Gene Set Enrichment Analysis Method Research Articles

Immunogenic cell death signatures from on-treatment tumor specimens predict immune checkpoint therapy response in metastatic melanoma

Melanoma is a highly malignant form of skin cancer that typically originates from abnormal melanocytes. Despite significant advances in treating metastatic melanoma with immune checkpoint blockade (ICB) therapy, a substantial number of patients do not respond to this treatment and face risks of recurrence and metastasis. This study collected data from multiple datasets, including cohorts from Riaz et al., Gide et al., MGH, and Abril-Rodriguez et al., focusing on on-treatment samples during ICB therapy. We used the single-sample gene set enrichment analysis (ssGSEA) method to calculate immunogenic cell death scores (ICDS) and employed an elastic network algorithm to construct a model predicting ICB efficacy. By analyzing 18 ICD gene signatures, we identified 9 key ICD gene signatures that effectively predict ICB treatment response for on-treatment metastatic melanoma specimens. Results showed that patients with high ICD scores had significantly higher response rates to ICB therapy compared to those with low ICD scores. ROC analysis demonstrated that the AUC values for both the training and validation sets were around 0.8, indicating good predictive performance. Additionally, survival analysis revealed that patients with high ICD scores had longer progression-free survival (PFS). This study used an elastic network algorithm to identify 9 ICD gene signatures related to the immune response in metastatic melanoma. These gene features can not only predict the efficacy of ICB therapy but also provide references for clinical decision-making. The results indicate that ICD plays an important role in metastatic melanoma immunotherapy and that expressing ICD signatures can more accurately predict ICB treatment response and prognosis for on-treatment metastatic melanoma specimens, thus providing a basis for personalized treatment.

Construction of a prognostic risk model based on pyroptosis-related genes and comprehensive analysis of key genes and tumor immune microenvironment for colon cancer.

Pyroptosis-related genes have great potential for prognosis, an accurate prognostic model based on pyroptosis genes has not been seen in Colorectal adenocarcinoma (COAD). Furthermore, understanding the mechanisms of gene expression characteristics and the Tumor Immune Microenvironment associated with the prognosis of COAD is still largely unknown. Constructing a prognostic model based on pyroptosis-related genes, and revealing prognosis-related mechanisms associated with the gene expression characteristics and tumor microenvironment. 59 pyroptosis-related genes were collected. The gene expression data and clinical data of COAD were downloaded from The Cancer Genome Atlas. External validation datasets were downloaded from the Gene Expression Omnibus database. 10 characteristic genes with prognostic values were obtained using univariate and LASSO Cox. 10-gene Riskscore prognostic model was constructed. Both gene set enrichment analysis and network propagation methods were used to find pathways and key genes leading to different prognostic risks. The area under the ROC curves were used to evaluate the performance of the model to distinguish between high-risk and low-risk patients, the results were 0.718, 0.672, and 0.669 for 1-, 3-, and 5-year survival times. A nomogram based on Riskscore and clinical characteristics showed the probability of survival at 1, 3, and 5 years, and the calibration curves showed good agreement between the predicted and actual observations, its C-index is 0.793. The decision curves showed that the net benefit of the nomogram was significantly superior to that of the other single variables. Four key pathways leading to different prognostic risks were obtained. Six key genes with prognostic value, significant expression differences (P < .05) and significant survival differences (P < .05) between high/low risk groups were obtained from the gene set of all 4 key pathways. This study constructed a prognostic model for COAD using 10 pyroptosis-related genes with prognostic value. This study also revealed significant differences in specific pathways and the tumor immune microenvironment (TME) between the high-risk group and the low-risk group, highlighted the roles of ALDH5A1 and Wnt signaling in promoting COAD and the suppressive effects of the IL-4/IL-13 pathway and RORC on COAD. The study will be helpful for precision therapy.

A best-match approach for gene set analyses in embedding spaces.

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

Analysis of the role of POC1A in the development and progression of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers worldwide. POC1 centriolar protein A (POC1A) is a gene encoding a protein that plays a key role in the centrosome, and is one of the two isoforms of POC1. To date, the expression of POC1A in HCC and its potential as a biomarker and tumor therapeutic target have not been examined. This study aimed to explore the effect of POC1A on patients with HCC and its potential mechanism. This study investigated the role of POC1A in the occurrence and development of HCC. It analyzed the expression of POC1A in various types of HCC patients and its effect on survival using HCC patient information from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), the Human Protein Atlas (HPA), and the Hepatocellular Carcinoma Cell DataBase (HCCDB). It then explored the major enrichment pathways and gene functions of POC1A in HCC using the gene set enrichment analysis (GSEA) method and examined its protein-protein interactions (PPIs). Finally, it predicted the potential transcription factors (TFs) and target microRNAs (miRNAs) of POC1A, and analyzed the single nucleotide variation (SNV) and copy number variation (CNV) mutations of POC1A and the related genes in HCC, as well as their effects on immune cells. The results showed that POC1A was significantly overexpressed in HCC and was significantly associated with a poor prognosis. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that POC1A was mainly involved in the regulation of cell-cycle pathways and chromosome segregation functions. POC1A showed significant interactions with NUDC and PPARG, and they both had different numbers of SNV and CNV mutations in the HCC samples. In relation to immunity, the high expression of POC1A and its reciprocal genes may play an important role in B cells and macrophages. In general, our findings suggest that POC1A overexpression could have an important effect on the development of HCC by regulating cell-cycle pathways, and that it could serve as a novel prognostic biomarker and a potential therapeutic target for HCC.

A comprehensive landscape analysis of autophagy in cancer development and drug resistance.

Autophagy plays important roles in cancer progression and therapeutic resistance, and the autophagy underlying the tumor pathogenesis and further mechanisms of chemoresistance emergence remains unknown. In this study, via the single-sample gene set enrichment analysis (ssGSEA) method, an autophagy 45-gene list was identified to evaluate samples' autophagy activity, verified through six GEO datasets with a confirmed autophagy phenotype. It was further utilized to distinguish tumors into autophagy score-high and score-low subtypes, and analyze their transcriptome landscapes, including survival analysis, correlation analysis of autophagy- and resistance-related genes, biological functional enrichment, and immune- and hypoxia-related and genomic heterogeneity comparison, in TCGA pan-cancer datasets. Furthermore, we performed an analysis of autophagy status in breast cancer chemoresistance combined with multiple GEO datasets and in vitro experiments to validate the mechanisms of potential anticancer drugs for reversing chemoresistance, including CCK-8 cell viability assays, RT-qPCR, and immunofluorescence. The 45-gene list was used to identify autophagy score-high and score-low subtypes and further analyze their multi-dimensional features. We demonstrated that cancer autophagy status correlated with significantly different prognoses, molecular alterations, biological process activations, immunocyte infiltrations, hypoxia statuses, and specific mutational processes. The autophagy score-low subtype displayed a more favorable prognosis compared with the score-high subtype, associated with their immune-activated features, manifested as high immunocyte infiltration, including high CD8+T, Tfh, Treg, NK cells, and tumor-associated macrophages M1/M2. The autophagy score-low subtype also showed a high hypoxia score, and hypoxic tumors showed a significantly differential prognosis in different autophagy statuses. Therefore, "double-edged" cell fates triggered by autophagy might be closely correlated with the immune microenvironment and hypoxia induction. Results demonstrated that dysregulated autophagy was involved in many cancers and their therapeutic resistance and that the autophagy was induced by the resistance-reversing drug response, in five breast cancer GEO datasets and validated by in vitro experiments. In vitro, dihydroartemisinin and artesunate could reverse breast cancer doxorubicin resistance, through inducing autophagy via upregulating LC3B and ATG7. Our study provided a comprehensive landscape of the autophagy-related molecular and tumor microenvironment patterns for cancer progression and resistance, and highlighted the promising potential of drug-induced autophagy in the activation of drug sensitivity and reversal of resistance.

Identification of sanguinarine as a novel antagonist for perfluorooctanoate/perfluorooctane sulfonate-induced senescence of hepatocytes: An integrated computational and experimental analysis

Perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS), two prominent per- and polyfluoroalkyl substances (PFASs), are potentially harmful to many human organs. However, there only exist limited methods to mitigate their health hazards. The aim of this study is to combine a bioinformatics analysis with in vitro experiments to discover small molecules that can alleviate liver damage caused by PFOA/PFOS. We identified 192 and 82 key genes related to hepatocytes exposed to PFOA and PFOS, respectively. The functional enrichment analysis of key genes suggested cellular senescence may be important in PFOA/PFOS-induced hepatotoxicity. The in vitro models revealed that PFOA/PFOS led to hepatocyte senescence by increasing the activity of SA-β-gal, inducing mitochondrial dysfunction, impacting cell cycle arrest, and elevating the expressions of p21, p53, IL-1β, and SASP-related cytokines. The drug-target gene set enrichment analysis method was employed to compare the transcriptome data from the Gene Expression Omnibus database (GEO), Comparative Toxicogenomics Database (CTD), and the high-throughput experiment- and reference-guided database (HERB), and 21 traditional Chinese medicines (TCMs) were identified that may alleviate PFOA/PFOS-induced liver aging. The experimental results of co-exposure to PFOA/PFOS and TCMs showed that sanguinarine has particular promise in alleviating cellular senescence caused by PFOA/PFOS. Further investigations revealed that the mTOR-p53 signaling pathway was involved in PFOA/PFOS-mediated hepatic senescence and can be blocked using sanguinarine.

Modulation of Neuronal Excitability and Plasticity by BHLHE41 Conveys Lithium Non-Responsiveness.

Many bipolar disorder (BD) patients are non-responsive to lithium. The mechanisms underlying lithium (non-)responsiveness are largely unknown. By using gene-set enrichment analysis methods, we found that core clock gene-sets are significantly associated with lithium response. Among the top hits was BHLHE41, a modulator of the molecular clock and homeostatic sleep. Since BHLHE41 and its paralog BHLHE40 are functionally redundant, we assessed chronic lithium response in double-knockout mutant mice (DKO). We demonstrated that DKOs are non-responsive to lithium's effect in various behavioral tasks. Cellular assays and patch clamp recordings revealed lowered excitability and reduced lithium-response in prefrontal cortical layer 2/3 DKO neurons and on hippocampal long-term potentiation. Single-cell RNA sequencing identified that lithium deregulated mitochondrial respiration, cation channel and postsynapse associated gene-sets specifically in upper layer excitatory neurons. Our findings show that lithium acts in a highly cell-specific way on neuronal metabolism and excitability and modulates synaptic plasticity depending on BHLHE40/41.

Identification of Immune Infiltration-related Molecular Features in Ovarian Cancer Patients and Experimental Validation of Immune Response Molecular Mechanisms through Integrated WGCNA, Machine Learning, and Single-cell Sequencing Analysis.

Ovarian cancer is one of the most common gynecological malignancies globally, and immunotherapy has emerged as a promising treatment strategy in recent years. However, the effectiveness of immunotherapy is often limited by immune escape mechanisms. To unravel the immune response mechanisms in ovarian cancer, this study aimed to employ integrated Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-- cell sequencing analysis to systematically investigate immune infiltration-related molecular features in ovarian cancer patients and experimentally validate the molecular mechanisms of the immune response. This research may provide a new theoretical foundation and treatment strategy for immune-based therapies in ovarian cancer. Relevant ovarian cancer datasets were collected from public databases. The ConsensusCluster- Plus and ggplot2 R packages were used to perform dimensionality reduction and clustering analysis of immune infiltration-related genes. Various algorithms were employed to select the best ovarian cancer prognostic model with OC consistency. The prognostic value of angiogenesis and immune-related gene expression was evaluated through Kaplan-Meier survival analysis, and the impact of immune infiltration on immune function in ovarian cancer patients was assessed. Functional pathways were identified using the Gene Set Enrichment Analysis (GSEA) method, and the infiltration abundance of immune and stromal components was inferred using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The influence of angiogenesis on the cellular level of Ovarian Cancer (OC) was explored in single- cell sequencing data, followed by in vitro cell experiments for further validation. The effect of the angiogenesis model on OC was evaluated through the above-mentioned research and experiments, aiming to investigate the mechanism of targeted therapy strategies in ovarian cancer. Immune-related data were collected from ovarian cancer patients in this study. Through WGCNA analysis, the MEturquoise module was identified, and a total of 1018 hub genes were determined. A prediction model was constructed using machine learning, with CoxBoost+StepCox selected as the best model, leading to the identification of 10 genes associated with ovarian cancer. Patients with high AIDPS had shorter survival time, and GSEA analysis revealed enrichment in immune-related pathways. Single-sample gene set enrichment analysis demonstrated increased immune cell infiltration and malignant stromal changes in the high AIDPS group. Results from in vitro cell experiments showed that silencing RPL31 inhibited the proliferation and migration of ovarian cancer cells while enhancing immune response capability. AIDPS holds significant clinical significance in Ovarian Cancer (OC) with poor prognosis observed in patients with high AIDPS. These patients exhibit more significant genomic variations, denser immune cell infiltration, and greater tolerance toward immune therapy. Importantly, inhibiting the expression of RPL31, a key component of AIDPS, can significantly suppress the proliferation, migration, and invasive properties of ovarian cancer cells, while stimulating the cytotoxicity of effector T cells and promoting immune response, thus slowing down the progression of ovarian cancer.

MUC16 as a serum-based prognostic indicator of prometastatic gastric cancer

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5’-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.

Construction of m7G RNA modification-related prognostic model and prediction of immune therapy response in hepatocellular carcinoma.

RNA plays an important role in tumorigenesis. Changes in RNA may cause changes in the biological function. The N7-methylguanosine (m7G) methylation modification performs an integral function in tumor progression as the most widely existed RNA modification. Hepatocellular carcinoma (HCC) is among the greatest threats to human health worldwide. Low detection rates remain the main cause of advanced disease progression. Therefore, finding significant biomarkers for prognosis prediction and immune therapy response in HCC is valuable and urgently needed. RNA expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Different subtypes screening was finished by consensus cluster. Different expression was performed by R software. The results were validated by western blot (WB) methods. Genes with HCC prognostic potential were identified utilizing least absolute shrinkage and selection operator (LASSO) analyses. A prognosis model was established with the help of the risk score that we calculated. Related genes screening and protein-protein interactions (PPI) network construction were performed using the GeneMANIA database. Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) databases. In addition, gene set enrichment analysis (GSEA) of key genes and immune infiltration status were both done by R software. Finally, the immune infiltration was performed by cibersort method and single sample GSEA (ssGSEA) method. The response of immune therapy was validated by Tumor Immune Dysfunction and Exclusion database (TIDE) and the immune therapy cohort in GEO database. We found that two different subtypes related with m7G RNA modification and four genes associated with m7G RNA modification were differentially expressed in the TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Additionally, to examine the value of these four genes in the HCC patients' prognoses according to the LASSO, we selected three genes, including WDR4, AGO2, and NCBP2, as prognostic related genes. Premised on the expression of these three genes, a risk score model and nomogram were constructed to provide a prediction of the HCC patients' prognoses. We performed functional annotation and created a PPI network based on the three genes (WDR4, NCBP2, and AGO2). Using R software, we performed the GSEA and immune regulation analyses. Finally, we predicted the relationship between the gene expression and the response of immune therapy. Our study suggests that high expression of m7G RNA modification subtype is related with poor prognosis and immune response. WDR4, AGO2, and NCBP2 are key regulators of m7G RNA modification which can be clinically promising biomarkers that can be used to treat HCC. In addition, our risk score model was shown to have a strong link to OS in patients with HCC.

Machine learning‐based identification of a cell death‐related signature associated with prognosis and immune infiltration in glioma

AbstractAccumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell‐type identification was estimated by using the cell‐type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time‐dependent ROC and calibration plots. In addition, a high‐risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death‐based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD‐L1 and vimentin expression in the in‐horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

Association of angiogenesis-associated genes with atherosclerotic plaque progression, intraplaque hemorrhage, and immune infiltration

Mounting evidence suggests that intraplaque angiogenesis is associated with the progression of atherosclerotic plaques and the development of intraplaque hemorrhage. The specificity of intraplaque immune cell infiltration may be associated with abnormalities in the structure and function of the nascent capillaries. Here, we analyzed expression levels of angiogenesis-associated genes in early and advanced carotid atheromatous plaque tissues as well as in stable and intraplaque hemorrhage plaques. Expression profiles of advanced arterial plaques based on angiogenesis-associated genes were classified into subtypes by performing a consensus clustering analysis. The correlation between the immune microenvironment of plaques and expression of angiogenesis-associated genes was also explored using the single sample gene set enrichment analysis method and the CIBERSORT algorithm. We identified hub angiogenesis-associated genes showing similar expression patterns throughout plaque adverse progression, and constructed a prediction model using the random forest algorithm. Receiver operating curves were constructed to evaluate efficacy in identification of intraplaque hemorrhage in a plaque. Our results suggest that heterogeneity of angiogenesis-related genes may promote the malignant development of plaques and cause plaque rupture. In conclusion, we propose a model based on expression of angiogenesis-related genes to predict the risk of plaque rupture.

#2423 Molecular classification of lupus nephritis based on immune pathway profiling—correlations with histological class and clinical manifestations

Abstract Background and Aims The ISN/RPS 2003 classification is the current gold standard histopathological classification for lupus nephritis (LN), and has important roles in guiding treatment and predicting prognosis. The classification system, largely based on light microscopy features, does not inform the underlying immunological aberrations. A classification based on molecular typing of immune-related pathways may better stratify LN patients and help select treatments that target specific immune cascades. Method The normalized expression matrix and clinical data were extracted from Gene Expression Omnibus database, Human Cell Atlas (Hacohen-Human-CELseq2) and Nephroseq. Four chip datasets (GSE127797, 32591, 69438 and 37463) and one single cell dataset were analyzed. Sample gene set enrichment analysis (ssGSEA) method was used to derive immune phenotype with gene expression data. The hierarchical clustering with ssGSEA scores of selected gene sets was used to classify LN tissues. Functional characterization and protein-protein interactions of the differential expression genes (DEGs) of three groups were compared with healthy donors using Metascape (absolute log FC &amp;gt; 1 and adjusted P-value &amp;lt; .05). Kruskal-Walli test and chi-square test were performed to compare the clinical parameters of the different groups. Results We analyzed 96 immune-related gene sets in the glomerular and tubulointerstitial regions of LN kidney biopsy samples. The immune-related genes were categorized into three clusters (Immune-Mild, Immune-Moderate and Immune-Severe) based on the degree of immune pathway activation. Interferon-stimulated genes are a hallmark of LN, with high signals in all three clusters. When compared to other groups, the Immune-Mild cluster from the glomerular region showed no T-cell activation or T cell receptor pathway. Also, the Immune-Mild (tubulointerstitial) group also showed minimal lymphocyte activation or migration. Protein-protein interactions networks with MOCDE components further suggested that STAT1 and ISG15 the hub genes in different clusters. The gene expressions of hub genes were then compared among epithelial cells and 21 immune cells from LN and heath controls. Immune clusters are correlated with clinical phenotypes, in which Immune-Mild Group (Glomerular) showed high prevalence of Class II and V LN, and Immune-Mild (Tubulointerstitial) showed more Class V LN. Immune-Severe group also showed higher degree of proteinuria and blood pressure but lower eGFR compared to Immune-Moderate and Immune-Mild groups. Conclusion A new molecular classification of LN based on the degree of immune pathway activation shows correlation with clinical manifestations and may potentially guide personalized therapy in LN patients.

Identification of a fatty acid metabolism-related gene signature to predict prognosis in stomach adenocarcinoma.

Fatty acid metabolism (FAM) contributes to tumorigenesis and tumor development, but the role of FAM in the progression of stomach adenocarcinoma (STAD) has not been comprehensively clarified. The expression data and clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA). FAM pathway was analyzed by gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) methods. Univariate Cox regression analysis was conducted to select prognosis genes. Molecular subtypes were classified by consensus clustering analysis. Furthermore, least absolute shrinkage and selection operator (Lasso) analysis was employed to develop a risk model. ESTIMATE and tumour immune dysfunction and exclusion (TIDE) algorithm were used to assess immunity. pRRophetic package was conducted to predict drug sensitivity. Based on 14 FAM related prognosis genes (FAMRG), 2 clusters were determined. Patients in C2 showed a worse overall survival (OS). Furthermore, a 7-FAMRG risk model was established as an independent predictor for STAD, with a higher riskscore indicating an unfavorable OS. High riskscore patients had higher TIDE score and these patients were more sensitive to anticancer drugs such as Bortezomib, Dasatinib and Pazopanib. A nomogram based on riskscore was an effective prediction tool applicable to clinical settings. The results from pan-cancer analysis supported a prominent application value of riskscore model in other cancer types. The FAMRGs model established in this study could help predict STAD prognosis and offer new directions for future studies on dysfunctional FAM-induced damage and anti-tumor drugs in STAD disease.

Analysis and validation of biomarkers of immune cell-related genes in postmenopausal osteoporosis: An observational study.

Postmenopausal osteoporosis (PMOP) is a common metabolic inflammatory disease. In conditions of estrogen deficiency, chronic activation of the immune system leads to a hypo-inflammatory phenotype and alterations in its cytokine and immune cell profile, although immune cells play an important role in the pathology of osteoporosis, studies on this have been rare. Therefore, it is important to investigate the role of immune cell-related genes in PMOP. PMOP-related datasets were downloaded from the Gene Expression Omnibus database. Immune cells scores between high bone mineral density (BMD) and low BMD samples were assessed based on the single sample gene set enrichment analysis method. Subsequently, weighted gene co-expression network analysis was performed to identify modules highly associated with immune cells and obtain module genes. Differential analysis between high BMD and low BMD was also performed to obtain differentially expressed genes. Module genes are intersected with differentially expressed genes to obtain candidate genes, and functional enrichment analysis was performed. Machine learning methods were used to filter out the signature genes. The receiver operating characteristic (ROC) curves of the signature genes and the nomogram were plotted to determine whether the signature genes can be used as a molecular marker. Gene set enrichment analysis was also performed to explore the potential mechanism of the signature genes. Finally, RNA expression of signature genes was validated in blood samples from PMOP patients and normal control by real-time quantitative polymerase chain reaction. Our study of PMOP patients identified differences in immune cells (activated dendritic cell, CD56 bright natural killer cell, Central memory CD4 T cell, Effector memory CD4 T cell, Mast cell, Natural killer T cell, T follicular helper cell, Type 1 T-helper cell, and Type 17 T-helper cell) between high and low BMD patients. We obtained a total of 73 candidate genes based on modular genes and differential genes, and obtained 5 signature genes by least absolute shrinkage and selection operator and random forest model screening. ROC, principal component analysis, and t-distributed stochastic neighbor embedding down scaling analysis revealed that the 5 signature genes had good discriminatory ability between high and low BMD samples. A logistic regression model was constructed based on 5 signature genes, and both ROC and column line plots indicated that the model accuracy and applicability were good. Five signature genes were found to be associated with proteasome, mitochondria, and lysosome by gene set enrichment analysis. The real-time quantitative polymerase chain reaction results showed that the expression of the signature genes was significantly different between the 2 groups. HIST1H2AG, PYGM, NCKAP1, POMP, and LYPLA1 might play key roles in PMOP and be served as the biomarkers of PMOP.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Developing a RiskScore Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction.

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment. Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD. To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment. Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package. Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment. We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

In silico investigation of potential COVID-19-associated microRNA signatures

Purpose: The global pandemic COVID-19, caused by the coronavirus SARS-CoV-2, is persistent despite the increasing vaccination rates, with new cases being reported per week. MicroRNAs, that is, non-coding RNA species that regulate gene expression at the post-transcriptional level, play a pivotal role in the SARS-CoV-2 life cycle, pathophysiology and host’s anticoronaviral responses. The objective of this study was the in silico discovery of functionally associated miRNAs that likely co-regulate COVID-19-related genes &#x0D; Materials and Methods: In the present study, an integrative bioinformatics approach was employed, including database searching, gene set enrichment analysis, network-based and microRNA target prediction methods, towards the discovery of epigenetic determinants of COVID-19.&#x0D; Results: An intricate microRNA-target gene network was constructed, and a set of 8 highly interacting microRNAs, that potentially co-target and co-regulate key COVID-19-related genes, was detected. These miRNAs and their corresponding genes are likely involved in the host’s response to SARS-CoV-2 infection.&#x0D; Conclusion: The 8 functionally associated miRNAs could constitute a signature for COVID-19 diagnosis.

Bioinformatics analysis identifies potential autophagy key genes and immune infiltration in preeclampsia.

Preeclampsia (PE) is a disease that seriously threatens maternal and fetal health. Appropriate autophagy can shield the placenta from oxidative stress, but its role in PE is unclear. To identify potential autophagy-related genes in PE. Microarray datasets from the Gene Expression Omnibus database, compassing the test dataset GSE10588, along with validation datasets GSE4707 and GSE60438 GPL10558, were utilized. Differentially expressed genes (DEGs) were identified using the limma R package, intersected with autophagy-related genes. Hub genes were obtained using the Cytoscape software and analyzed via gene set enrichment analysis (GSEA). The diagnostic capability of hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Analysis of immune cell infiltration was conducted using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT methods. Placental tissues were collected from 10 normal pregnant women and 10 preeclamptic pregnant women, and the expression of hub genes was validated through immunohistochemistry and western blot analysis. Analysis of the microarray data identified 2224 DEGs, among which 26 were autophagy-related DEGs identified through intersection with autophagy genes. Ten hub genes were identified. Immune cell infiltration analysis suggested the potential involvement of T regulatory cells (Tregs), natural killer cells, neutrophils, and T follicular helper cells in the pathogenesis of PE. ROC curve analysis indicated promising diagnostic capabilities for EGFR and TP53. Additionally, levels of EGFR and TP53 were significantly higher in placental tissue from PE pregnancies compared to normal pregnancies. EGFR and TP53 may play a role in PE by influencing autophagy.

SLC7A8 overexpression inhibits the growth and metastasis of lung adenocarcinoma and is correlated with a dismal prognosis.

Overexpression of solute carrier family 7 member 8 (SLC7A8) has been shown to relate to the survival time and tumor progression in cancer patients. However, the role of SLC7A8 in lung adenocarcinoma (LUAD) is still obscure. The relationships between SLC7A8 expression in LUAD tissues and clinical values as well as immune infiltration were explored through bioinformatics. The functions and pathways of SLC7A8 in LUAD were investigated using Kyoto Encyclopedia of Genes and Genomes enrichment analysis, Gene Set Enrichment Analysis, Western blotting, and other methods. We found that the expression of SLC7A8 was decreased significantly in LUAD tissues compared with normal tissues, which was related to the dismal survival time and disease progression. Moreover, it carried diagnostic value in LUAD and was a risk factor for dismal prognosis. Receiver operating characteristic curve analysis indicated that the expression level of SLC7A8 carried significant diagnostic value in LUAD. Overexpression of SLC7A8 inhibited the proliferation, invasion, and migration of LUAD cells, likely through a mechanism involving the cell cycle. SLC7A8 expression in LUAD was significantly correlated with the infiltration of immune cells, especially B cells, interstitial dendritic cells, mast cells, CD56 bright cells, natural killer cells, plasmacytoid dendritic cells, T follicular helper cells, T helper 2 and 17 cells, and immune factors. The downregulation of SLC7A8 was related to a dismal prognosis and immune cell infiltration in LUAD. Increasing the expression of SLC7A8 inhibited the growth and migration of LUAD cells, thereby improving the prognosis of patients.