ssGSEA Analysis Research Articles

A Novel Defined RAS-Related Gene Signature for Predicting the Prognosis and Characterization of Biological Function in Osteosarcoma.

Background Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents with a high incidence and poor prognosis. Activation of the RAS pathway promotes progression and metastasis of osteosarcoma. RAS has been studied in many different tumors; however, the prognostic value of RAS-associated genes in OS remains unclear. On this basis, we investigated the RAS-related gene signature and explored the intrinsic biological features of OS. Methods We obtained RNA transcriptome sequencing data and clinical information of osteosarcoma patients from the TARGET database. RAS pathway-related genes were obtained from the KEGG pathway database. Molecular subgroups and risk models were developed using consensus clustering and least absolute shrinkage and selection operator (LASSO) regression, respectively. ESTIMATE algorithm and ssGSEA analysis were used to assess the tumor microenvironment and immune penetrance between the two groups. A comprehensive review of gene ontology (GO) and KEGG analyses revealed inherent biological functional differences between the two groups. Results The consistent clustering showed stratification of osteosarcoma patients into two subtypes based on RAS-associated genes and provided a robust prediction of prognosis. A risk model further confirmed that RAS-related genes are the best prognostic indicators for OS patients. GO analysis showed that GDP/GTP binding, focal adhesion, cytoskeletal motor activity, and cell-matrix junctions were associated with the RAS-related model group. Furthermore, RAS signaling in osteosarcoma based on KEGG analysis was significantly associated with cancer progression, with immune function and tumor microenvironment particularly affected. Conclusion We constructed a prognostic model founded on RAS-related gene and demonstrated its predictive ability. Then, furtherly exploration of the molecular mechanisms and immune characteristics proved the role of RAS-related gene in the dysregulation in OS.

Development and validation of a novel fibroblast scoring model for lung adenocarcinoma.

The interaction between cancer-associated fibroblasts (CAFs) and the tumor microenvironment (TME) is a key factor for promoting tumor progression. In lung cancer, the crosstalk between CAFs and malignant and immune cells is expected to provide new directions for the development of immunotherapy. In this study, we have systematically analyzed a single-cell dataset and identified interacting genes between CAFs and other cells. Subsequently, a robust fibroblast-related score (FRS) was developed. Kaplan-Meier (KM) and ROC analyses showed its good predictive power for patient prognoses in the training set comprising of specimens from the cancer genome atlas (TCGA) and in three external validation sets from the Gene Expression Omnibus (GEO). Univariate and multivariate Cox regression analyses suggested that FRS was a significant prognostic factor independent of multiple clinical characteristics. Functional enrichment and ssGSEA analyses indicated that patients with a high FRS developed “cold” tumors with active tumor proliferation and immunosuppression capacities. In contrast, those with a low FRS developed “hot” tumors with active immune function and cell killing abilities. Genomic variation analysis showed that the patients with a high FRS possessed a higher somatic mutation burden and copy number alterations and were more sensitive to chemotherapy; patients with a low FRS were more sensitive to immunotherapy, particularly anti-PD1 therapy. Overall, these findings advance the understanding of CAFs in tumor progression and we generated a reliable FRS-based model to assess patient prognoses and guide clinical decision-making.

Bioinformatics analysis and experimental verification of the prognostic and biological significance mediated by fatty acid metabolism related genes for hepatocellular carcinoma.

BackgroundLiver cancer is among the leading causes of death related to cancer around the world. The most frequent type of human liver cancer is hepatocellular carcinoma (HCC). Fatty acid (FA) metabolism is an emerging hallmark that plays a promoting role in numerous malignancies. This study aimed to discover a FA metabolism-related risk signature and formulate a better model for HCC patients’ prognosis prediction.MethodsWe collected mRNA expression data and clinical parameters of patients with HCC using the TCGA databases, and the differential FA metabolism-related genes were explored. To create a risk prognostic model, we carried out the consensus clustering as well as univariate and multivariate Cox regression analyses. 16 genes were used to establish a prognostic model, which was then validated in the ICGC dataset. The accuracy of the model was performed using receiver operating characteristic (ROC) analyses, decision curve analysis (DCA) and nomogram. The immune cell infiltration level of risk genes was evaluated with single-sample GSEA (ssGSEA) algorithm. To reflect the response to immunotherapy, immunophenoscore (IPS) was obtained from TCGA-LIHC. Then, the expression of the candidate risk genes (p < 0.05) was validated by qRT-PCR, Western blotting and single-cell transcriptomics. Cellular function assays were performed to revealed the biological function of HAVCR1.ResultsAccording to the TCGA-LIHC cohort analysis, the majority of the FA metabolism-related genes were expressed differentially in the HCC and normal tissues. The prognosis of patients with high-risk scores was observed to be worse. Multivariate COX regression analysis confirmed that the model can be employed as an independent prognosis factor for HCC patients. Furthermore, ssGSEA analysis revealed a link between the model and the levels of immune cell infiltration. Our model scoring mechanism also provides a high predictive value in HCC patients receiving anti-PDL1 immunotherapy. One of the FA metabolism-related genes, HAVCR1, displays a significant differential expression between normal and HCC cell lines. Hepatocellular carcinoma cells (Huh7, and HepG2) proliferation, motility, and invasion were all remarkably inhibited by HAVCR1 siRNA.ConclusionOur study identified a novel FA metabolism-related prognostic model, revealing a better potential treatment and prevention strategy for HCC.

Ferroptosis-Related lncRNA Signature Correlates with the Prognosis, Tumor Microenvironment, and Therapeutic Sensitivity of Esophageal Squamous Cell Carcinoma.

Esophageal squamous cell carcinoma (ESCC) is the most prevalent form of esophageal cancer in China and is closely associated with malignant biological characteristics and poor survival. Ferroptosis is a newly discovered iron-dependent mode of cell death that plays an important role in the biological behavior of ESCC cells. The clinical significance of ferroptosis-related long noncoding RNAs (FRLs) in ESCC remains unknown and warrants further research. The current study obtained RNA sequencing profiles and corresponding clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and FRLs were obtained through coexpression analysis. Consensus clustering was employed to divide the subjects into clusters, and immune-associated pathways were identified by functional analysis. The current study observed significant differences in the enrichment scores of immune cells among different clusters. Patients from TCGA-ESCC database were designated as the training cohort. A ten-FRL prediction signature was established using the least absolute shrinkage and selection operator Cox regression model and validated using the GEO cohort and our own independent validation database. Real-time quantitative polymerase chain reaction was used to verify the expression of the ten FRLs, and the ssGSEA analysis was employed to evaluate their function. In addition, the IMvigor database was used to assess the predictive value of the signature in terms of immunotherapeutic responses. Multivariate Cox and stratification analyses revealed that the ten-FRL signature was an independent predictor of the overall survival (OS). Patients with ESCC in the high-risk group displayed worse survival, a characteristic tumor immune microenvironment, and low immunotherapeutic benefits compared to those in the low-risk group. Collectively, the risk model established in this study could serve as a promising predictor of prognosis and immunotherapeutic response in patients with ESCC.

Spatial transcriptomics atlas reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment components in colorectal cancer

BackgroundThe tumor-promoting role of tumor microenvironment (TME) in colorectal cancer has been widely investigated in cancer biology. Cancer-associated fibroblasts (CAFs), as the main stromal component in TME, play an important role in promoting tumor progression and metastasis. Hence, we explored the crosstalk between CAFs and microenvironment in the pathogenesis of colorectal cancer in order to provide basis for precision therapy.MethodsWe integrated spatial transcriptomics (ST) and bulk-RNA sequencing datasets to explore the functions of CAFs in the microenvironment of CRC. In detail, single sample gene set enrichment analysis (ssGSEA), gene set variation analysis (GSVA), pseudotime analysis and cell proportion analysis were utilized to identify the cell types and functions of each cell cluster. Immunofluorescence and immunohistochemistry were applied to confirm the results based on bioinformatics analysis.ResultsWe profiled the tumor heterogeneity landscape and identified two distinct types of CAFs, which myo-cancer-associated fibroblasts (mCAFs) is associated with myofibroblast-like cells and inflammatory-cancer-associated fibroblasts (iCAFs) is related to immune inflammation. When we carried out functional analysis of two types of CAFs, we uncovered an extensive crosstalk between iCAFs and stromal components in TME to promote tumor progression and metastasis. Noticeable, some anti-tumor immune cells such as NK cells, monocytes were significantly reduced in iCAFs-enriched cluster. Then, ssGSEA analysis results showed that iCAFs were related to EMT, lipid metabolism and bile acid metabolism etc. Besides, when we explored the relationship of chemotherapy and microenvironment, we detected that iCAFs influenced immunosuppressive cells and lipid metabolism reprogramming in patient who underwent chemotherapy. Additionally, we identified the clinical role of iCAFs through a public database and confirmed it were related to poor prognosis.ConclusionsIn summary, we identified two types of CAFs using integrated data and explored their functional significance in TME. This in-depth understanding of CAFs in microenvironment may help us to elucidate its cancer-promoting functions and offer hints for therapeutic studies.

Differential OAT methylation correlates with cell infiltration in tumor microenvironment and overall survival postradiotherapy in oral squamous cell carcinoma patient.

Given that DNA methylation and tumor microenvironment (TME) are susceptible to radiotherapy, we aimed to figure out specific differential DNA methylation to reflect oral squamous cell carcinoma (OSCC) prognosis and associated effect on TME changes postradiotherapy, performing as an efficient biomarker. Differentially methylation analysis was performed using data from The Cancer Genome Atlas. Curves of Kaplan-Meier (K-M) survival, cumulative hazard and events, Cox proportional hazards, and linear regression model were conducted to screen and validate differential methylation genes, while multiple regression equation to analyze if ornithine aminotransferase (OAT) methylation correlates with radiotherapy. For correlation between OAT methylation and immune infiltrates, CIBERSORT and ESTIMATE algorithms were performed, following gene set enrichment analysis (GSEA) and ssGSEA analysis to evaluate biological process. Compared to normal tissues, only OAT in OSCC was differential significantly by K-M analysis (p=0.0364). OAT hypermethylation was associated with increased overall survival (hazard ratio: 0.65, p=0.0358). Radiotherapy correlated with OAT methylation (β=-0.01, p=0.0061); most patients with OAT hypermethylation were radiation-sensitive. Hypomethylated OAT correlated with higher cell infiltrations in TME. Neuroactive ligand-receptor interaction was most significantly related to OAT methylation (p=9.2e-10). Sulfur metabolism was the most significantly in OAT hypermethylation group (p=0.0041) and RIG-I-like receptor in OAT hypomethylation group (p=0.0094). OAT methylation can serve as a predictor of OSCC prognosis postradiotherapy with potential mechanism by changing cell infiltrations in TME, but further experimental study deserves to carry out confirming the role and mechanism of OAT methylation in OSCC.

Prediction of Prognosis in Patients with Hepatocellular Carcinoma Based on Molecular Subtypes of Immune Genes.

For those patients with hepatocellular carcinoma (HCC), it is really a heavy burden. Herein, the immune genes of HCC were analyzed in groups to determine prognostic biomarkers related to immune genes in HCC. The mRNA data, clinical data in TCGA-LIHC dataset, and immune gene in the ImmPort database were collected for the combining usage with K-means concordance clustering to cluster HCC patients according to the immune gene matrix. Based on ssGSEA analysis result, HCC patients were sorted into high- and low-immune subtypes, and survival curve presented that patients in high-immune subtypes had a better prognosis. Subsequently, differential expression analysis was performed to obtain immune-related differentially expressed genes (IRGs). Cox and lasso analyses were performed for obtaining five optimal immune genes related to prognosis, and a risk assessment model was then established. Patient samples in the training and validation sets were, respectively, divided into high- and low-risk groups. K-M survival curves presented a better prognosis of patients in the low-risk group than in the high-risk group. The ROC curve indicated that this model was finely used for the prediction of prognosis. In addition, immune infiltration assessment revealed that NR0B1 and FGF9 had potential to impact the tumor immune microenvironment. Finally, using qRT-PCR and transwell assays, it was demonstrated that the macrophage chemotaxis was enhanced when NR0B1 and FGF9 were highly expressed in HCC cells. In general, a 5-gene prognostic risk assessment model was constructed based on immune genes and bioinformatics analysis methods, which provides some reference for the prognosis of HCC as well as immunotherapy.

Abstract 5002: The prognostic relevance and underlying mechanisms of the novel oxygen sensor ADO in cancers

Abstract Purpose: Hypoxia is widely recognized as a determiner in cancer. Cysteamine dioxygenase (ADO) is demonstrated to be a novel oxygen sensor, globally expressed in animals and plants. However, the functions of ADO in human cancers remain unclear. This study was to identify the survival association of ADO in cancers and to excavate its underlying mechanisms. Methods: Transcriptional expression profiles, clinical characteristics, single nucleotide variation (SNV) data, and 450K methylation data were downloaded from all cancer types in the cancer genome atlas (TCGA). Two independent liver cancer datasets GSE14520 and GSE36376 were downloaded for further validation. Results: The expression of ADO gene was differentially expressed in various cancers. High expression of ADO indicated a worse OS and PFS in adrenocortical carcinoma (ACC), acute myeloid leukemia, liver hepatocellular carcinoma (LIHC), while predicted better OS and PFS in brain lower-grade glioma (LGG), and kidney renal clear cell carcinoma (KIRC). Across various cancers, ADO was positively correlated with tumor-associated pathways including G2/M_checkpoint, MYC_targets, and TGFB while negatively correlated with Genes_upregulated_by_reactive_oxygen_species (ROS) through ssGSEA analysis. The expression of ADO gene was highly correlated with hypoxic marker HIF1A, HIF2A, KDM5A, and KDM6A in almost all cancer types while poorly correlated with Hallmark_hypoxia geneset and Regulation_of_cellular response_to_hypoxia geneset, which indicated that ADO might work independently with HIF1A-mediated response. ADO was positively correlated with the methylation of most chromatin regulators in LGG and LIHC. ADO expression positively correlated with most immune checkpoint molecules in most cancers. Notably, a highly positive correlation between ADO and CD276, ENTPD1, or HMGB1 was observed in most cancer types. In liver cancer, ADO was positively correlated with the infiltration of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, myeloid dendritic cells. However, ADO expression was not strongly relevant to TMB in liver cancer. The increased expression, poor survival indication and immune checkpoint correlation of ADO in liver cancer were further validated by GSE14520 and GSE36376 datasets. Conclusion: ADO was differentially expressed in various cancers and significantly associated with survival in some cancer types including liver cancer. The underlying mechanism might be associated with the G2/M checkpoint, MYC targets and TGFB pathways, and ADO-mediated HIF1A-independent hypoxic response. The immune cell infiltrates and immune checkpoints association hints that ADO might be a biomarker for immunotherapy in liver cancer. Citation Format: Jie Huang, Juan Shen, Yao Qiu, Jiannan Qian, Jiawei Li, Xueqin Chen, Shenglin Ma. The prognostic relevance and underlying mechanisms of the novel oxygen sensor ADO in cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5002.

Abstract 1059: Paricalcitol, hydroxychloroquine and gemcitabine promote antitumor effects and modulate immune profile in pancreatic ductal adenocarcinoma

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) has low (&amp;lt;5%) five-year survival. We hypothesized combined Vitamin D3 (Paricalcitol; Par) and hydroxychloroquine (HCQ) impacts stroma, anti-tumor immunity, and autophagy, thereby sensitizing PDAC to gemcitabine (Gem). Methods For subcutaneous efficacy, 5-6-week-old female nude mice (n=5/group) were injected with KPC-Luc cells and randomized to: 1) vehicle (PBS), 2) Gem plus HCQ, 3) Gem plus Par, and 4) Gem, HCQ, and Par once tumors were palpable. Gem and Par (twice weekly) were administered via intraperitoneal injection. HCQ was given orally (daily). For orthotropic efficacy and single cell analysis, female C57BL/6J mice (n=6/group) were injected with 2×105 KPC-Luc cells into the pancreas tail. On day 5, animals were randomized to 1) PBS and 2) combined Gem/HCQ/Par as above. Tumors were analyzed via bioluminescent imaging. After 15 days, animals were euthanized, tumors were weighed and dissociated into single cells. High viability samples were used to generate scRNA-Seq libraries using 10X genomics droplet approach. Results Gem, HCQ, and Par significantly (p&amp;lt;0.001) reduced tumor growth/weight versus controls in both models. Single cell transcriptome profiles of 11,188 cells (controls) and 7,624 cells (Gem/HCQ/Par) were obtained. Split UMAP plots of samples were generated with cell clusters annotated from established gene markers. Eight clusters expressing PDAC-associated genes were designated as PDAC cells. The tumor microenvironment (TME) showed dramatic post-treatment changes with significant decreases in PDAC cells (p=0.007) and increases in B cells, monocytes/macrophages (p=0.0006, 0.03 respectively), and increased T/NK cells in treated versus control mice. Supervised analysis among T cells showed more activation (Cd44, Tnfrsf4/9) and exhaustion (Ctla4, Lag3, Pdcd1) associated gene expression post-treatment, indicating robust T cell response. ssGSEA analysis showed significant (p&amp;lt;.001) activation of Th1/Th2/Th17 pathways. Monocyte/macrophages clusters analysis revealed greater expression of M1 genes and M2 genes in treated and control samples, respectively. Modified EMT was observed in stromal cells with enhanced MHC1 expression. Conclusion HCQ and Par enhance the efficacy of Gem in two mice PDAC models. Single cell transcriptome analyses revealed the combination treatment promoted immune shifts in the PDAC TME. Effects of Par and HCQ and standard chemotherapy drugs, Gem and nab-paclitaxel are being studied in an ongoing clinical trial (NCT04524702). Citation Format: Purnachandra Nagaraju Ganji, Beena Elizabeth Thomas, Maggie Phillips, Cameron Herting, Amanda Ruggieri, Jessica Millian Keilson, Michael Kim Turgeon, Sisira Saraswatula, William Pilcher, Gregory B. Lesinski, Manoj Bhasin, Bassel F. El-Rayes. Paricalcitol, hydroxychloroquine and gemcitabine promote antitumor effects and modulate immune profile in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1059.

CYP26A1 Is a Novel Cancer Biomarker of Pancreatic Carcinoma: Evidence from Integration Analysis and In Vitro Experiments.

Background CYP26A1 has been reported in multiple cancers. However, the role of CYP26A1 in pancreatic cancer (PC) has not been explored. Method The public data used for this study was obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Cancer Cell Line Encyclopedia (CCLE) cell lines. CCK8, colony formation, and EdU assay were used to assess the proliferation ability of cancer cells. Transwell and wound healing assays were used to evaluate the invasion and migration ability of cancer cells. qRT-PCR and western blot assays were used to analyze the RNA and protein level of genes. Survival package was used for prognosis analysis. Gene Set Enrichment Analysis (GSEA) was used to identify biological pathway differences between two groups. ssGSEA analysis was used to quantify the immune microenvironment in PC tissue. GDSC and TIDE analyses were used for sensitivity analysis of chemotherapy and immunotherapy. Results Our results showed that CYP26A1 was overexpressed in PC tissue and cell lines. Meanwhile, metastatic PC cell lines tend to have a higher CYP26A1 level compared with the primary PC cell lines based on CCLE data. Moreover, CYP26A1 was associated with worse clinical features. Also, we found that CYP26A1 had a satisfactory efficiency in predicting overall survival, disease-specific survival, and progression-free interval of PC patients, independent of other clinical features. In vitro experiments indicated that CYP26A1 could significantly facilitate the proliferation, invasion, and migration ability of PC cells. GSEA showed that the pathways of angiogenesis, E2F target, MYC target, mTORC signaling, G2M checkpoint, and epithelial-mesenchymal transition were activated in high CYP26A1 patients. Immune infiltration analysis showed that CYP26A1 was positively correlated with macrophages, Th1 cells, and Treg cells, but negatively correlated with Th17 cells. TIDE analysis showed that non_responder patients had a higher CYP26A1 level compared with predicted responder patients of immunotherapy. Drug sensitivity analysis and assay showed that CYP26A1 could increase the chemotherapy sensitivity of gemcitabine. Conclusions In summary, CYP26A1 promotes PC progression and is a novel biomarker of PC, with potential for clinical application.

Characterization of MET exon 14 skipping alterations (METex14) in non–small cell lung cancer (NSCLC) using whole transcriptome sequencing (WTS).

9122 Background: Multiple DNA alterations in exon 14 splice sites have been identified in NSCLC and result in skipping of the juxtamembrane domain Cbl-E3 ubiquitin ligase binding region, leading to increased MET stability and oncogenesis. The effects of these alterations on transcriptome-level have not been fully characterized. We present the largest cohort study of METex14 using WTS and identify key cellular pathways associated with invasion and metastases in METex14. Methods: 17,666 NSCLC tumor samples underwent genomic profiling at Caris Life Sciences. Analyses included next generation sequencing of DNA (592 Gene Panel, NextSeq, whole exome sequencing, NovaSeq) and RNA (NovaSeq, WTS). METex14 was captured via WTS. ssGSEA analysis was used to evaluate pathway enrichment. Wilcoxon, Fisher’s exact were used for statistical significance (p without and q values with multiple comparison correction). Results: 440 patients (2.5%) with METex14 were identified. METex14 patients were of older age, female gender, and enriched in sarcomatoid histology (Table 1). The most common alterations were point mutations (51.5%) and deletions (17.3%) at donor splice sites. Splice site alterations except point mutations at splice acceptor site translated to increased mRNA expression compared to wild-type MET (WT). MET amplification translated to higher mRNA expression compared to METex14 and WT with synergistic expression when co-altered with METex14 (q&lt;0.05). The most common co-alterations were amplifications of MDM2 (18.5% vs. 1.8% WT), HMGA2 (13.7% vs 0.9% WT), and CDK4 (10.4% vs 1.4% WT) (q &lt; 0.05). METex14 were mutually exclusive to mutations in KRAS and EGFR. High PD-L1 (22c3) &gt; 50% (53% vs. 27.6% WT, q&lt;0.001) and lower TMB (4 mut/Mb vs. 7 mut/Mb WT, p&lt;0.001) were observed with METex14 and pathways associated with skipping variants included IFNγ signaling, angiogenesis, and apical junction pathways on univariate analysis (q&lt;0.05). Conclusions: We present the largest WTS analysis of METex14. Splicing alterations and MET co-amplification translated to higher and synergistic MET expression at transcriptome level, respectively. Association with upregulated angiogenic and apical junction pathways support preclinical observation of vascular and cytoskeletal remodeling as potential mechanisms of invasion and metastases in MET ex14 NSCLC.[Table: see text]

ZNF384: A Potential Therapeutic Target for Psoriasis and Alzheimer's Disease Through Inflammation and Metabolism.

BackgroundPsoriasis is an immune-related skin disease notable for its chronic inflammation of the entire system. Alzheimer’s disease (AD) is more prevalent in psoriasis than in the general population. Immune-mediated pathophysiologic processes may link these two diseases, but the mechanism is still unclear. This article aimed to explore potential molecular mechanisms in psoriasis and AD.MethodsGene expression profiling data of psoriasis and AD were acquired in the Gene Expression Omnibus (GEO) database. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were first applied in two datasets. Differentially expressed genes (DEGs) of two diseases were identified, and common DEGs were selected. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to explore common biological pathways. Signature transcription factors (STFs) were identified and their diagnostic values was calculated by receiver operating characteristic (ROC) curve analysis in the exploration cohort and verified in the validation cohort. The expression levels of STFs were further investigated in the validation cohort and the GTEx Portal Database. Additionally, four kinds of interaction analysis were performed: correlation analysis among STFs, gene-gene, chemical-protein, and protein-ligand interaction analyses. In the end, we predicted the transcription factor that potentially regulates STFs.ResultsBiosynthesis and metabolic pathways were enriched in GSEA analysis. In ssGSEA analysis, most immunoreaction gene lists exhibited differential enrichment in psoriasis cases, whereas three receptor-related gene lists did in AD. The KEGG analysis of common DEGs redetermined inflammatory and metabolic pathways essential in both diseases. 5 STFs (PPARG, ZFPM2, ZNF415, HLX, and ANHX) were screened from common DEGs. The ROC analysis indicated that all STFs have diagnostic values in two diseases, especially ZFPM2. The correlation analysis, gene-gene, chemical-protein, and protein-ligand interaction analyses suggested that STFs interplay and involve inflammation and aberrant metabolism. Eventually, ZNF384 was the predicted transcription factor regulating PPARG, ZNF415, HLX, and ANHX.ConclusionsThe STFs (PPARG, ZFPM2, ZNF415, HLX, and ANHX) may increase the morbidity rate of AD in psoriasis by initiating a positive feedback loop of excessive inflammation and metabolic disorders. ZNF384 is a potential therapeutic target for psoriasis and AD by regulating PPARG, ZNF415, HLX, and ANHX.

Development and validation of apoptosis-related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients.

BackgroundPrevious evidence has shown that apoptosis performs integral functions in the tumorigenesis and development of various tumors. Therefore, this study aimed to establish a molecular subtype and prognostic signature based on apoptosis‐related genes (ARGs) to understand the molecular mechanisms and predict prognosis in patients with osteosarcoma.MethodsThe GEO and TARGET databases were utilized to obtain the expression levels of ARGs and clinical information of osteosarcoma patients. Consensus clustering analysis was used to explore the different molecular subtypes based on ARGs. GO, KEGG, GSEA, ESTIMATE, and ssGSEA analyses were performed to examine the differences in biological functions and immune characteristics between the distinct molecular subtypes. Then, we constructed an ARG signature by LASSO analysis. The prognostic significance of the ARG signature in osteosarcoma was determined by Kaplan–Meier plotter, Cox regression, and nomogram analyses.ResultsTwo apoptosis‐related subtypes were identified. Cluster 1 had a better prognosis, higher immunogenicity, and immune cell infiltration, as well as a better response to immunotherapy than Cluster 2. We discovered that patients in the high‐risk cohort had a lower survival rate than those in the low‐risk cohort according to the ARG signature. Furthermore, Cox regression analysis confirmed that a high risk score independently acted as an unfavorable prognostic marker. Additionally, the nomogram combining risk scores with clinical characteristics can improve prediction efficiency.ConclusionWe demonstrated that patients suffering from osteosarcoma may be classified into two apoptosis‐related subtypes. Moreover, we developed an ARG prognostic signature to predict the prognosis status of osteosarcoma patients.

Identification of the Pyroptosis-Related Prognosis Gene Signature and Immune Infiltration in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignancies worldwide with a dismal prognosis. Lack of efficient biomarkers, early detection, and prognosis is still a challenge for HCC. Pyroptosis is a new discovery inflammatory form of programmed cell death. There is growing evidence revealed that pyroptosis plays a role in physiological and pathological conditions of human cancers. However, the prognostic evaluation of these pyroptosis-related genes (PRGs) in HCC remains blank. Consensus clustering of PRGs was used to classify 374 patients with HCC from the TCGA-LIHC cohort. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 2-gene prognostic gene model (PLCG1 and GSDMC) was built and indicated the survival rate in HCC with medium-to-high accuracy. Then, the median risk score from the TCGA cohort was utilized; the prognostic gene model was also accurate in Gene Expression Omnibus (GEO) cohort. The functional enrichment analysis indicated that the oncogenic properties are associated with prominent hallmarks of cancer. The ssGSEA analyses and TIMER database indicated that immune infiltration tumor microenvironment in the HCC. In conclusion, our findings provide a foundation for further research targeting PRGs and their immune microenvironment.

The Diagnostic and Predictive Significance of Immune-Related Genes and Immune Characteristics in the Occurrence and Progression of IgA Nephropathy.

Objective To investigate the potential diagnostic and predictive significance of immune-related genes in IgA nephropathy (IgAN) and discover the abnormal glomerular inflammation in IgAN. Methods GSE116626 was used as a training set to identify different immune-related genes (DIRGs) and establish machine learning models for the diagnosis of IgAN; then, a nomogram model was generated based on GSE116626, and GSE115857 was used as a test set to evaluate its clinical value. Short Time-Series Expression Miner (STEM) analysis was also performed to explore the changing trend of DIRGs with the progression of IgAN lesions. GSE141344 was used with DIRGs to establish the ceRNA network associated with IgAN progression. Finally, ssGSEA analysis was performed on the GSE141295 dataset to discover the abnormal inflammation in IgAN. Results Machine learning (ML) performed excellently in diagnosing IgAN using six DIRGs. A nomogram model was constructed to predict IgAN based on the six DIRGs. Three trends related to IgAN lesions were identified using STEM analysis. A ceRNA network associated with IgAN progression which contained 8 miRNAs, 14 lncRNAs, and 3 mRNAs was established. A higher macrophage ratio and lower CD4+ T cell ratio in IgAN compared to controls were observed, and the correlation between macrophages and monocytes in the glomeruli of IgAN patients was inverse compared to controls. Conclusion This study reveals the diagnostic and predictive significance of DIRGs in IgAN and finds that the imbalance between macrophages and CD4+ immune cells may be an important pathomechanism of IgAN. These results provide potential directions for the treatment and prevention of IgAN.

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

BackgroundDiffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment.MethodsGene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC).ResultsConsensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses.ConclusionsThe molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

Identification of a Solute Carrier Family-Based Signature for Predicting Overall Survival in Osteosarcoma.

Given the important role of SLC family in essential physiological processes including nutrient uptake, ion transport, and waste removal, and that their dysregulation was found in distinct forms of cancer, here we identified a novel gene signature of SLC family for patient risk stratification in osteosarcoma. Gene expression data and relevant clinical materials of osteosarcoma samples were retrieved from The Cancer Genome Atlas (TCGA) database. Prognosis-related SLC genes were identified by performing univariate Cox regression analysis and were utilized to construct a four-SLC gene signature in osteosarcoma. It allowed patients to be classified into high- and low-risk groups, and Kaplan-Meier survival analysis in the training, testing, entire, and external GSE21257 cohorts suggested that the overall survival of patients in high-risk group was consistently worse than that in low-risk group, suggesting the promising accuracy and generalizability of the SLC-based signature in predicting the prognosis of patients with osteosarcoma. Moreover, univariate and multivariate Cox regression analyses indicated that the derived risk score was the only independent prognostic factor for osteosarcoma patients in TCGA and GSE21257 cohorts. Besides, a prognostic nomogram comprising the derived risk score and clinical features including gender and age was developed for clinical decision-making. Functional enrichment analyses of the differentially expressed genes between high- and low-risk group revealed that immune-related biological processes and pathways were significantly enriched. Estimation of tumor immune microenvironment using ESTIMATE algorithm revealed that patients with lower risk score had higher stromal, immune, and ESTIMATE score, and lower tumor purity. ssGSEA analyses indicated that the scores of various immune subpopulations including CD8+ T cells, DCs, and TIL were lower in high-risk group than these in low-risk group in both cohorts. As for the related immune functions, the scores of APC co-inhibition, CCR, check-point, T cell co-stimulation, and Type II IFN response were lower in high-risk group than these in low-risk group in both cohorts. In all, we identified a novel prognostic signature based on four SLC family genes that accurately predicted overall survival in osteosarcoma patients. Furthermore, the signature is linked to differences in immunological status and immune cell infiltrations in the tumor microenvironment.

The Expression, Prognostic Value, and Immunological Correlation of MCEMP1 and its Potential Role in Gastric Cancer.

Purpose Gastric cancer (GC) is a lethal cancer with a poor 5-year relative survival, which requires a new research perspective. Our study aims to explore the biological impact of the mast cell-expressed membrane protein 1 (MCEMP1) in GC, which includes its expression and potential biological functions. Methods The expression of MCEMP1 was assessed through public databases. The GO, KEGG, and GESA analyses were conducted to explore the biofunction of MCEMP1. And ssGSEA was used to analyze the infiltration of the immune cells for MCEMP1. The proliferation, migration, and invasion of GC cells were analyzed through CCK8, colony-forming, wound healing, Transwell, and Western blot assay. Results The expression of MCEMP1 was higher in GC tissues. Further, we found a close relationship between MCEMP1 and poorer prognosis of gastric cancer by prognostic analysis. The functional analysis showed that MCEMP1 is involved in immune, inflammation, and metabolism-related pathways. The ssGSEA analysis indicated MCEMP1 mRNA expression was associated with immune infiltration of multiple immune cells. In cellular experiments, the invasion and metastasis of gastric cancer cells could be promoted by regulating the rise of MCEMP1 expression. Western blot analysis showed that regulation of MCEMP1 expression can affect EMT-related protein expression and that NF-κB expression is involved in this process. Conclusion MCEMP1 shows a potential value for the prognosis in GC. And, abnormal expression of MCEMP1 in GC is correlated with tumor immune cell infiltration. In in vitro experiments, MCEMP1 can affect the proliferation, migration, and invasion of GC cells by regulating EMT, in which TLR4/NOD2/NF-κB was involved.

Development and Validation of a 7-Gene Inflammatory Signature Forecasts Prognosis and Diverse Immune Landscape in Lung Adenocarcinoma.

Background: Inflammatory responses are strongly linked with tumorigenesis and cancer development. This research aimed to construct and validate a novel inflammation response–related risk predictive signature for forecasting the prognosis of patients with LUAD. Methods: Differential expression analysis, univariate Cox, LASSO, and multivariate Cox regression analyses of 200 inflammatory response–related genes (IRRG) were performed to establish a risk predictive model in the TCGA training cohort. The performance of the IRRG model was verified in eight GEO datasets. GSEA analysis, ESTIMATE algorithms, and ssGSEA analysis were applied to elucidate the possible mechanisms. Furthermore, the relationship analysis between risk score, model genes, and chemosensitivity was performed. Last, we verified the protein expression of seven model genes by immunohistochemical staining or Western blotting. Results: We constructed a novel inflammatory response–related 7-gene signature (MMP14, BTG2, LAMP3, CCL20, TLR2, IL7R, and PCDH7). Patients in the high-risk group presented markedly decreased survival time in the TCGA cohort and eight GEO cohorts than the low-risk group. Interestingly, multiple pathways related to immune response were suppressed in high-risk groups. The low infiltration levels of B cell, dendritic cell, natural killer cell, and eosinophil can significantly affect the unsatisfactory prognosis of the high-risk group in LUAD. Moreover, the tumor cells’ sensitivity to anticancer drugs was markedly related to risk scores and model genes. The protein expression of seven model genes was consistent with the mRNA expression. Conclusion: Our IRRG prognostic model can effectively forecast LUAD prognosis and is tightly related to immune infiltration.

Comprehensive analysis of ferroptosis-related genes and prognosis of cutaneous melanoma

BackgroundCutaneous Melanoma (CM) is a malignant disease with increasing incidence and high mortality. Ferroptosis is a new kind of cell death and related to tumor blood and lymphatic metastasis. This study aims at using bioinformatics technology to construct a prognostic signature and identify ferroptosis-related biomarkers to improve the prognosis and treatment of cutaneous melanoma.MethodsWe used bioinformatics tools to analyze RNA sequencing expression data with clinical information from multiple databases, utilized varieties of statistical methods to construct a ferroptosis-related prognostic signature of cutaneous melanoma and screened out specific genes with independent prognostic ability.ResultsWe obtained 22 ferroptosis-related (P < 0.05) prognostic DEGs in the uniCox regression analysis, among which 10 high-expressed genes (ATG5, CHAC1, FANCD2, FBXL5, HMOX2, HSPB1, NQO1, PEBP1, PRNP, SLC3A2) were screened out by LASSO regression analysis to establish a predictive model. Meanwhile, the ferroptosis-related signature and the nomogram we drew performed an excellent performance based on Kaplan–Meier (K–M), Receiver operating characteristic (ROC) and calibration curves. Univariate and multivariable cox analyses displayed that our model was greater than other prognostic features. GO and KEGG analyses revealed that 10-biomarker signature was mainly related to epidermis differentiation and immunity. ssGSEA analysis indicated that the immune status between the two risk groups was highly different. Besides, we found that two genes (CP, ZEB1) had independent prognostic ability and can be applied for drug research. Both genes were highly related to immunity. GSEA illustrated that ZEB1 may be involved in cellular functions such as proliferation, apoptosis, and migration, while CP was closely connected to immune cell related functions.ConclusionThe present study suggested a 10-biomarker signature can be clinically used to predict the prognosis of cutaneous melanoma, which was better than conventional factors. CP and ZEB1 were independent prognostic genes and can be applied to guide treatment. In addition, ZEB1 mutation was highly related to overall survival in cutaneous melanoma, while CP may be associated with tumor progression. Our study comprehensively analyzed the relationship between iron metabolism, ferroptosis-related genes, and the prognosis of cutaneous melanoma, provided new insight for molecular mechanisms and treatment of ferroptosis and cutaneous melanoma.