Identifying COVID-19 subtypes by single-sample gene set enrichemnt analysis and providing guidance for sensitive drug selection.

This study aimed at using single-sample gene set enrichment analysis scores to cluster naso/pharyngeal swab specimen samples from coronavirus disease 2019 (COVID-19) patients into two clusters. One cluster with higher fractions of immune cells and more active inflammatory-related pathways was called the Immunity-High (Immunity-H) group, and the other one was called the Immunity-Low group. We explored impacts of the method on COVID-19 treatment. First, given that the Immunity-H group was mainly enriched in inflammatory-related pathways and had higher fractions of inflammatory cells, the Immunity-H group may obtain more curative effects from anti-inflammatory treatment. Second, we searched some hot genes from the PubMed platform that had been studied by researchers and found these genes upregulated in the Immunity-H group, so we speculated the Immunity-H group and Immunity-Low group may have different curative effects from drugs targeting these genes. Finally, we screened out hub genes for the Immunity-H group and predicted potential drugs for these hub genes by a public data set (http://dgidb.genome.wustl.edu). These hub genes are significantly upregulated in the Immunity-H group and neutrophils so that the Immunity-H group may obtain different treatment results from potential drugs compared with the Immunity-Low group. Therefore, the cluster method may provide help in drug development and administration for COVID-19 patients.

Identification of immune cell infiltration and effective biomarkers of polycystic ovary syndrome by bioinformatics analysis.

BACKGROUND: Patients with polycystic ovary syndrome (PCOS) exhibit a chronic inflammatory state, which is often accompanied by immune, endocrine, and metabolic disorders. Clarification of the pathogenesis of PCOS and exploration of specific biomarkers from the perspective of immunology by evaluating the local infiltration of immune cells in the follicular microenvironment may provide critical insights into disease pathogenesis. METHODS: In this study, we evaluated immune cell subsets and gene expression in patients with PCOS using data from the Gene Expression Omnibus database and single-sample gene set enrichment analysis. RESULTS: In total, 325 differentially expressed genes were identified, among which TMEM54 and PLCG2 (area under the curve = 0.922) were identified as PCOS biomarkers. Immune cell infiltration analysis showed that central memory CD4+ T cells, central memory CD8+ T cells, effector memory CD4+ T cells, γδ T cells, and type 17 T helper cells may affect the occurrence of PCOS. In addition, PLCG2 was highly correlated with γδ T cells and central memory CD4+ T cells. CONCLUSIONS: Overall, TMEM54 and PLCG2 were identified as potential PCOS biomarkers by bioinformatics analysis. These findings established a basis for further exploration of the immunological mechanisms of PCOS and the identification of therapeutic targets.

Establishment of a 10-gene prognostic model for gastric cancer based on the tumor immune microenvironment.

Gastric cancer seriously affects the health of modern people. The immune microenvironment of gastric cancer tissue is key to gastric cancer progression. We downloaded training and validation sets data from The Cancer Genome Atlas and Gene Expression Omnibus. Single-sample gene set enrichment analysis was used to sort patients into high, middle, and low immunity groups, of which immune infiltration in the high immunity group was substantially higher than of other two groups. Genes in high and low immunity groups expressed prominent differences. Further, the enrichment of differentially expressed genes was found mainly in immune-related pathways. Subsequently, an immune-related prognostic model was established, composed of ten prognosis-related genes identified by univariate risk regression, least absolute shrinkage and selection operator Cox, and multivariate risk regression. Survival analysis and receiver operating characteristic curves suggested good diagnostic efficacy of this model, and feature genes were linked to the degree of immune infiltration. An independent test suggested that the risk score could independently determine patient outcomes. We combined all clinical information and risk scores to establish a nomogram that could predict patient's prognosis. A prognostic model composed of 10 prognosis-related genes was generated with good diagnostic efficacy in predicting prognoses of gastric cancer patients.

Construction and Validation of a Glioma Prognostic Model Based on Immune Microenvironment.

OBJECTIVE: This study aims to construct a prognostic model based on the different immune infiltration statuses of the glioma samples. METHODS: Glioma-associated dataset was assessed from The Cancer Genome Atlas database. Hierarchical cluster analysis was performed to classify the glioma samples. Single-sample gene set enrichment analysis was introduced to the glioma samples for immune infiltration analysis. Kaplan-Meier survival analysis was applied to evaluate patients' prognoses. The differentially expressed genes (DEGs) between different sample groups were screened using limma package. Univariate Cox, LASSO Cox, and multivariate Cox regression analyses were employed to construct the prognostic model. The prediction performance of the model was examined by plotting a receiver-operating characteristic (ROC) curve, and GSEA was introduced to screen the differently activated pathways between high- and low-risk groups. RESULTS: The glioma samples were classified into 3 clusters where the different immune infiltration and survival statuses were presented among the clusters. 123 immune-related DEGs were screened from the differential expression analyses, and based on these DEGs, an 8-gene prognostic model was constructed. The ROC curve exhibited an optimal performance of the prognostic model, and GSEA showed that ECM-receptor interaction, complement and coagulation cascades, cytokine receptor pathways, and viral protein interaction with cytokine were differently activated between the two risk groups. CONCLUSION: The current study screened an immune-associated gene set by classifying and differential analysis, followed by constructing an 8-gene prognostic model based on the screened genes.

The identification of gene signatures in patients with extranodal NK/T-cell lymphoma from a pair of twins.

BACKGROUND: There is no unified treatment standard for patients with extranodal NK/T-cell lymphoma (ENKTL). Cancer neoantigens are the result of somatic mutations and cancer-specific. Increased number of somatic mutations are associated with anti-cancer effects. Screening out ENKTL-specific neoantigens on the surface of cancer cells relies on the understanding of ENKTL mutation patterns. Hence, it is imperative to identify ENKTL-specific genes for ENKTL diagnosis, the discovery of tumor-specific neoantigens and the development of novel therapeutic strategies. We investigated the gene signatures of ENKTL patients. METHODS: We collected the peripheral blood of a pair of twins for sequencing to identify unique variant genes. One of the twins is diagnosed with ENKTL. Seventy samples were analyzed by Robust Multi-array Analysis (RMA). Two methods (elastic net and Support Vector Machine-Recursive Feature Elimination) were used to select unique genes. Next, we performed functional enrichment analysis and pathway enrichment analysis. Then, we conducted single-sample gene set enrichment analysis of immune infiltration and validated the expression of the screened markers with limma packages. RESULTS: We screened out 126 unique variant genes. Among them, 11 unique genes were selected by the combination of elastic net and Support Vector Machine-Recursive Feature Elimination. Subsequently, GO and KEGG analysis indicated the biological function of identified unique genes. GSEA indicated five immunity-related pathways with high signature scores. In patients with ENKTL and the group with high signature scores, a proportion of functional immune cells are all of great infiltration. We finally found that CDC27, ZNF141, FCGR2C and NES were four significantly differential genes in ENKTL patients. ZNF141, FCGR2C and NES were upregulated in patients with ENKTL, while CDC27 was significantly downregulated. CONCLUSION: We identified four ENKTL markers (ZNF141, FCGR2C, NES and CDC27) in patients with extranodal NK/T-cell lymphoma.

Ferroptosis-related gene model to predict overall survival of papillary thyroid carcinoma.

BACKGROUND: Ferroptosis is a form of programmed cell death that is closely associated with the development of various tumors. However, the correlation between ferroptosis and papillary thyroid carcinoma (PTC) is unclear. This study was performed to investigate the expression and prognostic value of ferroptosis-related genes (FRG) in PTC. METHODS: mRNA expression profiles and corresponding clinical data of patients with PTC were analyzed to identify factors affecting prognosis. Independent risk factors were used to establish a predictive receiver operating characteristic model. Single-sample gene set enrichment analysis (ssGSEA) was used to evaluate the correlation between ferroptosis and immune cells. RESULTS: Most genes related to FRG (78.8%) were differentially expressed between the tumor and adjacent normal tissues. In univariate Cox regression analysis, 12 differentially expressed genes were associated with prognostic survival. We constructed a prognostic model of eight FRG, including DPP4, GPX4, GSS, ISCU, MIOX, PGD, TF, and TFRC, and divided patients into two groups: high and low risk. The high-risk group exhibited a significantly reduced overall survival rate. In multivariate Cox regression analysis, the risk score was used as an independent prognostic factor. ssGSEA showed that immune cell types and their expression in the high- and low-risk groups were significant. CONCLUSION: This study constructed a prognostic model of ferroptosis-related genes and determined its usefulness as an independent prognostic factor, providing a reference for the treatment and prognosis of patients with PTC.

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer.

BACKGROUND: Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. METHODS: Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. RESULTS: A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P < 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8). CONCLUSION: The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

Immune landscape of hepatocellular carcinoma: The central role of TP53-inducible glycolysis and apoptosis regulator.

Objective: This study aims to address the substantive issue of lacking reliable prognostic biomarkers in hepatocellular carcinoma (HCC) by investigating the relationship between TP53-inducible glycolysis and apoptosis regulator (TIGAR) and HCC prognosis using The Cancer Genome Atlas database. Methods: (1) Integrated statistical analyses, including logistic regression, Wilcoxon signed-rank test, and Kruskal-Wallis test, were conducted to explore the association between TIGAR expression and clinical-pathological features of HCC. (2) The Kaplan-Meier method combined with univariate and multivariate Cox regression models underscored TIGAR as a prognostic factor in HCC. (3) Gene set enrichment analysis (GSEA) revealed key pathways associated with TIGAR, while single-sample gene set enrichment analysis (ssGSEA) determined its relevance to cancer immune infiltration. Results: (1) Elevated TIGAR expression was significantly correlated with decreased survival outcomes in HCC patients. (2) GSEA highlighted the significant link between TIGAR and humoral immunity. (3) ssGSEA revealed a positive correlation between TIGAR expression and infiltration of Th1 and Th2 cells and a negative correlation with Th17 cell infiltration. Conclusion: TIGAR, as a potential prognostic biomarker for HCC, holds significant value in immune infiltration. Understanding the role of TIGAR could contribute to improved prognostic predictions and personalized treatment strategies for HCC patients.

Bioinformatics analysis of carotid vulnerable plaques associated with the SARS-CoV-2 pattern.

The rupture of carotid artery vulnerable plaque plays a critical role in ischemic stroke, and the widely spread new coronavirus in recent years plays a certain role in the development of human carotid artery vulnerable plaque, we screened out 27 differential expression genes (DEGs) of stable plaque and vulnerable plaque associated with the new coronavirus. Through the construction of the protein-protein interaction (PPI) network, the Cathepsin B (CTSB) and Niemann-Pick Disease Type 2 (NPC2) were identified as crucial expression genes, and further, we confirmed the validity of core gene expression in two validation sets. Additionally, we discovered a significant connection between CTSB, NPC2 and 28 different kinds of immune cells in carotid plaque tissue. We screened out 65 target interacting drugs based on 10 differentially expressed genes through online tools and finally verified the high expression of 2 core genes in fragile plaques through clinical sample experiments. These findings imply that two core genes may be novel targets for molecular diagnostics and immunotherapy of vulnerable plaques.

Diagnostic Values of METTL1-Related Genes and Immune Characteristics in Systemic Lupus Erythematosus.

Purpose: Methyltransferase like 1 (METTL1) regulates epitranscriptomes via the m7G modification in mammalian mRNA and microRNA. Systemic lupus erythematosus (SLE) is caused by abnormal immune reactivity and has diverse clinical manifestations. RNA methylation as a mechanism to regulate gene expression is widely implicated in immune regulation. However, the role of m7G in immune response of SLE has not been extensively studied. Patients and Methods: Expression of METTL1 was identified in the public dataset GSE122459 and validated in an independent cohort of SLE patients. We investigated the association between METTL1-expression and clinical manifestations of SLE. Subsequently, differentially expressed genes (DEG) that were correlated with METTL1-expression in GSE122459 were used for functional enrichment analysis. The correlation between infiltrating immune cells and METTL1, as well as candidate biomarkers identified to be correlated with either METTL1 or immune cell infiltration were assessed by single-sample GSEA. Potential mechanisms were explored with Gene ontology and KEGG pathway enrichment. Diagnostic performances of candidate biomarkers in SLE were analyzed. Results: The mRNA and protein expression of METTL1 in SLE patients were significantly decreased in both datasets. METTL1-coexpressed DEGs were enriched in several key immune-related pathways. Activated CD8 T cells, activated CD4 T cells, memory B cells and type 2 helper T cells were different between patients with high and low METTL1 expression. Further, activated CD8 T-cells, activated CD4 T-cells, memory B-cells were correlated with METTL1. The genes of LAMP3, CD83, PDCD1LG2, IGKVD3D-20, IGKV5-2, IGKV2D-30, IGLV3-19 and IGLV4-60 were identified as candidate targets that were correlated with immune cell proportion. Moreover, LAMP3, CD83, and PDCD1LG2 expression were of diagnostic value in SLE as indicated by ROC analysis. Conclusion: Our findings suggested that METTL1 and its candidate targets LAMP3, CD83, PDCD1LG2 may be used for diagnosing SLE and could be explored for developing targeted molecular therapy for SLE.

Identification of molecular subtypes of coronary artery disease based on ferroptosis- and necroptosis-related genes.

Aim: Coronary artery disease (CAD) is a heterogeneous disorder with high morbidity, mortality, and healthcare costs, representing a major burden on public health. Here, we aimed to improve our understanding of the genetic drivers of ferroptosis and necroptosis and the clustering of gene expression in CAD in order to develop novel personalized therapies to slow disease progression. Methods: CAD datasets were obtained from the Gene Expression Omnibus. The identification of ferroptosis- and necroptosis-related differentially expressed genes (DEGs) and the consensus clustering method including the classification algorithm used km and distance used spearman were performed to differentiate individuals with CAD into two clusters (cluster A and cluster B) based expression matrix of DEGs. Next, we identified four subgroup-specific genes of significant difference between cluster A and B and again divided individuals with CAD into gene cluster A and gene cluster B with same methods. Additionally, we compared differences in clinical information between the subtypes separately. Finally, principal component analysis algorithms were constructed to calculate the cluster-specific gene score for each sample for quantification of the two clusters. Results: In total, 25 ferroptosis- and necroptosis-related DEGs were screened. The genes in cluster A were mostly related to the neutrophil pathway, whereas those in cluster B were mostly related to the B-cell receptor signaling pathway. Moreover, the subgroup-specific gene scores and CAD indices were higher in cluster A and gene cluster A than in cluster B and gene cluster B. We also identified and validated two genes showing upregulation between clusters A and B in a validation dataset. Conclusion: High expression of CBS and TLR4 was related to more severe disease in patients with CAD, whereas LONP1 and HSPB1 expression was associated with delayed CAD progression. The identification of genetic subgroups of patients with CAD may improve clinician knowledge of disease pathogenesis and facilitate the development of methods for disease diagnosis, classification, and prognosis.

Ferroptosis-related genes identify tumor immune microenvironment characterization for the prediction of prognosis in cervical cancer.

Background: Cervical cancer (CC) is a disease that affects female health; therefore, timely prevention and diagnosis of CC are crucial to decrease its mortality. Ferroptosis, an iron-dependent form of non-apoptotic cell death, is involved in tumor progression. However, the role of ferroptosis-related genes (FRGs) in the immune microenvironment of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) remains unclear. Methods: The data sets of CESC patients, including RNA sequencing (RNA-seq) data and clinical information, were obtained from The Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was used to determine the stromal score, immune score, estimate score, and tumor purity in the CESC patients' data. Additionally, FRGs were identified and used to construct a signature marker for the diagnosis and prognosis of CESC. Patients were assigned to a high- or low-risk group based on their median risk score. The tumor microenvironment (TME), immune infiltration, and functional enrichment were compared between the low- and high-risk groups. Functional analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA), were conducted to explore the underlying mechanisms in the development and prognosis of CESC. Results: The results showed that the estimate score was suitable for predicting the prognosis of CESC patients. Additionally, a prediction model involving four FRGs [phosphatidylethanolamine-binding protein 1 (PEBP1), dual oxidase 1 (DUOX1), iron-sulfur cluster assembly enzyme (ISCU), and cytochrome b (-245) beta subunit (CYBB)] was constructed. The performance of the prognostic model and significant clinical characteristics in predicting CESC prognosis was subsequently validated. Our results showed that the expression of CYBB affected immune cells. Gene functional enrichment analyses showed that these differentially expressed FRGs were mainly enriched in the immunity-related signaling pathways, which indicated that FRGs might affect the development and prognosis of CC by regulating the immune microenvironment. Conclusions: The expression profiles of FRGs are closely related to the TME and the prognostic survival of CESC patients. The interaction between ferroptosis and immunity in the development of CC provides new insight into the molecular mechanisms of CC.

A Novel Immune-Related Gene Signature to Identify the Tumor Microenvironment and Prognose Disease Among Patients With Oral Squamous Cell Carcinoma Patients Using ssGSEA: A Bioinformatics and Biological Validation Study.

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model's predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

Degree of stemness predicts micro-environmental response and clinical outcomes of diffuse large B-cell lymphoma and identifies a potential targeted therapy.

Some cells within a diffuse large B-cell lymphoma (DLBCL) have the genotype of a stem cell, the proportion of which is termed degree of stemness. We interrogated correlations between the degree of stemness with immune and stromal cell scores and clinical outcomes in persons with DLBCL. We evaluated gene expression data on 1,398 subjects from Gene Expression Omnibus to calculate the degree of stemness. Subjects were classified into low- and high-stemness cohorts based on restricted cubic spline plots. Weighted gene co-expression network analysis (WGCNA) was used to screen for stemness-related genes. Immune and stromal scores correlated with the degree of stemness (both P < 0.001). A high degree of stemness correlated with a shorter progression-free survival (PFS; Hazard Ratio [HR; 95% Confidence Interval [CI] =1.90 (1.37, 2.64; P < 0.001) and a shorter survival (HR = 2.29 (1.53, 3.44; P < 0.001). CDC7 expression correlated with the degree of stemness, and CDC7-inhibitors significantly increased apoptosis (P < 0.01), the proportion of cells in G1 phase (P < 0.01), and inhibited lymphoma growth in a mice xenograft model (P = 0.04). Our data indicate correlations between the degree of stemness, immune and stromal scores, PFS, and survival. These data will improve the prediction of therapy outcomes in DLBCL and suggest potential new therapies.

Identification of hub biomarkers and immune cell infiltration characteristics of polymyositis by bioinformatics analysis.

Background: Polymyositis (PM) is an acquirable muscle disease with proximal muscle involvement of the extremities as the main manifestation; it is a category of idiopathic inflammatory myopathy. This study aimed to identify the key biomarkers of PM, while elucidating PM-associated immune cell infiltration and immune-related pathways. Methods: The gene microarray data related to PM were downloaded from the Gene Expression Omnibus database. The analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) networks were performed on differentially expressed genes (DEGs). The hub genes of PM were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) algorithm, and the diagnostic accuracy of hub markers for PM was assessed using the receiver operating characteristic curve. In addition, the level of infiltration of 28 immune cells in PM and their interrelationship with hub genes were analyzed using single-sample GSEA. Results: A total of 420 DEGs were identified. The biological functions and signaling pathways closely associated with PM were inflammatory and immune processes. A series of four expression modules were obtained by WGCNA analysis, with the turquoise module having the highest correlation with PM; 196 crossover genes were obtained by combining DEGs. Subsequently, six hub genes were finally identified as the potential biomarkers of PM using LASSO algorithm and validation set verification analysis. In the immune cell infiltration analysis, the infiltration of T lymphocytes and subpopulations, dendritic cells, macrophages, and natural killer cells was more significant in the PM. Conclusion: We identified the hub genes closely related to PM using WGCNA combined with LASSO algorithm, which helped clarify the molecular mechanism of PM development and might have great significance for finding new immunotherapeutic targets, and disease prevention and treatment.

Risk stratification of acute myeloid leukemia: Assessment using a novel prediction model based on ferroptosis-immune related genes.

In acute myeloid leukemia (AML), the link between ferroptosis and the immune microenvironment has profound clinical significance. The objective of this study was to investigate the role of ferroptosis-immune related genes (FIRGs) in predicting the prognosis and therapeutic sensitivity in patients with AML. Using The Cancer Genome Atlas dataset, single sample gene set enrichment analysis was performed to calculate the ferroptosis score of AML samples. To search for FIRGs, differentially expressed genes between the high- and low-ferroptosis score groups were identified and then cross-screened with immune related genes. Univariate Cox and LASSO regression analyses were performed on the FIRGs to establish a prognostic risk score model with five signature FIRGs (BMP2, CCL3, EBI3, ELANE, and S100A6). The prognostic risk score model was then used to divide the patients into high- and low-risk groups. For external validation, two Gene Expression Omnibus cohorts were employed. Overall survival was poorer in the high-risk group than in the low-risk group. The novel risk score model was an independent prognostic factor for overall survival in patients with AML. Infiltrating immune cells were also linked to high-risk scores. Treatment targeting programmed cell death protein 1 may be more effective in high-risk patients. This FIRG-based prognostic risk model may aid in optimizing prognostic risk stratification and treatment of AML.

Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a malignancy with a poor prognosis. This study aimed to distinguish patients with HCC having distinct tumour immune microenvironment (TIME) features and construct an immune-related gene signature (IRGs) to assess prognosis and provide a basis for personalised therapies. Methods: Transcriptomic data of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We assessed the immune cell infiltration in each HCC specimen using single sample gene set enrichment analysis (ssGSEA) and classified all patients with HCC into high- and low-immune clusters using a hierarchical clustering algorithm. The ESTIMATE and CIBERSORT computational methods were employed to verify the stability and effectiveness of the immune clusters. Subsequently, the differentially expressed genes (DEGs) of the high- and low-immune clusters and the immune-related genes intersected to obtain the immune-related DEGs. The least absolute shrinkage and selection operator (LASSO) was then employed to screen the optimal genes for the construction of a prognostic predictive signature and to divide patients into high- and low-risk subgroups. The predictive efficacy of the IRGs was further confirmed using Kaplan-Meier survival curves, univariate and multifactorial Cox regression and time-dependent ROC curves in the TCGA and GSE14520 validation cohorts. Furthermore, we developed a nomogram to predict the prognosis. Tumour mutation burden (TMB) was also analysed in the risk groups. Additionally, gene ontology and gene set variation analysis were used for biological function and pathway exploration. Lastly, drug sensitivity analyses were employed to investigate prospective therapeutics in the two risk populations. Results: Immune cluster analysis based on ssGSEA could well distinguish the TIME characteristics of patients with HCC. The stromal score, immune score and ESTIMATE score were all lower in the low-immune cluster. Meanwhile, most of the immune checkpoint-related genes and HLA family genes were overexpressed in the high-immune cluster, suggesting that this cluster could be a beneficial population for immune checkpoint inhibitors therapy. There were 1,617 DEGs between the two immune clusters, of which 414 genes intersected with immune-associated genes. Furthermore, Cox regression analysis revealed 49 DEGs that were associated with survival. Then, 19 DEGs were screened using the LASSO algorithm for IRGs construction and patients were classified into high- and low-risk groups. Both the constructed signature and nomogram had good prognostic predictive efficacy. The signature-based risk score was an independent prognostic predictor in both the TCGA and GSE14520 cohorts. Additionally, there was no significant difference in TMB between the two risk populations. Lastly, the half-maximal inhibitory concentrations of certain chemotherapeutic and targeted therapeutic agents differed between the two risk groups. Conclusion: Our study provides a personalized tool for predicting the prognosis and TIME landscape of HCC and a basis for developing personalised treatment regimens.

Identification and Verification of Immune-Related Genes Prognostic Signature Based on ssGSEA for Adrenocortical Carcinoma (ACC).

PURPOSE: Adrenocortical carcinoma (ACC) is an endocrine malignant tumor with poor prognosis. The study aimed to construct ACC immune-related gene prognostic signature and verify the efficacy of prognostic signature. METHODS: ACC RNA-seq data and clinical information are downloaded from TCGA databases and GEO databases. We used single sample gene set enrichment analysis (ssGSEA) to assess immune cell infiltration in ACC patients and ACC patients were divided into high- and low-immune cell infiltration clusters. The validity of ssGSEA grouping was verified using the ESTIMATE algorithm. A total of 275 differentially expressed immune-related genes (IRGs) were obtained from the intersection of IRGs and differentially expressed genes (DEGs) in high and low immune cell infiltration clusters. LASSO analysis was used to identify 13 IRGs that regulate the prognosis of ACC patients through immune infiltration. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that these 13 immune-related gene signatures were innovative and significant prognostic factors, which were independent of clinical features. Finally, ACC prognostic nomogram was constructed, ROC curve and calibration curve were drawn to evaluate the accuracy of the prognostic nomogram. RESULTS: LASSO regression analysis was used to screen out ACC survival-related genes. Univariate and multivariate Cox proportional risk regression models were used to analyze and construct the ACC prognosis nomogram. The AUC for predicting 1-, 3- and 5-year overall survival rate of ACC patients was 0.799, 0.966 and 0.969, suggesting good prediction accuracy. The calibration curve shows that the predicted results of the prognostic nomogram are in good agreement with the actual situation. CONCLUSION: ssGSEA technique plays an important role in the construction of ACC prognostic model. Based on IRGs associated with survival independently predicted ACC prognosis, we identified thirteen immune-related genes as prognostic signature for ACC.

Classification of lung adenocarcinoma based on stemness scores in bulk and single cell transcriptomes.

Tumor stemness is associated with tumor progression and therapy resistance. The recent advances in sequencing, genomics, and computational technologies have facilitated investigation into the tumor stemness cell-like characteristics. We identified subtypes of lung adenocarcinoma (LUAD) in bulk tumors or single cells based on the enrichment scores of 12 stemness signatures by clustering analysis of their transcriptomic profiles. Three stemness subtypes of LUAD were identified: St-H, St-M, and St-L, having high, medium, and low stemness signatures, respectively, consistently in six different datasets. Among the three subtypes, St-H was the most enriched in epithelial-mesenchymal transition, invasion, and metastasis signaling, genomically instable, irresponsive to immunotherapies and targeted therapies, and hence had the worst prognosis. We observed that intratumor heterogeneity was significantly higher in high-stemness than in low-stemness bulk tumors, but significantly lower in high-stemness than in low-stemness single cancer cells. Moreover, tumor immunity was stronger in high-stemness than in low-stemness cancer cells, but weaker in high-stemness than in low-stemness bulk tumors. These differences between bulk tumors and single cancer cells could be attributed to the non-tumor cells in bulk tumors that confounded the results of correlation analysis. Furthermore, pseudotime analysis showed that many St-H cells were at the beginning of the cell evolution trajectory, compared to most St-L cells in the terminal or later phase, suggesting that many low-stemness cells are originated from high-stemness cells. The stemness-based classification of LUAD may provide novel insights into the tumor biology as well as precise clinical management of this disease.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma.

Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.