Signature Database Research Articles

A predictive model of immune infiltration and prognosis of head and neck squamous cell carcinoma based on cell adhesion-related genes: including molecular biological validation.

Focal adhesion serves as a bridge between tumour cells and the extracellular matrix (ECM) and has multiple roles in tumour invasion, migration, and therapeutic resistance. However, studies on focal adhesion-related genes (FARGs) in head and neck squamous cell carcinoma (HNSCC) are limited. Data on HNSCC samples were obtained from The Cancer Genome Atlas and GSE41613 datasets, and 199 FARGs were obtained from the Molecular Signatures database. The integrated datasets' dimensions were reduced by the use of cluster analysis, which was also used to classify patients with HNSCC into subclusters. A FARG signature model was developed and utilized to calculate each patient's risk score using least extreme shrinkage and selection operator regression analysis. The risk score was done to quantify the subgroups of all patients. We evaluated the model's value for prognostic prediction, immune infiltration status, and therapeutic response in HNSCC. Preliminary molecular and biological experiments were performed to verify these results. Two different HNSCC molecular subtypes were identified according to FARGs, and patients with C2 had a shorter overall survival (OS) than those with C1. We constructed an FARG signature comprising nine genes. We constructed a FARG signature consisting of nine genes. Patients with higher risk scores calculated from the FARG signature had a lower OS, and the FARG signature was considered an independent prognostic factor for HNSCC in univariate and multivariate analyses. FARGs are associated with immune cell invasion, gene mutation status, and chemosensitivity. Finally, we observed an abnormal overexpression of MAPK9 in HNSCC tissues, and MAPK9 knockdown greatly impeded the proliferation, migration, and invasion of HNSCC cells. The FARG signature can provide reliable prognostic prediction for patients with HNSCC. Apart from that, the genes in this model were related to immune invasion, gene mutation status, and chemosensitivity, which may provide new ideas for targeted therapies for HNSCC.

Liver metastases across cancer types sharing tumor environment immunotolerance can impede immune response therapy and immune monitoring

BackgroundHepatic immune tolerance might contribute to the development of therapeutic resistance to immunotherapy. However, addressing this issue is challenging since the efficacy of immunotherapy in the context of liver metastasis (LM) remains poorly studied. Here, we aimed to establish an LM common immune feature (LMCIF) score to quantify the characteristics of LM immunotolerance across cancer types for assisting clinical disease management. MethodsLarge-scale clinical data were collected to identify the prognosis of LM. Multi-omics datasets of metastatic cancers with LM special immune-related pathways (LMSIPs) from the Molecular Signatures Database (MSigDB)were used to obtain an LMCIF cluster. Based on differential expression genes (DEGs), a novel LMCIF score for the LMCIF cluster was constructed. In addition, multi-omics, and immunohistochemistry (IHC) data from the public and in-house cohorts were used to explore the features of LM, and LMCIF score. ResultsPatients with LM had a worse prognosis and significantly lower infiltration of immune cells than patients with metastasis to other organs when analyzed with combined clinical and RNA sequencing data. After extracting the LMCIF cluster from 373 samples by utilizing 29 LMSIPs and validating them in a microarray cohort, an LMCIF score was established to confirm the role of the immunosuppressive environment as a contributor to the poor prognosis of LM across cancer types. Moreover, this LMCIF score could be used to predict the immune response of cancer patients undergoing immunotherapy. Finally, we identified that the majority of the 31 LMCIF genes exhibited a negative correlation with TME cells in LM patients, one of them, KRT19, which possessed the strongest positive correlation with LMCIF score, was confirmed to have an immunosuppressive effect through IHC analysis. ConclusionsOur results suggest that LM across cancer types share similar immunological profiles that induce immunotolerance and escape from immune monitoring. The novel LMCIF score represents a common liver metastasis immune cluster for predicting immunotherapy response, the results of which might benefit clinical disease management.

Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer.

Lysosome dysfunction has been shown to play an important role in cancer progression. However, few research studies have reported the role of lysosomes in head and neck squamous cell carcinoma (HNSCC) progression. Lysosome-related genes (LRGs) were collected from the Molecular Signatures Database. Differentially expressed lysosome-related genes (DELRGs) were identified from the TCGA-HNSCC dataset. The least absolute shrinkage and selection operator and multivariate Cox regression analysis were used to identify the prognostic genes. The prognostic values and expression of hub DELRGs were further validated by GEO datasets. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data and the single-sample gene set enrichment analysis were applied to evaluate the correlation between cathepsin G (CTSG) and immune infiltrates. Twenty-two DELRGs were identified. Among them, CTSG was an independent prognostic biomarker for HNSCC patients. Gene set enrichment analysis indicated that the potential mechanism of CTSG in regulating HNSCC was associated with the immune- and inflammation-related pathways. CTSG expression was highly correlated with immune cell infiltration. Finally, two potential compounds (CH and MAN) targeting CTSG protein were identified, and their reliability was validated through molecular docking analysis. CTSG was associated with immune infiltration and had prognostic value in HNSCC patients, which may be a potential biomarker for predicting the outcome of immunotherapy.

Integrated analysis revealing a novel stemness-metabolism-related gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant lethal tumor and both cancer stem cells (CSCs) and metabolism reprogramming have been proven to play indispensable roles in HCC. This study aimed to reveal the connection between metabolism reprogramming and the stemness characteristics of HCC, established a new gene signature related to stemness and metabolism and utilized it to assess HCC prognosis and immunotherapy response. The clinical information and gene expression profiles (GEPs) of 478 HCC patients came from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). The one-class logistic regression (OCLR) algorithm was employed to calculate the messenger ribonucleic acid expression-based stemness index (mRNAsi), a new stemness index quantifying stemness features. Differentially expressed analyses were done between high- and low-mRNAsi groups and 74 differentially expressed metabolism-related genes (DEMRGs) were identified with the help of metabolism-related gene sets from Molecular Signatures Database (MSigDB). After integrated analysis, a risk score model based on the three most efficient prognostic DEMRGs, including Recombinant Phosphofructokinase Platelet (PFKP), phosphodiesterase 2A (PDE2A) and UDP-glucuronosyltransferase 1A5 (UGT1A5) was constructed and HCC patients were divided into high-risk and low-risk groups. Significant differences were found in pathway enrichment, immune cell infiltration patterns, and gene alterations between the two groups. High-risk group patients tended to have worse clinical outcomes and were more likely to respond to immunotherapy. A stemness-metabolism-related model composed of gender, age, the risk score model and tumor-node-metastasis (TNM) staging was generated and showed great discrimination and strong ability in predicting HCC prognosis and immunotherapy response.

Topography of mutational signatures in human cancer

The somatic mutations found in a cancer genome are imprinted by different mutational processes. Each process exhibits a characteristic mutational signature, which can be affected by the genome architecture. However, the interplay between mutational signatures and topographical genomic features has not been extensively explored. Here, we integrate mutations from 5,120 whole-genome-sequenced tumors from 40 cancer types with 516 topographical features from ENCODE to evaluate the effect of nucleosome occupancy, histone modifications, CTCF binding, replication timing, and transcription/replication strand asymmetries on the cancer-specific accumulation of mutations from distinct mutagenic processes. Most mutational signatures are affected by topographical features, with signatures of related etiologies being similarly affected. Certain signatures exhibit periodic behaviors or cancer-type-specific enrichments/depletions near topographical features, revealing further information about the processes that imprinted them. Our findings, disseminated via the COSMIC (Catalog of Somatic Mutations in Cancer) signatures database, provide a comprehensive online resource for exploring the interactions between mutational signatures and topographical features across human cancer.

Characteristics of Autophagy-Related Genes, Diagnostic Models, and Their Correlation with Immune Infiltration in Keratoconus.

Keratoconus (KTCN) is one of the most common degenerative keratopathies, significantly affecting vision and even leading to blindness. This study identifies potential biomarkers of KTCN based on the characterization of autophagy-related genes (ARGs) and the construction of a diagnostic model; and explores their relevance to immune infiltrating cells in KTCN. Gene Expression Omnibus (GEO) data were downloaded and ARGs were acquired from GeneCards and Molecular Signatures Database (MSigDB). Autophagy-related differential expression genes (ARDEGs) were discovered through the integration of differentially expressed genes (DEGs) with ARGs, while hub genes of KTCN were discovered by protein-protein interaction (PPI) network analysis. The probable biological roles of these hub ARDEGs were examined using functional enrichment analysis, and a KTCN diagnostic model was generated using theleast absolute shrinkage and selection operator (LASSO) regression analysis. We also employed the CIBERSORTx and ssGSEA algorithms to identify potential regulatory pathways to compare the abundance of immune cell infiltrates and their association with hub genes. Finally, the hub gene expression levels were confirmed using validation datasets as well as blood samples from KTCN and healthy individuals. In this study, we identified 12 hub ARDEGs, of which 9 genes were substantially distinct between KTCN patients and normal groups. The LASSO risk score was used to generate the nomogram, and the calibration curve evaluated the model's effective diagnostic performance (C index of 0.961). Patients with KTCN had greater percentages of M2 Macrophages and Gamma delta T cells, according to CIBERSORTx and ssGSEA. The outcomes of the bioinformatics analysis were supported by the DDIT3 and BINP3 expression levels in KTCN patients and healthy controls, according to the qRT-PCR data. Five biomarkers (CFTR, PLIN2, DDIT3, BAG3, and BNIP3) and diagnostic models offer fresh perspectives on identifying and managing KTCN.

Prognostic Analysis of a Hypoxia-Associated lncRNA Signature in Glioblastoma and its Pan-Cancer Landscape.

Hypoxia is an important clinical feature of glioblastoma (GBM), which regulates a variety of tumor processes and is inseparable from radiotherapy. Accumulating evidence suggests that long noncoding RNAs (lncRNAs) are strongly associated with survival outcomes in GBM patients and modulate hypoxia-induced tumor processes. Therefore, the aim of this study was to establish a hypoxia-associated lncRNAs (HALs) prognostic model to predict survival outcomes in GBM patients. LncRNAs in GBM samples were extracted from The Cancer Genome Atlas database. Hypoxia-related genes were downloaded from the Molecular Signature Database. Co-expression analysis of differentially expressed lncRNAs and hypoxia-related genes in GBM samples was performed to determine HALs. Six optimal lncRNAs were selected for building HALs models by univariate Cox regression analysis. The prediction model has a good predictive effect on the prognosis of GBM patients. Meanwhile, LINC00957 among the six lncRNAs was selected and subjected to pan-cancer landscape analysis. Taken together, our findings suggest that the HALs assessment model can be used to predict the prognosis of GBM patients. In addition, LINC00957 included in the model may be a useful target to study the mechanism of cancer development and design individualized treatment strategies.

Computational Oil-Slick Hub for Offshore Petroleum Studies

The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies.

Molecular basis of GDF15 induction and suppression by drugs in cardiomyocytes and cancer cells toward precision medicine

GDF15 has recently emerged as a key driver of the development of various disease conditions including cancer cachexia. Not only the tumor itself but also adverse effects of chemotherapy have been reported to contribute to increased GDF15. Although regulation of GDF15 transcription by BET domain has recently been reported, the molecular mechanisms of GDF15 gene regulation by drugs are still unknown, leaving uncertainty about the safe and effective therapeutic strategies targeting GDF15. We screened various cardiotoxic drugs and BET inhibitors for their effects on GDF15 regulation in human cardiomyocytes and cancer cell lines and analyzed in-house and public gene signature databases. We found that DNA damaging drugs induce GDF15 in cardiomyocytes more strongly than drugs with other modes of action. In cancer cells, GDF15 induction varied depending on drug- and cell type-specific gene signatures including mutations in PI3KCA, TP53, BRAF and MUC16. GDF15 suppression by BET inhibition is particularly effective in cancer cells with low activity of the PI3K/Akt axis and high extracellular concentrations of pantothenate. Our findings provide insights that the risk for GDF15 overexpression and concomitant cachexia can be reduced by a personalized selection of anticancer drugs and patients for precision medicine.

Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke

Ischemic stroke (IS) is a major threat to human health, and it is the second leading cause of long-term disability and death in the world. Impaired cerebral perfusion leads to acute hypoxia and glucose deficiency, which in turn induces a stroke cascade response that ultimately leads to cell death. Screening and identifying hypoxia-related genes (HRGs) and therapeutic targets is important for neuroprotection before and during brain recanalization to protect against injury and extend the time window to further improve functional outcomes before pharmacological and mechanical thrombolysis. First, we downloaded the GSE16561 and GSE58294 datasets from the NCBI GEO database. Bioinformatics analysis of the GSE16561 dataset using the limma package identified differentially expressed genes (DEGs) in ischemic stroke using adj. p. values < 0.05 and a fold change of 0.5 as thresholds. The Molecular Signature database and Genecards database were pooled to obtain hypoxia-related genes. 19 HRGs associated with ischemic stroke were obtained after taking the intersection. LASSO regression and multivariate logistic regression were applied to identify critical biomarkers with independent diagnostic values. ROC curves were constructed to validate their diagnostic efficacy. We used CIBERSORT to analyze the differences in the immune microenvironment between IS patients and controls. Finally, we investigated the correlation between HRGs and infiltrating immune cells to understand molecular immune mechanisms better. Our study analyzed the role of HRGs in ischemic stroke. Nineteen hypoxia-related genes were obtained. Enrichment analysis showed that 19 HRGs were involved in response to hypoxia, HIF-1 signaling pathway, autophagy, autophagy of mitochondrion, and AMPK signaling pathway. Because of the good diagnostic properties of SLC2A3, we further investigated the function of SLC2A3 and found that it is closely related to immunity. We have also explored the relevance of other critical genes to immune cells. Our findings suggest that hypoxia-related genes play a crucial role in the diversity and complexity of the IS immune microenvironment. Exploring the association between hypoxia-related critical genes and immune cells provides innovative insights into the therapeutic targets for ischemic stroke.

Identification and Analysis of DNA Methylation Inflammation-Related Key Genes in Intracerebral Hemorrhage.

Inflammation and DNA methylation have been reported to play key roles in intracerebral hemorrhage (ICH). This study aimed to investigate new diagnostic biomarkers associated with inflammation and DNA methylation using a comprehensive bioinformatics approaches. GSE179759 and GSE125512 were collected from the Gene Expression Omnibus database, and 3222 inflammation-related genes (IFRGs) were downloaded from the Molecular Signatures Database. Key differentially expressed methylation-regulated and inflammation-related genes (DE-MIRGs) were identified by overlapping methylation-regulated differentially expressed genes (MeDEGs) between patients with ICH and control samples, module genes from weighted correlation network analysis, and IFRGs. Functional annotation of DE-MIRGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network was constructed to clarify the interrelationships between different DE-MIRGs. The key genes were categorized by least absolute shrinkage selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), and gene set enrichment analysis (GSEA). A total of 22 DE-MIRGs were acquired from 451 MeDEGs, 3222 IFRGs, and 302 module genes, and were mainly enriched in the GO terms of wound healing, blood coagulation, and hemostasis; and the KEGG pathways of PI3K/Akt signaling, focal adhesion, and regulation of actin cytoskeleton. A PPI network with 22 nodes and 87 edges was constructed based on the 22 DE-MIRGs, 11 of which were selected for key gene selection. Two 2 key genes (SELP and S100A4) were identified using LASSO and SVM-RFE. Finally, SELP was mainly enriched in cell morphogenesis involved in differentiation, cytoplasmic translation, and actin binding of GO terms, and the KEGG pathway including endocytosis, focal adhesion, and platelet activation. S100A4 was mainly enriched in GO terms including mitochondrial inner membrane; mitochondrial respirasome and lysosomal membrane; and the KEGG pathway of oxidative phosphorylation, regulation of actin cytoskeleton, and chemical carcinogenesis-reactive oxygen species. Twenty-two DE-MIRGs-associated inflammation and DNA methylation were identified between patients with ICH and normal controls, and two key genes (SELP and S100A4) were identified and regarded as biomarkers for ICH, which could provide the research foundation for further investigation of the pathological mechanism of ICH.

Construction of a Diagnostic Model and a lncRNA-Associated ceRNA Network Based on Apoptosis-Related Genes for Schizophrenia.

Gene expression profiles and apoptosis-related data were downloaded from the Gene Expression Omnibus and Molecular Signature databases, respectively. Apoptosis-related differentially expressed mRNAs (DEGs) and miRNAs (DEMs) from blood samples between the schizophrenia and healthy control individuals were screened. A diagnostic model was developed using the data from univariate and least absolute shrinkage and selection operator (LASSO) regression analyses, followed by validation using the GSE38485 dataset. Cases were divided into low-risk (LR) and high-risk (HR) groups based on the risk score of the model, and differences in immune gene sets and pathways between these two groups were compared. Finally, a ceRNA network was constructed by integrating long non-coding RNAs (lncRNAs), DEMs, and DEGs. A diagnostic model containing 15 apoptosis-related genes was developed and its diagnostic efficiency was found to be robust. The HR group was correlated with higher immune scores of chemokines, cytokines, and interleukins; it was also significantly involved in pathways such as pancreatic beta cells and early estrogen response. A ceRNA network composed of 2 lncRNAs, 14 miRNAs, and 5 mRNAs was established. The established model is a potential tool to improve the diagnostic efficiency of patients with schizophrenia, and the nodes included in the ceRNA network might serve as biomarkers and therapeutic targets for schizophrenia.

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

ObjectiveThe aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning.MethodThe pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets.ResultsNine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result.ConclusionsThe XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients.

Identification and analysis of cellular senescence-associated signatures in diabetic kidney disease by integrated bioinformatics analysis and machine learning.

Diabetic kidney disease (DKD) is a common complication of diabetes that is clinically characterized by progressive albuminuria due to glomerular destruction. The etiology of DKD is multifactorial, and numerous studies have demonstrated that cellular senescence plays a significant role in its pathogenesis, but the specific mechanism requires further investigation. This study utilized 5 datasets comprising 144 renal samples from the Gene Expression Omnibus (GEO) database. We obtained cellular senescence-related pathways from the Molecular Signatures Database and evaluated the activity of senescence pathways in DKD patients using the Gene Set Enrichment Analysis (GSEA) algorithm. Furthermore, we identified module genes related to cellular senescence pathways through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm and used machine learning algorithms to screen for hub genes related to senescence. Subsequently, we constructed a cellular senescence-related signature (SRS) risk score based on hub genes using the Least Absolute Shrinkage and Selection Operator (LASSO), and verified mRNA levels of hub genes by RT-PCR in vivo. Finally, we validated the relationship between the SRS risk score and kidney function, as well as their association with mitochondrial function and immune infiltration. The activity of cellular senescence-related pathways was found to be elevated among DKD patients. Based on 5 hub genes (LIMA1, ZFP36, FOS, IGFBP6, CKB), a cellular senescence-related signature (SRS) was constructed and validated as a risk factor for renal function decline in DKD patients. Notably, patients with high SRS risk scores exhibited extensive inhibition of mitochondrial pathways and upregulation of immune cell infiltration. Collectively, our findings demonstrated that cellular senescence is involved in the process of DKD, providing a novel strategy for treating DKD.

#6771 IDENTIFICATION OF KEY GENES IN DIABETIC NEPHROPATHY BASED ON LIPID METABOLISM

Abstract Background and Aims Diabetic nephropathy (DN), which is one of the most common systemic microvascular complications of diabetes mellitus, is extremely harmful to the patients’ health. There were some studies had shown that the disturbance of lipid metabolism was connected with the progression of DN. Therefore, the purpose of our study was to find the lipid metabolism-related hub genes in DN and provide a better reference for the diagnosis of DN. Method The Gene Expression Omnibus (GEO) database was used to download the gene expression profile data of DN and healthy samples (GSE142153), and we obtained the lipid metabolism-related genes from the Molecular Signatures Database (MSigDB). Differentially expressed genes (DEGs) between DN and healthy samples were analyzed and the weighted gene co-expression network analysis (WGCNA) was performed to examine the connection between genes and clinical traits and screen the key module genes in DN. Next, we utilized the Venn Diagram R package to identify the lipid metabolism-related genes in DN, and the Protein-Protein Interaction (PPI) of these genes was constructed. Then we carried out the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Moreover, the hub genes were identified using two machine learning algorithms, and the Gene Set Enrichment Analysis (GSEA) was used to analyze the functions of the hub genes. Furthermore, the immune infiltration discrepancies between DN and healthy samples and the correlation between the immune cells and hub genes were estimated. Finally, quantitative reverse transcription-PCR (qRT-PCR) experiment verified the expression of key genes. Results A total of 1445 DEGs were found in DN samples compared to healthy samples, and 694 DN-related genes in yellow and turquoise modules were identified by WGCNA. Next, we used the Venn Diagram R package to further identify 17 genes that were related to lipid metabolism and constructed a PPI network. Then GO analysis revealed that these 17 genes were significantly correlated with ‘phospholipid biosynthetic process’ and ‘cholesterol biosynthetic process’, while the KEGG analysis showed these lipid metabolism-related genes were enriched in ‘glycerophospholipid metabolism’ and ‘fatty acid degradation’. Moreover, SAMD8 and CYP51A1 were identified through the intersections of two machine learning algorithms. The results of GSEA analysis revealed that the ‘mitochondrial matrix’ and ‘GTPase activity’ were the significantly enriched GO terms in SAMD8 and CYP51A1, and the KEGG pathways of them were mainly concentrated in ‘pathways of neurodegeneration - multiple diseases’. Immune infiltration analysis suggested that there were 9 immune cells expressed differently in DN and healthy samples, and both SAMD8 and CYP51A1 were significantly correlated with activated B cell and effector memory CD8 T cell. Finally, qRT-PCR confirmed the expression of SAMD8 and CYP51A1 in DN was high. Conclusion In summary, the lipid metabolism-related genes SAMD8 and CYP51A1 may play key roles in DN.

Identification of PANoptosis-Based Prognostic Signature for Predicting Efficacy of Immunotherapy and Chemotherapy in Hepatocellular Carcinoma.

PANoptosis has been a research hotspot, but the role of PANoptosis in hepatocellular carcinoma (HCC) remains widely unknown. Drug resistance and low response rate are the main limitations of chemotherapy and immunotherapy in HCC. Thus, construction of a prognostic signature to predict prognosis and recognize ideal patients for corresponding chemotherapy and immunotherapy is necessary. The mRNA expression data of HCC patients was collected from TCGA database. Through LASSO and Cox regression, we developed a prognostic signature based on PANoptosis-related genes. KM analysis and ROC curve were implemented to evaluate the prognostic efficacy of this signature, and ICGC and GEO database were used as external validation cohorts. The immune cell infiltration, immune status, and IC50 of chemotherapeutic drugs were compared among different risk subgroups. The relationships between the signature and the efficacy of ICI therapy, sorafenib treatment, and transcatheter arterial chemoembolization (TACE) therapy were investigated. A 3-gene prognostic signature was constructed which divided the patients into low- and high-risk subgroups. Low-risk patients had better prognosis, and the risk score was proved to be an independent predictor of overall survival (OS), which had a well predictive effect. Patients in high-risk population had more immunosuppressive cells (Tregs, M0 macrophages, and MDSCs), higher TIDE score and TP53 mutation rate, and elevated activity of base excision repair (BER) pathways. Patients with low risk benefited more from ICI, TACE, and sorafenib therapy. The predictive value of the risk score was comparable with TIDE and MSI for OS under ICI therapy. The risk score could be a biomarker to predict the response to ICI, TACE, and sorafenib therapy. The novel signature based on PANoptosis is a promising biomarker to distinguish the prognosis predict the benefit of ICI, TACE, and sorafenib therapy, and forecast the response to them.

The mitotic spindle-related seven-gene predicts the prognosis and immune microenvironment of lung adenocarcinoma

PurposeAbnormalities in the mitotic spindle have been linked to a variety of cancers. Data on their role in the onset, progression, and treatment of lung adenocarcinoma (LUAD) need to be explored.MethodsThe data were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Signatures Database (MSigDB), for the training cohort, external validation cohort, and the hallmark mitotic spindle gene set, respectively. Mitotic spindle genes linked to LUAD prognosis were identified and intersected with differentially expressed up-regulated genes in the training cohort. Nomogram prediction models were built based on least absolute shrinkage and selection operator (LASSO) regression, univariate cox, and multivariate cox analyses. The seven-gene immunological score was examined, as well as the correlation of immune checkpoints. The DLGAP5 and KIF15 expression in BEAS-2B, A549, H1299, H1975, and PC-9 cell lines was validated with western blot (WB).ResultsA total of 965 differentially expressed up-regulated genes in the training cohort intersected with 51 mitotic spindle genes associated with LUAD prognosis. Finally, the seven-gene risk score was determined and integrated with clinical characteristics to construct the nomogram model. Immune cell correlation analysis revealed a negative correlation between seven-gene expression with B cell, endothelial cell (excluding LMNB1), and T cell CD8 + (p < 0.05). However, the seven-gene expression was positively correlated with multiple immune checkpoints (p < 0.05). The expression of DLGAP5 and KIF15 were significantly higher in A549, H1299, H1975, and PC-9 cell lines than that in BEAS-2B cell line.ConclusionHigh expression of the seven genes is positively correlated with poor prognosis of LUAD, and these genes are promising as prospective immunotherapy targets.

RNA sequencing to characterize pathways in EGFR-mutated non-small cell lung cancer.

e21019 Background: The purpose of our study is to explore patterns in gene expression for patients with EGFR-mutated non-small cell lung cancer (NSCLC). Methods: 287 patients with EGFR-mutated lung cancer were identified using the Tempus Lens database. Raw RNA sequence expression data was requested from TEMPUS XT RNA analysis. Sequences were processed through the EdgeR analysis in the R programming environment. Differential abundance analysis was done to further filter out genes and Benjamini-Hochberg correction selected genes with a corrected p-value of &lt; 0.05 for further pathway analysis. Pathway analysis was performed using GSEA (Gene Set Enrichment Analysis). Enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GSEA Molecular Signatures Database (MsigDB) was performed for overexpressed and under-expressed genes in patients. Results: Pathway analysis run against the KEGG reference database and GSEA MSigDB helped to illustrate genes shared in activated and suppressed pathways in our patient cohort. For example, using the KEGG reference database, enriched pathways meeting p value significance threshold of &lt; 0.005 included the nicotine addiction, olfactory transduction, and cortisol synthesis and secretion pathways, amongst others. Suppressed pathways using the KEGG reference database meeting p value significance threshold of &lt; 0.005 included fat digestion and absorption, vitamin digestion and absorption, PPAR signaling pathway, and drug metabolism by cytochrome P450 and other enzymes. Using the GSEA MSigDB, enriched pathways included G protein-coupled receptor activity, sensory perception, synaptic signaling and trans-synaptic signaling, meanwhile suppressed pathways included cytolysis by host of symbiont cells, negative regulation of very-low-density lipoprotein particle remodeling, and oxidoreductase activity. Conclusions: Patterns in gene and pathway expression were observed in our cohort of patients with EGFR-mutated NSCLC. Analyzing RNA sequencing pathways showed differential up and down regulation of multiple pathways. For example, suppression of the fat and vitamin digestion and absorption pathways may shed light on processes involved in cancer cachexia and metabolism. Though further work needs to be done, RNA sequencing results for patients with EGFR-mutated lung cancer may lead to information on cancer development and additional therapeutic targets.

DIANA-miRPath v4.0: expanding target-based miRNA functional analysis in cell-type and tissue contexts.

DIANA-miRPath is an online miRNA analysis platform harnessing predicted or experimentally supported miRNA interactions towards the exploration of combined miRNA effects. In its latest version (v4.0, http://www.microrna.gr/miRPathv4), DIANA-miRPath breaks new ground by introducing the capacity to tailor its target-based miRNA functional analysis engine to specific biological and/or experimental contexts. Via a redesigned modular interface with rich interaction, annotation and parameterization options, users can now perform enrichment analysis on Gene Ontology (GO) terms, KEGG and REACTOME pathways, sets from Molecular Signatures Database (MSigDB) and PFAM. Included miRNA interaction sets are derived from state-of-the-art resources of experimentally supported (DIANA-TarBase v8.0, miRTarBase and microCLIP cell-type-specific interactions) or from in silico miRNA-target interactions (updated DIANA-microT-CDS and TargetScan predictions). Bulk and single-cell expression datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx) and adult/fetal single-cell atlases are integrated and can be used to assess the expression of enriched term components across a wide range of states. A discrete module enabling enrichment analyses using CRISPR knock-out screen datasets enables the detection of selected miRNAs with potentially crucial roles within conditions under study. Notably, the option to upload custom interaction, term, expressionand screen sets further expands the versatility of miRPath webserver.

Lactate metabolism-related genes to predict the clinical outcome and molecular characteristics of endometrial cancer

BackgroundMetabolic reprogramming is one of hallmarks of cancer progression and is of great importance for the tumor microenvironment (TME). As an abundant metabolite, lactate has been found to play a critical role in cancer development and immunosuppression of TME. However, the potential role of lactate metabolism-related genes in endometrial cancer (EC) remains obscure.MethodsRNA sequencing data and clinical information of EC were obtained from The Cancer Genome Atlas (TCGA) database. Lactate metabolism-related genes (LMRGs) WERE from Molecular Signature Database v7.4 and then compared the candidate genes from TCGA to obtain final genes. Univariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were performed to screen prognostic genes. A lactate metabolism-related risk profile was constructed using multivariate Cox regression analysis. The signature was validated by time-dependent ROC curve analysis and Kaplan-Meier analysis. The relationship between the risk score and age, grade, stage, tumor microenvironmental characteristics, and drug sensitivity was as well explored by correlation analyses. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional analysis between the high and low-risk groups were performed. CCK8, EdU, and clone formation assays were applied to detect the proliferation ability of EC cells, Transwell assay was performed to detect the migration ability of EC cells, and intracellular lactate and glucose content was used to asses lactate metabolism.ResultsWe constructed a risk signature based on 18 LMRGs. Kaplan-Meier curves confirmed that the high-risk group had poorer prognosis compared to the low-risk group. A nomogram was then constructed to predict the probability of EC survival. We also performed GO enrichment analysis and KEGG pathway functional analysis between the high and low-risk groups, and the outcome revealed that the features were significantly associated with energy metabolism. There was a significant correspondence between LMRGs and tumor mutational load, checkpoints and immune cell infiltration. C1, C2, and C4 were the most infiltrated in the high-risk group. The high-risk group showed increased dendritic cell activation, while the low-risk group showed increased plasma cells and Treg cells. Drug sensitivity analysis showed LMRGs risk was more resistant to Scr kinase inhibitors. We further proved that one of the lactate metabolism related genes, TIMM50 could promote EC cell proliferation, migration and lactate metabolism.ConclusionIn conclusion, we have established an effective prognostic signature based on LMRG expression patterns, which may greatly facilitate the assessment of prognosis, molecular features and treatment modalities in EC patients and may be useful in the future translation to clinical applications. TIMM50 was identified as a novel molecule that mediates lactate metabolism in vitro and in vivo, maybe a promising target for EC prognosis.