Construction of an HLA Classifier for Early Diagnosis, Prognosis, and Recognition of Immunosuppression in Sepsis by Multiple Transcriptome Datasets

Identification of lncRNAs in peripheral blood mononuclear cells associated with sepsis immunosuppression based on weighted gene co-expression network analysis

BACKGROUND

Sepsis-induced immunosuppression involves complex molecular mechanisms, including dysregulated long noncoding RNAs (lncRNAs), which remain poorly understood.

OBJECTIVE

We aimed to identify immunosuppression-related lncRNAs and their functional pathways in sepsis. Methods: Using weighted gene coexpression network analysis (WGCNA), we analyzed lncRNA profiles from peripheral blood mononuclear cells (PBMCs) of three sepsis patients and three healthy controls. Key modules linked to immunosuppression were validated via RT-PCR and external datasets. Pathway enrichment and protein interaction networks were employed to prioritize mechanisms.

RESULTS

A sepsis-associated module containing 4,193 lncRNAs revealed three immunosuppression-related pathways: Th17 cell differentiation, cytokine-cytokine receptor interactions, and cancer-related proteoglycan signaling. Protein-protein interaction networks identified three central genes (SLFN12, ICOS, IKZF2) and their linked lncRNAs (ENSG00000267074, lnc-ICOSLG-1, lnc-IKZF2-7), all significantly downregulated in sepsis patients.

CONCLUSION

 Our findings highlight novel lncRNA-regulated pathways in sepsis-induced immunosuppression, providing potential targets for improved diagnosis and therapy.

COMPREHENSIVE CHARACTERIZATION OF CYTOKINES IN PATIENTS UNDER EXTRACORPOREAL MEMBRANE OXYGENATION: EVIDENCE FROM INTEGRATED BULK AND SINGLE-CELL RNA SEQUENCING DATA USING MULTIPLE MACHINE LEARNING APPROACHES

ABSTRACT

Background : Extracorporeal membrane oxygenation (ECMO) is an effective technique for providing short-term mechanical support to the heart, lungs, or both. During ECMO treatment, the inflammatory response, particularly involving cytokines, plays a crucial role in pathophysiology. However, the potential effects of cytokines on patients receiving ECMO are not comprehensively understood. Methods : We acquired three ECMO support datasets, namely two bulk and one single-cell RNA sequencing (RNA-seq), from the Gene Expression Omnibus (GEO) combined with hospital cohorts to investigate the expression pattern and potential biological processes of cytokine-related genes (CRGs) in patients under ECMO. Subsequently, machine learning approaches, including support vector machine (SVM), random forest (RF), modified Lasso penalized regression, extreme gradient boosting (XGBoost), and artificial neural network (ANN), were applied to identify hub CRGs, thus developing a prediction model called CRG classifier. The predictive and prognostic performance of the model was comprehensively evaluated in GEO and hospital cohorts. Finally, we mechanistically analyzed the relationship between hub cytokines, immune cells, and pivotal molecular pathways. Results : Analyzing bulk and single-cell RNA-seq data revealed that most CRGs were significantly differentially expressed; the enrichment scores of cytokine and cytokine-cytokine receptor (CCR) interaction were significantly higher during ECMO. Based on multiple machine learning algorithms, nine key CRGs (CCL2, CCL4, IFNG, IL1R2, IL20RB, IL31RA, IL4, IL7, and IL7R) were used to develop the CRG classifier. The CRG classifier exhibited excellent prognostic values (AUC > 0.85), serving as an independent risk factor. It performed better in predicting mortality and yielded a larger net benefit than other clinical features in GEO and hospital cohorts. Additionally, IL1R2, CCL4, and IL7R were predominantly expressed in monocytes, NK cells, and T cells, respectively. Their expression was significantly positively correlated with the relative abundance of corresponding immune cells. Gene set variation analysis (GSVA) revealed that para-inflammation, complement and coagulation cascades, and IL6/JAK/STAT3 signaling were significantly enriched in the subgroup that died after receiving ECMO. Spearman correlation analyses and Mantel tests revealed that the expression of hub cytokines (IL1R2, CCL4, and IL7R) and pivotal molecular pathways scores (complement and coagulation cascades, IL6/JAK/STAT3 signaling, and para-inflammation) were closely related. Conclusion : A predictive model (CRG classifier) comprising nine CRGs based on multiple machine learning algorithms was constructed, potentially assisting clinicians in guiding individualized ECMO treatment. Additionally, elucidating the underlying mechanistic pathways of cytokines during ECMO will provide new insights into its treatment.

Comprehensive Characterization of Th2/Th17 Cells-Related Gene in Systemic Juvenile Rheumatoid Arthritis: Evidence from Mendelian Randomization and Transcriptome Data Using Multiple Machine Learning Approaches

Background

Growing research has demonstrated that alterations in Th2 and Th17 cell composition were linked to systemic juvenile rheumatoid arthritis (sJRA). Nevertheless, whether these associations indicate a causal link remains unclear, and the potential effects of Th2/Th17-related molecules have not been clarified.

Methods

Mendelian randomization (MR) alongside transcriptome examination was implemented to ascertain the links between the Th2/Th17 cells and sJRA. Subsequently, we established an innovative machine learning (ML) framework encompassing 12 ML approaches and their 111 permutations to generate a unified Th2/Th17 classifier, which underwent verification across three separate cohorts. The hub Th2/Th17-related genes' level in the sJRA patients was substantiated via qRT-PCR. Lastly, the SHapley Additive exPlanations (SHAP) in conjunction with the XGBoost algorithm to pinpoint ideal Th2/Th17-linked genes.

Results

Based on MR analyses of two sJRA GWAS, 2 immunophenotypes (lymphocyte and IgD+ B cell) were causally linked to sJRA. Based on IOBR algorithms, we revealed that lymphocyte Th2/Th17 proportion was markedly changed in sJRA from seven cohorts. WGCNA and differential analysis in two merged GEO cohorts identified 64 Th2/Th17-related genes. Based on the average AUC (0.844) and model stability in four cohorts, we converted 12 ML techniques into 111 combinations, from which we chose the optimal algorithm to generate an ML-derived diagnostic signature (Th2/Th17 classifier). qRT-PCR verified results. Moreover, immune cell infiltration and functional enrichment analysis suggested hub Th2/Th17-related gene potentially mediated sJRA onset. XGBoost algorithm and SHAP detected HRH2 as crucial genetic markers, which may be an important target for sJRA.

Conclusion

A diagnostic model (Th2/Th17 classifier) via 111 ML algorithm combinations in six independent cohorts was generated and validated, which stands as an effective instrument for sJRA detection. The identification of essential immune components and molecular cascades, along with HRH2, could emerge as vital therapeutic targets for sJRA intervention, providing an enhanced understanding of its fundamental processes.

Regulatory T cells-related gene in primary sclerosing cholangitis: evidence from Mendelian randomization and transcriptome data

The present study utilized large-scale genome-wide association studies (GWAS) summary data (731 immune cell subtypes and three primary sclerosing cholangitis (PSC) GWAS datasets), meta-analysis, and two PSC transcriptome data to elucidate the pivotal role of Tregs proportion imbalance in the occurrence of PSC. Then, we employed weighted gene co-expression network analysis (WGCNA), differential analysis, and 107 combinations of 12 machine-learning algorithms to construct and validate an artificial intelligence-derived diagnostic model (Tregs classifier) according to the average area under curve (AUC) (0.959) in two cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) verified that compared to control, Akap10, Basp1, Dennd3, Plxnc1, and Tmco3 were significantly up-regulated in the PSC mice model yet the expression level of Klf13, and Scap was significantly lower. Furthermore, immune cell infiltration and functional enrichment analysis revealed significant associations of the hub Tregs-related gene with M2 macrophage, neutrophils, megakaryocyte-erythroid progenitor (MEP), natural killer T cell (NKT), and enrichment scores of the autophagic cell death, complement and coagulation cascades, metabolic disturbance, Fc gamma R-mediated phagocytosis, mitochondrial dysfunction, potentially mediating PSC onset. XGBoost algorithm and SHapley Additive exPlanations (SHAP) identified AKAP10 and KLF13 as optimal genes, which may be an important target for PSC.

Construction of the miRNA/Pyroptosis-Related Molecular Regulatory Axis in Abdominal Aortic Aneurysm: Evidence From Transcriptome Data Combined With Multiple Machine Learning Approaches Followed by Experiment Validation

Background: Abdominal aortic aneurysm (AAA) represents a permanent and localized widening of the abdominal aorta, posing a potentially lethal risk of aortic rupture. Several recent studies have highlighted the role of pyroptosis, a pro-inflammatory programed cell death, as critical molecular regulators in AAA occurrence, progression, and rupture. However, the potential effects of pyroptosis in AAA and its upstream microRNA (miRNA) have not been comprehensively clarified. Methods: Through a search of the gene expression omnibus (GEO) database, the expression profiles of mRNAs (GSE7084, GSE57691, and GSE98278) and miRNAs (GSE62179) and corresponding clinical features were downloaded, respectively. Expression profiles of 15 AAA and 10 normal vascular samples were consecutively collected for in vitro experimentation and subsequent analysis. Various machine learning techniques were employed to identify hub pyroptosis-related genes (PRGs), leading to the development of a predictive model termed the PRG classifier. Quantitative real-time-polymerase chain reaction (qRT-PCR), western blot (WB), and enzyme-linked immunosorbent assay (ELISA) were used to confirm the expression of the hub PRGs. The diagnostic and predictive capabilities of the model were comprehensively evaluated in GEO and hospital cohorts. Then, the crucial immune cell infiltration and molecular pathways implicated in the initiation and rupture of AAA and their association with pyroptosis were explored. Lastly, a miRNA/hub pyroptosis-related molecular regulatory axis was constructed using the TargetScan dataset, which was further explored through loss-of-function assays. Results: Differential analysis, enrichment score analysis, and principal component analysis (PCA) revealed that pyroptosis-related molecules were significantly involved in the occurrence of AAA. Utilizing multiple machine learning algorithms, eight key PRGs (cysteinyl aspartate specific proteinase [CASP]1, infiltrating lymphocyte [IL]1B, IL18, IL6, NOD-, LRR- and pyrin domain-containing protein [NLRP]1, NLRP2, NLRP3, and tumor necrosis factor [TNF]) were integrated to establish a PRG classifier. Demonstrating robust diagnostic capabilities (area under curve [AUC] > 0.90), the PRG classifier provided clinical insights across two GEO datasets and effectively differentiated small AAA from large AAA, elective stable AAA (eAAA), and ruptured AAA (rAAA), respectively. qRT-PCR, WB, and ELISA verified the mRNA and protein expression of the hub PRGs. Notably, in hospital cohorts, a substantial positive link was unveiled between the PRG classifier and AAA risk factors (hypertension history, diastolic pressure, triglyceride levels, and aneurysm diameter). Furthermore, immune cell infiltration and functional enrichment analysis revealed significant associations of the PRG classifier/PRGs with M2 macrophage infiltration, activated dendritic cells, and enrichment scores of the cytosolic deoxyribonucleic acid (DNA) sensing pathway and tryptophan metabolism, potentially mediating AAA onset and rupture. Finally, based on 90 differentially expressed miRNAs (DEmiRNAs) and eight hub PRGs through TargetScan dataset, a hsa-miR-331-3p/TNF regulatory axis was constructed, wherein upregulation of hsa-miR-331-3p expression significantly reduced TNF and CASP1 protein levels. Conclusion: A predictive model (PRG classifier) incorporating eight PRGs through multiple machine learning algorithms was developed and validated. This model may stand as a potent tool for diagnosing AAA and assessing disease severity. The identification of the cytosolic DNA sensing pathway and the hsa-miR-331-3p/TNF interaction axis may represent crucial targets for AAA treatment, offering deeper insights into its potential pathogenesis.

Identification of diagnostic candidate genes in COVID-19 patients with sepsis

PURPOSE

Coronavirus Disease 2019 (COVID-19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID-19 patients with sepsis.

PATIENTS AND METHODS

We obtained a COVID-19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID-19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration.

RESULTS

The COVID-19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID-19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes.

CONCLUSION

Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID-19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID-19 patients with sepsis.

Profiling Molecular Changes of Host Response to Predict Outcome in Children with Septic Shock

Background

Septic shock is associated with high mortality and there is significant heterogeneity in the host response. The aim of this study was to understand the genome-wide expression transcriptomic signatures in children with septic shock and correlate them with outcomes.

Methods

This was a prospective study conducted on children (aged 1 month to 18 years) admitted to the PICU (June-December 2021) with septic shock. Demographic details, clinical details, and administered treatment were collected. Differential gene expression analysis was performed to understand the genes and pathways affecting in different subjects.

Results

Fifteen patients were recruited (Septic shock survivors (n = 5), nonsurvivors (n = 5), and non-sepsis controls (n = 5). The median age of the patients in survivors and nonsurvivors was 15 (13, 24) months and 180 (180, 184) months, respectively. The sepsis-survivors vs nonsepsis possessed 983 upregulated and 624 downregulated genes while comparing sepsis nonsurvivors (SNS) with nonsepsis yielded 1,854 upregulated and 1,761 downregulated genes. Further, the lowest number of deregulated genes (383 upregulated and 486 downregulated) were present in SNS compared to sepsis survivors. The major Reactome pathways, found upregulated in SNSs relative to survivors included CD22 mediated B cell receptor (BCR) regulation, scavenging of heme from plasma, and creation of C4 and C2 activators while T cell receptor (TCR) signaling, the common pathway of fibrin clot formation and generation of second messenger molecules were found to be downregulated.

Conclusion

Mortality-related gene signatures are promising diagnostic biomarkers for pediatric sepsis.

How to cite this article

Lalitha AV, Vasudevan A, Moorthy M, Ramaswamy G. Profiling Molecular Changes of Host Response to Predict Outcome in Children with Septic Shock. Indian J Crit Care Med 2024;28(9):879-886.

The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis

Sepsis is a severe disease characterized by high mortality rates. Our aim was to develop an early prognostic indicator of adverse outcomes in sepsis, utilizing easily accessible routine blood tests. A retrospective analysis of sepsis patients from the MIMIC-IV database was conducted. We performed univariate and multivariate regression analyses to identify independent risk factors associated with in-hospital mortality within 28 days. Logistic regression was utilized to combine the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-platelet ratio (NPR) into a composite score, denoted as NLR_NPR. We used ROC curves to compare the prognostic performance of the models and Kaplan-Meier survival curves to assess the 28 day survival rate. Subgroup analysis was performed to evaluate the applicability of NLR_NPR in different subpopulations based on specific characteristics. This study included a total of 1263 sepsis patients, of whom 179 died within 28 days of hospitalization, while 1084 survived beyond 28 days. Multivariate regression analysis identified age, respiratory rate, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), hypertension, and sequential organ failure assessment (SOFA) score as independent risk factors for 28 day mortality in septic patients (P < 0.05). Additionally, in the prediction model based on blood cell-related parameters, the combined NLR_NPR score exhibited the highest predictive value for 28 day mortality (AUC = 0.6666), followed by NLR (AUC = 0.6456) and NPR (AUC = 0.6284). Importantly, the performance of the NLR_NPR score was superior to that of the commonly used SOFA score (AUC = 0.5613). Subgroup analysis showed that NLR_NPR remained an independent risk factor for 28 day in-hospital mortality in the subgroups of age, respiratory rate, and SOFA, although not in the hypertension subgroup. The combined use of NLR and NPR from routine blood tests represents a readily available and reliable predictive marker for 28 day mortality in sepsis patients. These results imply that clinicians should prioritize patients with higher NLR_NPR scores for closer monitoring to reduce mortality rates.

Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration

BACKGROUND

Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation.

METHODS

The current study utilized two publicly accessible gene expression profiles (GSE111368 and GSE21802) from the Gene Expression Omnibus database. The research focused on identifying the genes exhibiting differential expression between severe and non-severe influenza patients. We employed three machine learning algorithms, namely the Least Absolute Shrinkage and Selection Operator regression model, Random Forest, and Support Vector Machine-Recursive Feature Elimination, to detect potential key genes. The key gene was further selected based on the diagnostic performance of the target genes substantiated in the dataset GSE101702. A single-sample gene set enrichment analysis algorithm was applied to evaluate the participation of immune cell infiltration and their associations with key genes.

RESULTS

A total of 44 differentially expressed genes were recognized; among them, we focused on 10 common genes, namely PCOLCE2, HLA_DPA1, LOC653061, TDRD9, MPO, HLA_DQA1, MAOA, S100P, RAP1GAP, and CA1. To ensure the robustness of our findings, we employed overlapping LASSO regression, Random Forest, and SVM-RFE algorithms. By utilizing these algorithms, we were able to pinpoint the aforementioned 10 genes as potential biomarkers for distinguishing between both cases of influenza (severe and non-severe). However, the gene HLA_DPA1 has been recognized as a crucial factor in the pathological condition of severe influenza. Notably, the validation dataset revealed that this gene exhibited the highest area under the receiver operating characteristic curve, with a value of 0.891. The use of single-sample gene set enrichment analysis has provided valuable insights into the immune responses of patients afflicted with severe influenza that have further revealed a categorical correlation between the expression of HLA_DPA1 and lymphocytes.

CONCLUSION

The findings indicated that the HLA_DPA1 gene may play a crucial role in the immune-pathological condition of severe influenza and could serve as a promising therapeutic target for patients infected with severe influenza.

Unlocking the potential of senescence-related gene signature as a diagnostic and prognostic biomarker in sepsis: insights from meta-analyses, single-cell RNA sequencing, and in vitro experiments

Cellular senescence is closely associated with the pathogenesis of sepsis. However, the diagnostic and prognostic value of senescence-related genes remain unclear. In this study, 866 senescence-related genes were collected from CellAge. The training cohort, GSE65682, which included 42 control and 760 sepsis samples, was obtained from the Gene Expression Omnibus (GEO). Feature selection was performed using gene expression difference detection, LASSO analysis, random forest, and Cox regression. TGFBI and MAD1L1 were ultimately selected for inclusion in the multivariate Cox regression model. Clustering based on the expressions of TGFBI and MAD1L1 was significantly associated with sepsis characteristics and prognoses (all P < 0.05). The risk signature served as a reliable prognostic predictor across the GSE65682, GSE95233, and GSE4607 cohorts (pooled hazard ratio = 4.27; 95% confidence interval [CI] = 1.63-11.17). Furthermore, it also served as a robust classifier to distinguish sepsis samples from control cases across 14 cohorts (pooled odds ratio = 5.88; 95% CI = 3.54-9.77). Single-cell RNA sequencing analyses from five healthy controls and four sepsis subjects indicated that the risk signature could reflect the senescence statuses of monocytes and B cells; this finding was then experimentally validated in THP-1 and IM-9 cells in vitro (both P < 0.05). In all, a senescence-related gene signature was developed as a prognostic and diagnostic biomarker for sepsis, providing cut-in points to uncover underlying mechanisms and a promising clinical tool to support precision medicine.

An integrative analysis of single-cell and bulk transcriptome and bidirectional mendelian randomization analysis identified C1Q as a novel stimulated risk gene for Atherosclerosis

Background

The role of complement component 1q (C1Q) related genes on human atherosclerotic plaques (HAP) is less known. Our aim is to establish C1Q associated hub genes using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis to diagnose and predict HAP patients more effectively and investigate the association between C1Q and HAP (ischemic stroke) using bidirectional Mendelian randomization (MR) analysis.

Methods

HAP scRNA-seq and bulk-RNA data were download from the Gene Expression Omnibus (GEO) database. The C1Q-related hub genes was screened using the GBM, LASSO and XGBoost algorithms. We built machine learning models to diagnose and distinguish between types of atherosclerosis using generalized linear models and receiver operating characteristics (ROC) analyses. Further, we scored the HALLMARK_COMPLEMENT signaling pathway using ssGSEA and confirmed hub gene expression through qRT-PCR in RAW264.7 macrophages and apoE-/- mice. Furthermore, the risk association between C1Q and HAP was assessed through bidirectional MR analysis, with C1Q as exposure and ischemic stroke (IS, large artery atherosclerosis) as outcomes. Inverse variance weighting (IVW) was used as the main method.

Results

We utilized scRNA-seq dataset (GSE159677) to identify 24 cell clusters and 12 cell types, and revealed seven C1Q associated DEGs in both the scRNA-seq and GEO datasets. We then used GBM, LASSO and XGBoost to select C1QA and C1QC from the seven DEGs. Our findings indicated that both training and validation cohorts had satisfactory diagnostic accuracy for identifying patients with HPAs. Additionally, we confirmed SPI1 as a potential TF responsible for regulating the two hub genes in HAP. Our analysis further revealed that the HALLMARK_COMPLEMENT signaling pathway was correlated and activated with C1QA and C1QC. We confirmed high expression levels of C1QA, C1QC and SPI1 in ox-LDL-treated RAW264.7 macrophages and apoE-/- mice using qPCR. The results of MR indicated that there was a positive association between the genetic risk of C1Q and IS, as evidenced by an odds ratio (OR) of 1.118 (95%CI: 1.013-1.234, P = 0.027).

Conclusion

The authors have effectively developed and validated a novel diagnostic signature comprising two genes for HAP, while MR analysis has provided evidence supporting a favorable association of C1Q on IS.

GPRASP1 is a candidate anti-oncogene and correlates with immune microenvironment and immunotherapeutic efficiency in head and neck cancer

BACKGROUND

G-protein-coupled receptor-associated sorting protein 1 (GPRASP1) plays an important role in tumorigenesis. However, GPRASP1 specific role has not been clarified in head and neck cancer (HNC).

METHODS

HNC RNA sequencing (RNA-seq) datasets, DNA methylation data, somatic mutation data, copy number variation (CNV) data, and corresponding clinicopathologic information were acquired from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive evaluation was performed to explore the relationship of GPRASP1 expression with clinicopathologic characteristics, CNV, and DNA methylation. Additionally, we employed HNC tissue microarray (TMA) to further confirm the relation between GPRASP1 expression and clinical features. Then, we systematically associated the GPRASP1 with immunological properties from numerous perspectives, such as immune cell infiltration, immune-related pathways, immune checkpoint inhibitors (ICIs), immunomodulators, immunogenicity, and immunotherapy.

RESULTS

Analyzing TCGA, GEO, and TMA datasets, GPRASP1 is significantly down-regulated in HNC compared to normal tissues. The expression of GPRASP1 is significantly negatively correlated with clinical features (perineural invasion, histologic grade, T stage, and TNM stage), and is an independent predictor of favorable prognosis, regardless of other clinicopathological features (HR: 0.42, 95% CI 0.20-0.91, p = 0.028). The etiological investigation found that the abnormal expression of GPRASP1 was related to DNA methylation, not CMV. Subsequently, the high expression of GPRASP1 was significantly correlated with immune cell infiltration (CD8+  T cell, tumor infiltrating lymphocyte), immune-related pathways (cytolytic activity, check-point, human leukocyte antigen), ICIs (CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT), immunomodulators (CCR4/5, CXCL9, CXCR3/4/5), and immunogenicity (immune score, neoantigen, tumor mutation burden). Finally, immunophenoscore and tumor immune dysfunction and exclusion analysis demonstrated that GPRASP1 expression levels can accurately predict the immunotherapeutic response.

CONCLUSION

GPRASP1 is a promising candidate biomarker that plays a role in the occurrence, development, and prognosis of HNC. Evaluating GPRASP1 expression will aid in the characterization of tumor microenvironment infiltration and orient more efficient immunotherapy strategies.

Construction of Autophagy-Related Gene Classifier for Early Diagnosis, Prognosis and Predicting Immune Microenvironment Features in Sepsis by Machine Learning Algorithms

Background

The immune system plays a fundamental role in the pathophysiology of sepsis, and autophagy and autophagy-related molecules are crucial in innate and adaptive immune responses; however, the potential roles of autophagy-related genes (ARGs) in sepsis are not comprehensively understood.

Methods

A systematic search was conducted in ArrayExpress and Gene Expression Omnibus (GEO) cohorts from July 2005 to May 2022. Machine learning approaches, including modified Lasso penalized regression, support vector machine, and artificial neural network, were applied to identify hub ARGs, thereby developing a prediction model termed ARG classifier. Diagnostic and prognostic performance of the model was comprehensively analyzed using multi-transcriptome data. Subsequently, we systematically correlated the ARG classifier/hub ARGs with immunological characteristics of multiple aspects, including immune cell infiltration, immune and molecular pathways, cytokine levels, and immune-related genes. Further, we collected clinical specimens to preliminarily investigate ARG expression levels and to assess the diagnostic performance of ARG classifier.

Results

A total of ten GEO and three ArrayExpress datasets were included in this study. Based on machine learning algorithms, eight key ARGs (ATG4C, BAX, BIRC5, ERBB2, FKBP1B, HIF1A, NCKAP1, and NFKB1) were integrated to establish ARG classifier. The model exhibited excellent diagnostic values (AUC > 0.85) in multiple datasets and multiple points in time and superiorly distinguished sepsis from other critical illnesses. ARG classifier showed significant correlations with clinical characteristics or endotypes and performed better in predicting mortality (AUC = 0.70) than other clinical characteristics. Additionally, the identified hub ARGs were significantly associated with immune cell infiltration (B, T, NK, dendritic, T regulatory, and myeloid-derived suppressor cells), immune and molecular pathways (inflammation-promoting pathways, HLA, cytolytic activity, apoptosis, type-II IFN response, complement and coagulation cascades), levels of several cytokines (PDGFRB, IL-10, IFNG, and TNF), which indicated that ARG classifier/hub ARGs adequately reflected the immune microenvironment during sepsis. Finally, using clinical specimens, the expression levels of key ARGs in patients with sepsis were found to differ significantly from those of control patients, and ARG classifier exhibited superior diagnostic performance, compared to procalcitonin and C-reactive protein.

Conclusion

Collectively, a diagnostic and prognostic model (ARG classifier) based on eight ARGs was developed which may assist clinicians in diagnosis of sepsis and recognizing patient at high risk to guide personalized treatment. Additionally, the ARG classifier effectively reflected the immune microenvironment diversity of sepsis and may facilitate personalized counseling for specific therapy.