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Multi-Omics Characterization of a Glycerolipid Metabolism-Related Gene Enrichment Score in Colon Cancer.
BACKGROUND: Glycerolipid metabolism is involved in the genesis and progression of colon cancer. The current study aims at exploring the prognostic value and potential molecular mechanism of glycerolipid metabolism-related genes in colon cancer from the perspective of multi-omics. METHODS: Clinical information and mRNA expression data of patients with colon cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Single-sample gene set enrichment analysis (ssGSEA) was applied to calculate the glycerolipid metabolism-related gene enrichment score (GLMS). Univariable and multivariable Cox regression analyses were used to study the prognostic value of GLMS in TCGA-COAD and GSE39582 cohorts. The molecular mechanism of the prognostic factor was investigated via immune cell infiltration estimation and correlation analysis of cancer hallmark pathways. Single-cell transcriptomic dataset GSE146771 was used to identify the cell populations which glycerolipid metabolism targeted on. RESULTS: The GLMS was found to be associated with tumor location and consensus molecular types (CMSs) of colon cancer in TCGA-COAD cohort (P < 0.05). Patients in the low-GLMS group exhibited poorer overall survival (OS) in TCGA cohort (P = 0.03; HR, 0.63; 95% CI, 0.42-0.94), which was further validated in the GSE39582 dataset (P < 0.001; HR, 0.57; 95% CI, 0.43-0.76). The association between the GLMS and OS remained significant in the multivariable analysis (TCGA cohort: P = 0.04; HR, 0.64; 95% CI, 0.42-0.98; GSE39582 cohort: P < 0.001; HR, 0.60; 95% CI, 0.45-0.80). The GLMS was positively correlated with cancer hallmark pathways including bile acid metabolism, xenobiotic metabolism, and peroxisome and negatively correlated with pathways such as interferon gamma response, allograft rejection, apoptosis, and inflammatory response (P < 0.05). Increased immune infiltration and upregulated expression of immune checkpoints were observed in patients with lower GLMS (P < 0.05). Single-cell datasets verified the different distribution of GLMS in cell subsets, with significant enrichment of GLMS in malignant cells and Tprolif cells. CONCLUSION: We demonstrated that GLMS was a potential independent prognostic factor for colon cancer. The GLMS was also correlated with several cancer hallmark pathways, as well as immune microenvironment.
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Dynamic molecular atlas of cardiac fibrosis at single-cell resolution shows CD248 in cardiac fibroblasts orchestrates interactions with immune cells.
Post-injury remodeling is a complex process involving temporal specific cellular interactions in the injured tissue where the resident fibroblasts play multiple roles. Here, we performed single-cell and spatial transcriptome analysis in human and mouse infarcted hearts to dissect the molecular basis of these interactions. We identified a unique fibroblast subset with high CD248 expression, strongly associated with extracellular matrix remodeling. Genetic Cd248 deletion in fibroblasts mitigated cardiac fibrosis and dysfunction following ischemia/reperfusion. Mechanistically, CD248 stabilizes type I transforming growth factor beta receptor and thus upregulates fibroblast ACKR3 expression, leading to enhanced T cell retention. This CD248-mediated fibroblast-T cell interaction is required to sustain fibroblast activation and scar expansion. Disrupting this interaction using monoclonal antibody or chimeric antigen receptor T cell reduces T cell infiltration and consequently ameliorates cardiac fibrosis and dysfunction. Our findings reveal a CD248+ fibroblast subpopulation as a key regulator of immune-fibroblast cross-talk and a potential therapy to treat tissue fibrosis.
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Comprehensive pan-cancer analysis of HSPG2 as a marker for prognosis.
BACKGROUND: In recent years, several studies have shown that HSPG2 is associated with the prognosis of specific cancers. The aim of this study was to investigate the prognostic value of HSPG2 in pan-cancer and to analyze its possible mechanisms. METHODS: We used The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to explore the expression of HSPG2 in 33 tumors and corresponding controls. Univariate Cox regression and Kaplan-Meier survival analysis were applied to detect the effects of HSPG2 on overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in patients with these tumors, and to analyze the relationship between HSPG2 and clinical characteristics. And we further analyzed the relationship between HSPG2 and immune infiltration, DNA methylation and single cell function. And GO and KEGG enrichment analyses were performed using HSPG2 co-expressed genes. Finally, we explored the diagnostic efficacy of HSPG2 for diseases of interest and validated it using qPCR experiment. RESULTS: HSPG2 was lowly expressed in 17 cancers and highly expressed in 11 cancers, and was correlated with patient's clinical characteristics in many cancers. Multivariate regression analysis showed that HSPG2 was an independent prognostic factor for DSS, OS, and PFI in bladder urothelial carcinoma (BLCA) and Mesothelioma (MESO). HSPG2 was correlated with DNA methylation, single-cell function, and immune infiltration in a variety of cancers. HSPG2 exhibited a good diagnostic efficacy for BLCA and MESO. qPCR and western blot results showed that HSPG2 expression was increased in mesothelioma compared to normal controls. CONCLUSION: These findings suggest that HSPG2 could be considered as a potential diagnostic and prognostic marker for BLCA and MESO.
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Identification and validation of Cystatin A as a novel promising therapeutic target for gastric cancer.
BACKGROUND: The effect of pharmacological treatment of gastric cancer (GC) is limited, thus, it holds significant scientific importance to thoroughly investigate the molecular mechanisms underlying GC development and identify novel molecules capable of substantially extending patients' survival. This study utilized bioinformatics techniques to identify 11 genes associated with recurrence-free survival (RFS) in GC patients and investigated the potential biological functions of these genes through single-cell transcriptomic analysis. Subsequently, a single gene Cystatin A (CSTA) was selected for further analysis to explore its impact on signaling pathways and treatment. METHODS: Differentially expressed genes (DEGs) were identified and overlapped in the analysis of RFS to identify potential prognostic genes for GC patients, based on data from the Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) and GSE54129. Subsequently, a prognostic model based on RFS in GC patients was established. Single-cell sequencing data were employed to explore the potential functions of these model genes. CSTA, one of the RFS-related genes, was further investigated using immunohistochemistry (IHC), Cell Counting Kit 8 (CCK-8), transwell, scratch, colony formation assays, flow cytometry, and Western blotting methods. RESULTS: Through bioinformatics analysis, we identified 23 RFS-related genes in GC. Using the least absolute shrinkage and selection operator (LASSO)-Cox method, an RFS prognostic model was developed which pinpointed 11 GC prognosis-related (GPR) genes as significant factors influencing RFS in GC patients. The single-cell analysis revealed their potential role in affecting differentiation and maturation of pre-fibroblasts thereby impacting RFS in GC patients. CSTA exhibited low expression levels in GC tissues. Overexpression of CSTA promoted apoptosis in GC cells through the caspase-dependent apoptotic pathway and enhanced their response to cisplatin via this same pathway. CONCLUSIONS: The 11 GPR genes are primarily enriched within a specific type of stromal cell exhibiting heightened communication, metabolism, and differentiation levels. The gene signature of these stromal cells has implications for patient prognosis. Additionally, CSTA, a gene related to prognosis, has been shown to influence apoptosis levels in GC cells.
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Spatial Proteomics and Epigenomics Reveal Cell Morphology and Epigenetic Gene Regulation in Human Hippocampus.
We present the first spatial tri-omic (proteomic, transcriptomic, and epigenomic) taxonomy of human hippocampus, addressing two key challenges in single-cell and spatial transcriptomics: visualizing cell morphology and epigenetic gene regulation in situ. By designing and multiplex imaging a novel panel of 25 protein targets, we generated the first large scale spatial proteomics map of the hippocampus at single cell resolution (330 nanometers). Through integrated spatial proteomic and transcriptomic analyses, we identified canonical hippocampal subfields and distinct cell types within the dentate gyrus and cornu Ammonis (CA). RNA synthesis rates were the highest in the CA pyramidal neurons than any other hippocampal subfields. To map gene regulation, we applied state-of-art spatial ATAC-seq to identify open chromatin regions and cis-regulatory elements, revealing how chromatin landscapes differ across hippocampal subfields. These regional differences help explain why certain neurons are more excitable and recruited for circuit formation, as their chromatin is open and responsive to transcription factors. Together, these approaches created the most comprehensive spatial atlas of the human hippocampus to date, consisting of over 1.6 million single cells from 16 individuals. This study demonstrates the use of spatial proteomics and epigenomics to investigate the molecular neuropathology of human brain in both healthy and diseased states.
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Key genes and associated mechanisms of PCOS and EC comorbidity: A bioinformatics analysis.
Endometrial cancer (EC) is a major reproductive system tumor and a common cancer in women. Polycystic ovary syndrome (PCOS) is one of the most prevalent female reproductive endocrine disorders. The incidence of EC is significantly higher in individuals with PCOS. This study seeks to elucidate the organic correlations and interaction mechanisms between the 2 diseases through series of exploration of key genes with a bioinformatics analysis. The PCOS sample data and the EC single-cell dataset were downloaded from the gene expression omnibus database. The EC sample data were retrieved from the cancer genome atlas public database. The random survival forest method was employed to identify key genes associated with the prognosis of PCOS and EC comorbidity. Corresponding analyses on functional pathway enrichment, regulatory networks, and immune micro-environment are conducted. From a bioinformatics perspective, the association and interaction mechanisms between PCOS and EC comorbidity were explored to provide research and development references for the prevention and control of PCOS and EC comorbidity. Five key genes associated with the prognosis of PCOS and EC comorbidity were identified using the random survival forest method. The identified genes are SYTL1, PARVG, ID4, IL1RN, and S100A9. The abnormal expression of these key genes has impacted various enrichment pathways, including the TGF-β signaling-pathway, motif regulatory networks (such as motif cisbp__M4556), and miRNA regulatory networks, which encompass genes such as ATM, BARD1 and BRCA1. Furthermore, these also influence the immune cell microenvironment, such as T cells regulatory. Collectively, these key genes play a significant role in the occurrence and progression of comorbidities through the pathways mentioned above. The dysregulation of key genes (SYTL1, PARVG, ID4, IL1RN, S100A9) in the context of PCOS-EC comorbidities, along with their associated enrichment pathways, including the TGF-β signaling-pathway and immune microenvironment, plays a significant role in the occurrence and progression of EC.
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A new CCCH-type zinc finger-related lncRNA signature predicts the prognosis of clear cell renal cell carcinoma patients.
Background: Clear cell renal cell carcinoma (ccRCC) is the main component of renal cell carcinoma (RCC), and advanced ccRCC frequently indicates a poor prognosis. The significance of the CCCH-type zinc finger (CTZF) gene in cancer has been increasingly demonstrated during the past few years. According to studies, targeted radical therapy for cancer treatment may be a revolutionary therapeutic approach. Both lncRNAs and CCCH-type zinc finger genes are essential in ccRCC. However, the predictive role of long non-coding RNA (lncRNA) associated with the CCCH-type zinc finger gene in ccRCC needs further elucidation. This study aims to predict patient prognosis and investigate the immunological profile of ccRCC patients using CCCH-type zinc finger-associated lncRNAs (CTZFLs). Methods: From the Cancer Genome Atlas database, RNA-seq and corresponding clinical and prognostic data of ccRCC patients were downloaded. Univariate and multivariate Cox regression analyses were conducted to acquire CTZFLs for constructing prediction models. The risk model was verified using receiver operating characteristic curve analysis. The Kaplan-Meier method was used to analyze the overall survival (OS) of high-risk and low-risk groups. Multivariate Cox and stratified analyses were used to assess the prognostic value of the predictive feature in the entire cohort and different subgroups. In addition, the relationship between risk scores, immunological status, and treatment response was studied. Results: We constructed a signature consisting of eight CTZFLs (LINC02100, AC002451.1, DBH-AS1, AC105105.3, AL357140.2, LINC00460, DLGAP1-AS2, AL162377.1). The results demonstrated that the prognosis of ccRCC patients was independently predicted by CTZFLs signature and that the prognosis of high-risk groups was poorer than that of the lower group. CTZFLs markers had the highest diagnostic adequacy compared to single clinicopathologic factors, and their AUC (area under the receiver operating characteristic curve) was 0.806. The overall survival of high-risk groups was shorter than that of low-risk groups when patients were divided into groups based on several clinicopathologic factors. There were substantial differences in immunological function, immune cell score, and immune checkpoint expression between high- and low-risk groups. Additionally, Four agents, including ABT737, WIKI4, afuresertib, and GNE 317, were more sensitive in the high-risk group. Conclusion: The Eight-CTZFLs prognostic signature may be a helpful prognostic indicator and may help with medication selection for clear cell renal cell carcinoma.
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Establishment and validation of a novel lysosome-related gene signature for predicting prognosis and immune landscape in hepatocellular carcinoma.
BACKGROUND: Recent studies have shown that lysosomes not only provide energy for tumor cell growth, but also participate in the occurrence and development of malignant tumors by regulating various ways of tumor cell death. However, the role of lysosome associated genes (LSAGs) in hepatocellular carcinoma (HCC) remains unclear. METHODS: Transcriptome data and clinical data of HCC were downloaded from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. We identified differential expression of LSAGs by comparing tumor tissue with normal liver tissue. Subsequently, we used univariate COX analysis and least absolute shrinkage and selection operator (LASSO) COX regression to construct the prognostic feature of LSAGs. Kaplan-Meier survival curve and receiver operating characteristic curve were used to evaluate the predictive ability of LSAGs feature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis of risk differential genes. The relationship between LSAGs score and tumor microenvironment and chemotherapy drug sensitivity was analyzed. Finally, the cellular communication of tumor cells with high and low expression of model LSAGs was explored. RESULTS: We identified sixteen prognostic associated LSAGs, four of which were selected to construct prognostic feature of LSAGs. Patients in the low LSAGs group had a better prognosis than those in the high LSAGs group. GO and KEGG analyses showed that risk differential genes were enriched in leukocyte migration, cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway. The group with low LSAGs score had lower immune score. Patients in the high LSAGs group were more sensitive to drugs for chemotherapy. In addition, tumor cells with high expression of model LSAGs showed stronger association with immune cells through the interleukin-2 (IL2), fibroblast growth factor (FGF), adiponectin, and bone morphogenetic proteins (BMP) signaling pathways. CONCLUSION: We established a LSAGs signature that had the ability to predict clinical prognosis and immune landscape, proposing potential therapeutic targets for HCC.
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E2F7 serves as a potential prognostic biomarker for lung adenocarcinoma.
E2F transcription factors (E2Fs) are a family of transcription factors critical regulators of the cell cycle, apoptosis, and differentiation, thus influencing tumorigenesis. However, the specific roles of E2Fs in lung adenocarcinoma (LUAD) remain unclear. Data from The Cancer Genome Atlas (TCGA) were used. R version. 4.0.3 and multiple databases (TIMER, cBioportal, gene expression profile interaction analysis [GEPIA], LinkedOmics, and CancerSEA) were utilized to investigate mRNA expression, mutational analysis, prognosis, clinical correlations, co-expressed gene, pathway and network, and single-cell analyses. Immunohistochemistry (IHC) confirmed that E2F transcription factor 7 (E2F7) correlated with LUAD. Among the E2Fs, E2F7 was identified by constructing a prognostic model most significantly associated with overall survival (OS) in LUAD patients. The univariate and multivariate Cox regression analyses showed that E2F7, p-T stage, and p-TNM stage were closely related to OS and progression-free survival (PFS) (P < .05) in LUAD. E2F 7/8 were also identified as significantly associated with tumor stage in the GEPIA database. Compared with paracancerous tissues, E2F7 was up-regulated in LUAD by IHC, and E2F7 might be positively correlated with larger tumors and higher TNM stages. E2F7 may primarily regulate DNA repair, damage, and cell cycle processes and thus affect LUAD tumorigenesis, invasion, and metastasis by LinkedOmics and CancerSEA. E2F7 serves as a potential prognostic biomarker for LUAD.
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Effector T cells under hypoxia have an altered transcriptome similar to tumor-stressed T cells found in non-responsive melanoma patients.
BACKGROUND: In the tumor microenvironment (TME), hypoxia stands as a significant factor that modulates immune responses, especially those driven by T cells. As T cell-based therapies often fail to work in solid tumors, this study aims to investigate the effects of hypoxia on T cell topo-distribution in the TME, gene expression association with T cell states, and clinical responses in melanoma. METHODS: To generate detailed information on tumor oxygenation and T cell accessibility, we used mathematical modeling of human melanoma tissue microarrays that incorporate oxygen supply from vessels, intratumoral diffusion, and cellular uptake. We created tumor maps and derived plots showing the fraction of CD4 and CD8 T cells against the distance to the nearest vessel and oxygen pressure. To assess their function and transcriptional changes caused by hypoxia, effector T cells were generated and cultured under hypoxia (0.5% oxygen) or normoxia (21% oxygen). The T cell hypoxia-transcriptional signature was compared against datasets from msigDB, iATLAS (clinical trials of melanoma patients treated with immune checkpoint inhibitors (ICIs)), ORIEN AVATAR (real-world melanoma patients treated with ICIs), and a single-cell atlas of tumor-infiltrating lymphocytes. RESULTS: We made three specific observations: (1) in melanoma T cells preferentially accumulated in oxygenated areas close to blood vessels (50-100 µm from the vasculature in the regions of high oxygen availability) but not in hypoxic areas far from blood vessels. (2) Our analysis confirmed that under hypoxia, T cell functions were significantly reduced compared with normoxic conditions and accompanied by a unique gene signature. Furthermore, this hypoxic gene signature was prevalent in resting and non-activated T cells. Notably and clinically relevant, the hypoxic T cell gene set was found to correlate with reduced overall survival and reduced progression-free survival in melanoma patients, which was more pronounced in non-responder patients undergoing ICI therapy. (3) Finally, compared with a single-cell atlas of tumor-infiltrating T cells, our hypoxia signature aligned with a population of cells at a state termed stress response state (TSTR). CONCLUSIONS: Our study highlights the critical role of hypoxia in shaping T cell distribution and its correlation with clinical outcomes in melanoma. We revealed a preferential accumulation of T cells in oxygenated areas. Moreover, hypoxic T cells develop a distinct hypoxic gene signature prevalent in resting, non-activated T cells and TSTR that was also associated with poorer outcomes, particularly pronounced among non-responders to ICIs.