Conclusions

Overall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.

Immune infiltration and prognosis in gastric cancer: role of NAD+ metabolism-related markers

Background

This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients.

Methods

In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes.

Results

Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples.

Conclusion

In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients.

A cuproptosis-related lncRNAs signature predicts prognosis and reveals pivotal interactions between immune cells in colon cancer

Copper-mediated cell death presents distinct pathways from established apoptosis processes, suggesting alternative therapeutic approaches for colon cancer. Our research aims to develop a predictive framework utilizing long-noncoding RNAs (lncRNAs) related to cuproptosis to predict colon cancer outcomes while examining immune interactions and intercellular signaling. We obtained colon cancer-related human mRNA expression profiles and clinical information from the Cancer Genome Atlas repository. To isolate lncRNAs involved in cuproptosis, we applied Cox proportional hazards modeling alongside the least absolute shrinkage and selection operator technique. We elucidated the underlying mechanisms by examining the tumor mutational burden, the extent of immune cell penetration, and intercellular communication dynamics. Based on the model, drugs were predicted and validated with cytological experiments. A 13 lncRNA-cuproptosis-associated risk model was constructed. Two colon cancer cell lines were used to validate the predicted representative mRNAs with high correlation coefficients with copper-induced cell death. Survival enhancement in the low-risk cohort was evidenced by the trends in Kaplan-Meier survival estimates. Analysis of immune cell infiltration suggested that survival was induced by the increased infiltration of naïve CD4+ T cells and a reduction of M2 macrophages within the low-risk faction. Decreased infiltration of naïve B cells, resting NK cells, and M0 macrophages was significantly associated with better overall survival. Combined single-cell analysis suggested that CCL5-ACKR1, CCL2-ACKR1, and CCL5-CCR1 pathways play key roles in mediating intercellular dialogues among immune constituents within the neoplastic microhabitat. We identified three drugs with a high sensitivity in the high-risk group. In summary, this discovery establishes the possibility of using 13 cuproptosis-associated lncRNAs as a risk model to assess the prognosis, unravel the immune mechanisms and cell communication, and improve treatment options, which may provide a new idea for treating colon cancer.

Integrative analyses of genes associated with oxidative stress and cellular senescence in triple-negative breast cancer

Background

Oxidative stress and cellular senescence (OSCS) have great impacts on the occurrence and progression of triple-negative breast cancer (TNBC). This study was intended to construct a prognostic model based on oxidative stress and cellular senescence related difference expression genes (OSCSRDEGs) for TNBC.

Methods

The Cancer Genome Atlas (TCGA) databases and two Gene Expression Omnibus (GEO) databases were used to identify OSCSRDEGs. The relationship between OSCSRDEGs and immune infiltration was examined using single-sample gene-set enrichment analysis (ssGSEA), ESTIMATE, and the CIBERSORT algorithm. Least absolute shrinkage and selection operator (LASSO) regression analyses, Cox regression and Kaplan-Meier analysis were employed to construct a prognostic model. Receiver operating characteristic (ROC) curves, nomograms, and decision curve analysis (DCA) were used to evaluate the prognostic efficacy. Gene Set Enrichment Analysis (GSEA) Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the potential functions and mechanism.

Results

A comprehensive analysis identified a total of 27 OSCSRDEGs, out of which 15 genes selected for development of a prognostic model. A high degree of statistical significance was observed for the riskscores derived from this model to accurately predict TNBC Overall survival. The decision curve analysis (DCA) and ROC curve analysis further confirmed the superior accuracy of the OSCSRDEGs prognostic model in predicting efficacy. Notably, the nomogram analysis highlighted that DMD exhibited the highest utility within the model. In comparison between high and low OSCScore groups, the infiltration abundance of immune cells was statistically different in the TCGA-TNBC dataset.

Conclusion

These studies have effectively identified four essential OSCSRDEGs (CFI, DMD, NDRG2, and NRP1) and meticulously developed an OSCS-associated prognostic model for individuals diagnosed with TNBC. These discoveries have the potential to significantly contribute to the comprehension of the involvement of OSCS in TNBC.

Single-Cell RNA-seq Analysis Reveals a Positive Correlation between Ferroptosis and Beta-Cell Dedifferentiation in Type 2 Diabetes

Insulin deficiency in patients with type 2 diabetes mellitus (T2D) is associated with beta-cell dysfunction, a condition increasingly recognized to involve processes such as dedifferentiation and apoptosis. Moreover, emerging research points to a potential role for ferroptosis in the pathogenesis of T2D. In this study, we aimed to investigate the potential involvement of ferroptosis in the dedifferentiation of beta cells in T2D. We performed single-cell RNA sequencing analysis of six public datasets. Differential expression and gene set enrichment analyses were carried out to investigate the role of ferroptosis. Gene set variation and pseudo-time trajectory analyses were subsequently used to verify ferroptosis-related beta clusters. After cells were categorized according to their ferroptosis and dedifferentiation scores, we constructed transcriptional and competitive endogenous RNA networks, and validated the hub genes via machine learning and immunohistochemistry. We found that ferroptosis was enriched in T2D beta cells and that there was a positive correlation between ferroptosis and the process of dedifferentiation. Upon further analysis, we identified two beta clusters that presented pronounced features associated with ferroptosis and dedifferentiation. Several key transcription factors and 2 long noncoding RNAs (MALAT1 and MEG3) were identified. Finally, we confirmed that ferroptosis occurred in the pancreas of high-fat diet-fed mice and identified 4 proteins (NFE2L2, CHMP5, PTEN, and STAT3) that may participate in the effect of ferroptosis on dedifferentiation. This study helps to elucidate the interplay between ferroptosis and beta-cell health and opens new avenues for developing therapeutic strategies to treat diabetes.

A m6A regulators-related classifier for prognosis and tumor microenvironment characterization in hepatocellular carcinoma

Background

Increasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification.

Method

RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC).

Results

Two distinct m6A modification patterns were identified based on 23 m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit.

Conclusion

Our findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients.

The potential of cancer stem cells for personalized risk assessment and therapeutic intervention in individuals with intrahepatic cholangiocarcinoma

BACKGROUND

Accumulating evidence suggests that intrahepatic cholangiocarcinoma (ICC) is a stem cell-based disease, but information on the biology of cancer stem cells (CSC) in ICC is very limited.

METHODS

ICC RNA-seq cohorts from three different public databases were integrated and the protein-coding genes were divided into different modules using "WGCNA" to screen the most relevant modules with CSC scores. Least Absolute Shrinkage and Selection Operator (LASSO) regression were introduced to construct prognostic classification models. In addition, the extent of immune cell infiltration in patients in different risk groups was assessed based on the ESTIMATE, CIBERSORT, MCP-Counter, and single sample gene set enrichment analysis (ssGSEA) algorithms. Finally, the correlation between different risk scores and common drugs was analyzed by pRRophetic package and Spearman method.

RESULTS

In the present study, we found that a high CSC score was associated with a poorer prognosis in patients with ICC. The yellow module obtained by WGCNA was significantly positively correlated with the CSCs score, in which 8 genes were served to build a prognostic classification model, and the obtained risk score was negatively correlated with CSCs score and prognosis. The low-risk score was more suitable for immunotherapy, and the high-risk score was more suitable for treatment with 11 antitumor drugs.

CONCLUSION

This study revealed the regulatory role of CSC-mediated EMT, angiogenesis, and immunomodulatory biological processes in ICC, and applied a prognostic classification model to highlight the great potential of CSC for personalized risk assessment, chemotherapy, and immunotherapy intervention in ICC individuals.

Identification of MAP1LC3A as a promising mitophagy-related gene in polycystic ovary syndrome

Increasing evidence suggests that mitophagy is crucially involved in the progression of polycystic ovary syndrome (PCOS). Exploration of PCOS-specific biomarkers related to mitophagy is expected to provide critical insights into disease pathogenesis. In this study, we employed bioinformatic analyses and machine learning algorithms to determine novel biomarkers for PCOS that may be tied with mitophagy. A grand total of 12 differential expressed mitophagy-related genes (DE-MRGs) associated with PCOS were identified. TOMM5 and MAP1LC3A among the 12 DE-MRGs were recognized as potential marker genes by LASSO, RF and SVM-RFE algorithms. The area under the ROC curve (AUROC) of MAP1LC3A were all greater than 0.8 both in the training set and validation sets. The CIBERSORT analysis indicated a potential association between alterations in the immune microenvironment of PCOS individuals and MAP1LC3A expression. In addition, we found that MAP1LC3A was positively related to the testosterone levels of PCOS patients. Overall, MAP1LC3A was identified as optimal PCOS-specific biomarkers related to mitophagy. Our findings created a diagnostic strength and offered a perspective for investigating the mitophagy process in PCOS.

Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy

BACKGROUND

Non-small cell lung cancer (NSCLC) is the primary form of lung cancer, and the combination of chemotherapy with immunotherapy offers promising treatment options for patients suffering from this disease. However, the emergence of drug resistance significantly limits the effectiveness of these therapeutic strategies. Consequently, it is imperative to devise methods for accurately detecting and evaluating the efficacy of these treatments.

AIM

To identify the metabolic signatures associated with neutrophil extracellular traps (NETs) and chemoimmunotherapy efficacy in NSCLC patients.

METHODS

In total, 159 NSCLC patients undergoing first-line chemoimmunotherapy were enrolled. We first investigated the characteristics influencing clinical efficacy. Circulating levels of NETs and cytokines were measured by commercial kits. Liquid chromatography tandem mass spectrometry quantified plasma metabolites, and differential metabolites were identified. Least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest algorithms were employed. By using plasma metabolic profiles and machine learning algorithms, predictive metabolic signatures were established.

RESULTS

First, the levels of circulating interleukin-8, neutrophil-to-lymphocyte ratio, and NETs were closely related to poor efficacy of first-line chemoimmunotherapy. Patients were classed into a low NET group or a high NET group. A total of 54 differential plasma metabolites were identified. These metabolites were primarily involved in arachidonic acid and purine metabolism. Three key metabolites were identified as crucial variables, including 8,9-epoxyeicosatrienoic acid, L-malate, and bis(monoacylglycerol)phosphate (18:1/16:0). Using metabolomic sequencing data and machine learning methods, key metabolic signatures were screened to predict NET level as well as chemoimmunotherapy efficacy.

CONCLUSION

The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patients.

Radiomics incorporating deep features for predicting Parkinson's disease in 123I-Ioflupane SPECT

PURPOSE

123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0.

METHODS

In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models.

RESULTS

For the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.

CONCLUSION

The combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

Molecular insights into programmed cell death in esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is a deadly type of esophageal cancer. Programmed cell death (PCD) is an important pathway of cellular self-extermination and is closely involved in cancer progression. A detailed study of its mechanism may contribute to ESCC treatment.

Methods

We obtained expression profiling data of ESCC patients from public databases and genes related to 12 types of PCD from previous studies. Hub genes in ESCC were screened from PCD-related genes applying differential expression analysis, machine learning analysis, linear support vector machine (SVM), random forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. In addition, based on the HTFtarget and TargetScan databases, transcription factors (TFs) and miRNAs interacting with the hub genes were selected. The relationship between hub genes and immune cells were analyzed using the CIBERSORT algorithm. Finally, to verify the potential impact of the screened hub genes on ESCC occurrence and development, a series of in vitro cell experiments were conducted.

Results

We screened 149 PCD-related DEGs, of which five DEGs (INHBA, LRRK2, HSP90AA1, HSPB8, and EIF2AK2) were identified as the hub genes of ESCC. The area under the curve (AUC) of receiver operating characteristic (ROC) curve of the integrated model developed using the hub genes reached 0.997, showing a noticeably high diagnostic accuracy. The number of TFs and miRNAs regulating hub genes was 105 and 22, respectively. INHBA, HSP90AA1 and EIF2AK2 were overexpressed in cancer tissues and cells of ESCC. Notably, INHBA knockdown suppressed ECSS cell migration and invasion and altered the expression of important apoptotic and survival proteins.

Conclusion

This study identified significant molecules with promising accuracy for the diagnosis of ESCC, which may provide a new perspective and experimental basis for ESCC research.

Pyroptosis-related long-noncoding RNA signature predicting survival and immunotherapy efficacy in patients with lung squamous cell carcinoma

Pyroptosis-related long-noncoding RNAs (PRlncRNAs) play an important role in cancer progression. However, their role in lung squamous cell carcinoma (LUSC) is unclear. A risk model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on RNA sequencing data from The Cancer Genome Atlas database. The LUSC cohort was divided into high- and low-risk groups based on the median risk score. For the prognostic value of the model, the Kaplan-Meier analysis, log-rank test, and Cox regression analysis were performed. A nomogram was constructed to predict the prognosis of patients, using a risk score and clinical parameters such as age, sex, clinical stage, and tumor node metastasis classification (TNM) stage. Afterwards, six common algorithms were employed to assess the invasion of immune cells. The Gene Set Enrichment Analysis (GSEA) was conducted to identify differences between patients at high and low risk. Furthermore, the pRRophetic package was employed to forecast the half-maximal inhibitory doses of prevalent chemotherapeutic drugs, while the tumor immune dysfunction and exclusion score was computed to anticipate the response to immunotherapy. The expression levels of the seven PRlncRNAs were examined in both LUSC and normal lung epithelial cell lines using RT-qPCR. Proliferation, migration, and invasion assays were also carried out to investigate the role of MIR193BHG in LUSC cells. Patients in the low-risk group showed prolonged survival in the total cohort or subgroup analysis. The Cox regression analysis showed that the risk model could act as an independent prognostic factor for patients with LUSC. The results of GSEA analysis revealed that the high-risk group showed enrichment of cytokine pathways, Janus tyrosine kinase/signal transducer and activator of the transcription signalling pathway, and Toll-like receptor pathway. Conversely, the low-risk group showed enrichment of several gene repair pathways. Furthermore, the risk score was positively correlated with immune cell infiltration. Moreover, patients in the high-risk category showed reduced responsiveness to conventional chemotherapeutic medications and immunotherapy. The majority of the long noncoding RNAs in the risk model were confirmed to be overexpressed in LUSC cell lines compared to normal lung epithelial cell lines by in vitro tests. Further studies have shown that downregulating the expression of MIR193BHG may inhibit the growth, movement, and infiltration capabilities of LUSC cells, whereas increasing the expression of MIR193BHG could enhance these malignant tendencies. This study found that PRlncRNAs were linked to the prognosis of LUSC patients. The risk model, evaluated across various clinical parameters and treatment modalities, shows potential as a future reference for clinical applications.

Identification of mitophagy-related hub genes during the progression of spinal cord injury by integrated multinomial bioinformatics analysis

Spinal cord injury (SCI) is a disturbance of peripheral and central nerve conduction that causes disability in sensory and motor function. Currently, there is no effective treatment for SCI. Mitophagy plays a vital role in mitochondrial quality control during various physiological and pathological processes. The study aimed to elucidate the role of mitophagy and identify potential mitophagy-related hub genes in SCI pathophysiology. Two datasets (GSE15878 and GSE138637) were analyzed. Firstly, the differentially expressed genes (DEGs) were identified and mitophagy-related genes were obtained from GeneCards, then the intersection between SCI and mitophagy-related genes was determined. Next, we performed gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), protein-protein interaction network (PPI network), least absolute shrinkage and selection operator (LASSO), and cluster analysis to identify and define the hub genes in SCI. Finally, the link between hub genes and infiltrating immune cells was investigated and the potential transcriptional regulation/small molecular compounds to target hub genes were predicted. In total, SKP1 and BAP1 were identified as hub genes of mitophagy-related DEGs during SCI development and regulatory T cells (Tregs)/resting NK cells/activated mast cells may play an essential role in the progression of SCI. LINC00324 and SNHG16 may regulate SKP1 and BAP1, respectively, through miRNAs. Eleven and eight transcriptional factors (TFs) regulate SKP1 and BAP1, respectively, and six small molecular compounds target BAP1. Then, the mRNA expression levels of BAP1 and SKP1 were detected in the injured sites of spinal cord of SD rats at 6 h and 72 h after injury using RT-qPCR, and found that the level were decreased. Therefore, the pathways of mitophagy are downregulated during the pathophysiology of SCI, and SKP1 and BAP1 could be accessible targets for diagnosing and treating SCI.

Identification of novel endoplasmic reticulum-related genes and their association with immune cell infiltration in major depressive disorder

BACKGROUND

Several lines of evidence point to an interaction between genetic predisposition and environmental factors in the onset of major depressive disorder (MDD). This study is aimed to investigate the pathogenesis of MDD by identifying key biomarkers, associated immune infiltration using bioinformatic analysis and human postmortem sample.

METHODS

The Gene Expression Omnibus (GEO) database of GSE98793 was adopted to identify hub genes linked to endoplasmic reticulum (ER) stress-related genes (ERGs) in MDD. Another GEO database of GSE76826 was employed to validate the novel target associated with ERGs and immune infiltration in MDD. Moreover, human postmortem sample from MDD patients was utilized to confirm the differential expression analysis of hub genes.

RESULTS

We discovered 12 ER stress-related differentially expressed genes (ERDEGs). A LASSO Cox regression analysis helped construct a diagnostic model for these ERDEGs, incorporating immune infiltration analysis revealed that three hub genes (ERLIN1, SEC61B, and USP13) show the significant and consistent expression differences between the two groups. Western blot analysis of postmortem brain samples indicated notably higher expression levels of ERLIN1 and SEC61B in the MDD group, with USP13 also tending to increase compared to control group.

LIMITATIONS

The utilization of the MDD gene chip in this analysis was sourced from the GEO database, which possesses a restricted number of pertinent gene chip samples.

CONCLUSIONS

These findings indicate that ERDEGs especially including ERLIN1, SEC61B, and USP13 associated the infiltration of immune cells may be potential diagnostic indicators for MDD.

Identification of key biomarkers of endothelial dysfunction in hypertension with carotid atherosclerosis based on WGCNA and the LASSO algorithm

Background

Endothelial dysfunction is the early stage of carotid atherosclerosis (CAS) in patients with hypertension. It is worth identifying the potential hub genes of endothelial dysfunction to elucidate pathological mechanism in the progression of the disease.

Method

We obtained gene expression profiles of GSE43292 from the Gene Expression Omnibus (GEO) database. Hub genes associated with CAS were identified through weighted gene correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore potential biological mechanisms and signaling pathways. Finally, in vitro experiments on human umbilical vein endothelial cells (HUVECs) were conducted to validate these hub genes.

Results

The microarray dataset GSE43292 included 32 CAS plaques samples and corresponding macroscopically intact tissues from patients with hypertension. A total of 161 differentially expressed genes were discovered. Through WGCNA analysis, the gray60 module emerged as the most significant module associated with clinical features. The GO and KEGG enrichment analyses of genes in the gray60 module highlighted the substantial involvement of immune response-related signaling pathways. Two key hub genes (CCR1 and NCKAP1L) were pinpointed via LASSO regression. We found a significant increase in the mRNA expression level of the hub genes in oxidized low density lipoprotein (ox-LDL) treated HUVECs.

Conclusions

Our study indicated that the hub genes related to immune responses are involved in the development of CAS. Two hub genes (CCR1 and NCKAP1L) of endothelial dysfunction were identified. These genes may provide a valuable therapeutic target of CAS in patients with hypertension.

The prognostic and clinical value of genes associate with immunity and amino acid Metabolism in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the commonest subtype of primary lung cancer. A comprehensive analysis of the association of immunity with amino acid metabolism in LUAD is critical for understanding the disease.

Methods

The present study examined LUAD and noncancerous cases from the TCGA database. Differentially expressed genes (DEGs) between LUAD and noncancerous tissues were detected by analyzing processed expression profiles. We cross-referenced the up-regulated DEGs with Immune and Amino Acid Metabolism-related genes (I&AAMGs), resulting in Immune and Amino Acid Metabolism related differentially expressed genes (IAAAMRDEGs). The STRING database was employed to analyze PPI on IAAAMRDEGs, obtaining excavated hub genes, whose biological processes, molecular functions and cellular components were examined with GO/KEGG. Potential mechanisms related to LUAD were investigated by GSEA and GSVA. A prognostic model was built by LASSO-COX analysis, taking into consideration risk scores and prognostic factors to determine biomarkers affecting LUAD occurrence and prognosis.

Results

Totally 377 genes were detected at the intersection of upregulated DEGs and I&AAMGs. Analysis of PPI on these 377 IAAAMRDEGs yielded 17 hub genes. A LASSO regression analysis was utilized to assess the prognostic values of the 17 hub genes. Validation using the combined dataset confirmed 4 genes, e.g., polo-like kinase (PLK1), Ribonucleotide Reductase Subunit M2 (RRM2), Thyroid Hormone Receptor Interactor 13 (TRIP13), and Hyaluronan-Mediated Motility Receptor (HHMR). The model's accuracy was further assessed by ROC curve analysis and the COX model. In addition, immunohistochemical staining obtained from the HPA database, revealed enhanced PLK1 expression in LUAD samples.

Conclusion

LUAD pathogenesis is highly associated with immunity and amino acid metabolism. The PLK1, RRM2, TRIP13, and HMMR genes have prognostic values for LUAD. PLK1 upregulation in LUAD might be involved in tumorigenesis by modulating the cell cycle and represents a potential prognostic factor in clinic.

Comprehensive Analysis of angiogenesis associated genes and tumor microenvironment infiltration characterization in cervical cancer

Background

Cervical cancer is among the most prevalent malignancies worldwide. This study explores the relationships between angiogenesis-related genes (ARGs) and immune infiltration, and assesses their implications for the prognosis and treatment of cervical cancer. Additionally, it develops a diagnostic model based on angiogenesis-related differentially expressed genes (ARDEGs).

Methods

We systematically evaluated 15 ARDEGs using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA). Immune cell infiltration was assessed using a single-sample gene-set enrichment analysis (ssGSEA) algorithm. We then constructed a diagnostic model for ARDEGs using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and evaluated the diagnostic value of this model and the hub genes in predicting clinical outcomes and immunotherapy responses in cervical cancer.

Results

A set of ARDEGs was identified from the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UCSC Xena database. We performed KEGG, GO, and GSEA analyses on these genes, revealing significant involvement in cell proliferation, differentiation, and apoptosis. The ARDEGs diagnostic model, constructed using LASSO regression analysis, showed high predictive accuracy in cervical cancer patients. We developed a reliable nomogram and decision curve analysis to evaluate the clinical utility of the ARDEG diagnostic model. The 15 ARDEGs in the model were associated with clinicopathological features, prognosis, and immune cell infiltration. Notably, ITGA5 expression and the abundance of immune cell infiltration (specifically mast cell activation) were highly correlated.

Conclusion

This study identifies the prognostic characteristics of ARGs in cervical cancer patients, elucidating aspects of the tumor microenvironment. It enhances the predictive accuracy of immunotherapy outcomes and establishes new strategies for immunotherapeutic interventions.

Exploration of the prognostic value of methylation regulators related to m5C in papillary thyroid carcinoma

The incidence of papillary thyroid carcinoma (PTC) has increased significantly in recent years, and for patients with metastatic and recurrent PTC, the options for treatment currently available are insufficient. To date, the exact molecular mechanism underlying PTC is still not fully understood. 5-Methylcytosine (m5C) RNA methylation is associated with the prognosis of a variety of tumors. However, the molecular mechanisms and biomarkers associated with m5C in the diagnosis, treatment, and prognosis of this disease have not been fully elucidated. Ten m5C regulators with significantly different expression levels were included in this study. Immune infiltration analysis revealed significant negative correlations between most of these regulators and regulatory T cells. TRDMT1, NSUN5, and NSUN6 had high weights and strong correlations in the protein-protein interaction network. Using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis, 1489 differentially expressed genes were screened from The Cancer Genome Atlas messenger RNA matrix, indicating that these differentially expressed genes were significantly enriched in various pathways and functions related to cancers. Four m5C regulators, NSUN2, NSUN4, NSUN6, and DNMT3B, were screened as prognostic markers by least absolute shrinkage and selection operator regression analysis, and NSUN2 and NSUN6 were identified as risk factors for poor prognosis. We found that the prognostic prediction model constructed using the m5C regulators NSUN2, NSUN4, NSUN6, and DNMT3B showed good prognostic prediction ability and diagnostic ability. This model was applied to predict the survival probability of patients with PTC, the prediction ability of 5-year survival was the best. The multi-factor prognostic prediction model combined with the tumor node metastasis stage and risk score grouping showed better prognostic predictive power.

Shared biomarkers and mechanisms in idiopathic pulmonary fibrosis and non-small cell lung cancer

BACKGROUND

Epidemiological evidence has indicated the occurrence of idiopathic pulmonary fibrosis (IPF) with coexisting lung cancer is not a coincidence. The pathogenic mechanisms shared between IPF and non-small cell lung cancer (NSCLC) at the transcriptional level remain elusive and need to be further elucidated.

METHODS

IPF and NSCLC datasets of expression profiles were obtained from the GEO database. Firstly, to detect the shared dysregulated genes positively correlated with both IPF and NSCLC, differentially expressed analysis and WGCNA analysis were carried out. Functional enrichment and the construction of protein-protein network were employed to reveal pathogenic mechanisms related to two diseases mediated by the shared dysregulated genes. Then, the LASSO regression was adopted for screening critical candidate biomarkers for two disorders. Moreover, ROC curves were applied to evaluate the diagnostic value of the candidate biomarkers in both IPF and NSCLC.

RESULTS

The 20 shared dysregulated genes positively correlated with both IPF and NSCLC were identified after intersecting differentially expressed analysis and WGCNA analysis. Functional enrichment revealed the 20 shared genes mostly enriched in extracellular region, which is critical in the organization of extracellular matrix. The protein-protein networks unrevealed the interaction of the 11 shared genes involving in collagen deposition and the connection between PYCR1 with PSAT1. PSAT1, PYCR1, COL10A1 and KIAA1683 were screened by the LASSO regression. ROC curves comprising area under the curve (AUC) verified the potential diagnostic value of PSAT1 and COL10A1 in both IPF and NSCLC.

CONCLUSIONS

We revealed dysregulated extracellular matrix through aberrant expression of the relevant genes, which provided further understanding for the common molecular mechanisms predisposing the occurrence of both IPF and NSCLC.

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

Background

Thyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet.

Methods

We identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas.

Results

A LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue.

Conclusion

A potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

TMErisk score: A tumor microenvironment-based model for predicting prognosis and immunotherapy in patients with head and neck squamous cell carcinoma

Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.

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

BACKGROUND

Liver ischemia-reperfusion injury (LIRI) is closely associated with immune infiltration, which commonly occurs after liver surgery, especially liver transplantation. Therefore, it is crucial to identify the genes responsible for LIRI and develop effective therapeutic strategies that target immune response. Methylation modifications in mRNA play various crucial roles in different diseases. This study aimed to identify potential methylation-related markers in patients with LIRI and evaluate the corresponding immune infiltration.

METHODS

Two Gene Expression Omnibus datasets containing human liver transplantation data (GSE12720 and GSE151648) were downloaded for integrated analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to investigate the functional enrichment of differentially expressed genes (DEGs). Differentially expressed methylation-related genes (DEMRGs) were identified by overlapping DEG sets and 65 genes related to N6-methyladenosine (m6A), 7-methylguanine (m7G), 5-methylcytosine (m5C), and N1-methyladenosine (m1A). To evaluate the relationship between DEMRGs, a protein-protein interaction (PPI) network was utilized. The core DEMRGs were screened using three machine learning algorithms: least absolute shrinkage and selection operator, random forest, and support vector machine-recursive feature elimination. After verifying the diagnostic efficacy using the receiver operating characteristic curve, we validated the expression of the core DEMRGs in clinical samples and performed relative cell biology experiments. Additionally, the immune status of LIRI was comprehensively assessed using the single sample gene set enrichment analysis algorithm. The upstream microRNA and transcription factors of the core DEMRGs were also predicted.

RESULTS

In total, 2165 upregulated and 3191 downregulated DEGs were identified, mainly enriched in LIRI-related pathways. The intersection of DEGs and methylation-related genes yielded 28 DEMRGs, showing high interaction in the PPI network. Additionally, the core DEMRGs YTHDC1, METTL3, WTAP, and NUDT3 demonstrated satisfactory diagnostic efficacy and significant differential expression and corresponding function based on cell biology experiments. Furthermore, immune infiltration analyses indicated that several immune cells correlated with all core DEMRGs in the LIRI process to varying extents.

CONCLUSIONS

We identified core DEMRGs (YTHDC1, METTL3, WTAP, and NUDT3) associated with immune infiltration in LIRI through bioinformatics and validated them experimentally. This study may provide potential methylation-related gene targets for LIRI immunotherapy.

Comprehensive transcriptomic analysis unveils macrophage-associated genes for establishing an abdominal aortic aneurysm diagnostic model and molecular therapeutic framework

BACKGROUND

Abdominal aortic aneurysm (AAA) is a highly lethal cardiovascular disease. The aim of this research is to identify new biomarkers and therapeutic targets for the treatment of such deadly diseases.

METHODS

Single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were used to identify distinct immune cell infiltration types between AAA and normal abdominal aortas. Single-cell RNA sequencing data were used to analyse the hallmark genes of AAA-associated macrophage cell subsets. Six macrophage-related hub genes were identified through weighted gene co-expression network analysis (WGCNA) and validated for expression in clinical samples and AAA mouse models. We screened potential therapeutic drugs for AAA through online Connectivity Map databases (CMap). A network-based approach was used to explore the relationships between the candidate genes and transcription factors (TFs), lncRNAs, and miRNAs. Additionally, we also identified hub genes that can effectively identify AAA and atherosclerosis (AS) through a variety of machine learning algorithms.

RESULTS

We obtained six macrophage hub genes (IL-1B, CXCL1, SOCS3, SLC2A3, G0S2, and CCL3) that can effectively diagnose abdominal aortic aneurysm. The ROC curves and decision curve analysis (DCA) were combined to further confirm the good diagnostic efficacy of the hub genes. Further analysis revealed that the expression of the six hub genes mentioned above was significantly increased in AAA patients and mice. We also constructed TF regulatory networks and competing endogenous RNA networks (ceRNA) to reveal potential mechanisms of disease occurrence. We also obtained two key genes (ZNF652 and UBR5) through a variety of machine learning algorithms, which can effectively distinguish abdominal aortic aneurysm and atherosclerosis.

CONCLUSION

Our findings depict the molecular pharmaceutical network in AAA, providing new ideas for effective diagnosis and treatment of diseases.

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

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

Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression

Motivation

Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.

Results

We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of six isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve interindividual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min. 0.94, Mean 0.98, Max. 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analysing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to nonimmune cells.

Availability and implementation

EAD workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for single-cell RNA-sequencing data demultiplexing using interindividual variations).

Significance of neutrophil extracellular traps-related gene in the diagnosis and classification of atherosclerosis

Atherosclerosis (AS) is a pathological process associated with various cardiovascular diseases. Upon different stimuli, neutrophils release reticular complexes known as neutrophil extracellular traps (NETs). Numerous researches have indicated a strong correlation between NETs and AS. However, its role in cardiovascular disease requires further investigation. By utilizing a machine learning algorithm, we examined the genes associated with NETs that were expressed differently in individuals with AS compared to normal controls. As a result, we identified four distinct genes. A nomogram model was built to forecast the incidence of AS. Additionally, we conducted analysis on immune infiltration, functional enrichment and consensus clustering in AS samples. The findings indicated that individuals with AS could be categorized into two groups, exhibiting notable variations in immune infiltration traits among the groups. Furthermore, to measure the NETs model, the principal component analysis algorithm was developed and cluster B outperformed cluster A in terms of NETs. Additionally, there were variations in the expression of multiple chemokines between the two subtypes. By studying AS NETs, we acquired fresh knowledge about the molecular patterns and immune mechanisms implicated, which could open up new possibilities for AS immunotherapy.

Global field collection data confirm an affinity of brown rot fungi for coniferous habitats and substrates

Unlike 'white rot' (WR) wood-decomposing fungi that remove lignin to access cellulosic sugars, 'brown rot' (BR) fungi selectively extract sugars and leave lignin behind. The relative frequency and distribution of these fungal types (decay modes) have not been thoroughly assessed at a global scale; thus, the fate of one-third of Earth's aboveground carbon, wood lignin, remains unclear. Using c. 1.5 million fungal sporocarp and c. 30 million tree records from publicly accessible databases, we mapped and compared decay mode and tree type (conifer vs angiosperm) distributions. Additionally, we mined fungal record metadata to assess substrate specificity per decay mode. The global average for BR fungi proportion (BR/(BR + WR records)) was 13% and geographic variation was positively correlated (R2 = 0.45) with conifer trees proportion (conifer/(conifer + angiosperm records)). Most BR species (61%) were conifer, rather than angiosperm (22%), specialists. The reverse was true for WR (conifer: 19%; angiosperm: 62%). Global BR proportion patterns were predicted with greater accuracy using the relative distributions of individual tree species (R2 = 0.82), rather than tree type. Fungal decay mode distributions can be explained by tree type and, more importantly, tree species distributions, which our data suggest is due to strong substrate specificities.

Comprehensive analysis and prediction model of mitophagy and ferroptosis in primary immune thrombocytopenia

Primary immune thrombocytopenia (ITP) is linked to specific pathogenic mechanisms, yet its relationship with mitophagy and ferroptosis is poorly understood. This study aimed to identify new biomarkers and explore the role of mitophagy and ferroptosis in ITP pathogenesis. Techniques such as differential analysis, Mfuzz expression pattern clustering, machine learning, gene set enrichment analysis, single-cell RNA sequencing (scRNA-seq) and immune infiltration analysis were employed to investigate the molecular pathways of pivotal genes. Two-sample Mendelian randomization (TSMR) assessed the causal effects in ITP. Key genes identified in the training set included GABARAPL1, S100A8, LIN28A, and GDF9, which demonstrated diagnostic potential in validation sets. Functional analysis indicated these genes' involvement in ubiquitin phosphorylation, PPAR signalling pathway and T-cell differentiation. Immune infiltration analysis revealed increased macrophage presence in ITP, related to the critical genes. scRNA-seq indicated reduced GABARAPL1 expression in ITP bone marrow macrophages. TSMR linked S100A8 with ITP diagnosis, presenting an OR of 0.856 (95% CI = 0.736-0.997, p = 0.045). The study pinpointed four central genes, GABARAPL1, S100A8, LIN28A, and GDF9, tied to mitophagy and ferroptosis in ITP. It posits that diminished GABARAPL1 expression may disrupts ubiquitin phosphorylation and PPAR signalling, impairing mitophagy and inhibiting ferroptosis, leading to immune imbalance.

An assessment of crucial structural contributors of HDAC6 inhibitors through fragment-based non-linear pattern recognition and molecular dynamics simulation approaches

Amidst the Zn2+-dependant isoforms of the HDAC family, HDAC6 has emerged as a potential target associated with an array of diseases, especially cancer and neuronal disorders like Rett's Syndrome, Alzheimer's disease, Huntington's disease, etc. Also, despite the availability of a handful of HDAC inhibitors in the market, their non-selective nature has restricted their use in different disease conditions. In this situation, the development of selective and potent HDAC6 inhibitors will provide efficacious therapeutic agents to treat different diseases. In this context, this study has been carried out to evaluate the potential structural contributors of quinazoline-cap-containing HDAC6 inhibitors via machine learning (ML), conventional classification-dependant QSAR, and MD simulation-based binding mode of interaction analysis toward HDAC6 binding. This combined conventional and modern molecular modeling study has revealed the significance of the quinazoline moiety, substitutions present at the quinazoline cap group, as well as the importance of molecular property, number of hydrogen bond donor-acceptor functions, carbon-chlorine distance that significantly affects the HDAC6 binding of these inhibitors, subsequently affecting their potency . Interestingly, the study also revealed that the substitutions such as the chloroethyl group, and bulky quinazolinyl cap group can affect the binding of the cap function with the amino acid residues present in the loops proximal to the catalytic site of HDAC6. Such contributions of cap groups can lead to both stabilization and destabilization of the cap function after occupying the hydrophobic catalytic site by the aryl hydroxamate linker-ZBG functions.

Anoikis and SPP1 in idiopathic pulmonary fibrosis: integrating bioinformatics, cell, and animal studies to explore prognostic biomarkers and PI3K/AKT signaling regulation

OBJECTIVE

This study aims to explore the relevance of anoikis in idiopathic pulmonary fibrosis (IPF) and identify associated biomarkers and signaling pathways.

METHOD

Unsupervised consensus cluster analysis was employed to categorize IPF patients into subtypes. We utilized Weighted Gene Co-Expression Network Analysis (WGCNA) and Protein-Protein Interaction network construction to identify anoikis-related modules and key genes. A prognostic signature was developed using Lasso and multivariate Cox regression analysis. Single-cell sequencing assessed hub gene expression in various cell types, and both cell and animal experiments confirmed IPF-related pathways.

RESULTS

We identified two distinct anoikis-associated subtypes with differing prognoses. WGCNA revealed essential hub genes, with SPP1 being prominent in the anoikis-related signature. The anoikis-related signature is effective in determining the prognosis of patients with IPF. Single-cell sequencing highlighted significant differences in SPP1 expression, notably elevated in fibroblasts derived from IPF patients. In vivo and in vitro experiments demonstrated that SPP1 enhances fibrosis in mouse lung fibroblasts by regulating p27 through the PI3K/Akt pathway.

CONCLUSION

Our research demonstrates a robust prognostic signature associated with anoikis and highlights SPP1 as a pivotal regulator of the PI3K/AKT signaling pathway in pulmonary fibrosis.

Epigenetic-related gene-based prognostic model construction and validation in prostate adenocarcinoma

Prostate adenocarcinoma (PRAD), driven by both genetic and epigenetic factors, is a common malignancy that affects men worldwide. We aimed to identify and characterize differentially expressed epigenetic-related genes (ERGs) in PRAD and investigate their potential roles in disease progression and prognosis. We used PRAD samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify prognosis-associated ERGs. Thirteen ERGs with two distinct expression profiles were identified through consensus clustering. Gene set variation analysis highlighted differences in pathway activities, particularly in the Hedgehog and Notch pathways. Higher epigenetic scores correlated with favorable prognosis and improved immunotherapeutic response. Experimental validation underscored the importance of CBX3 and KAT2A, suggesting their pivotal roles in PRAD. This study provides crucial insights into the epigenetic scoring approach and presents a promising prognostic tool, with CBX3 and KAT2A as key players. These findings pave the way for targeted and personalized interventions for the treatment of PRAD.