Immune infiltration difference between tumor and adjacent normal regions is prognostic for gastric cancer patients.
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ABSTRACT:
SUBMITTER: Zhang B
PROVIDER: S-EPMC9000221 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
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Immune infiltration difference between tumor and adjacent normal regions is prognostic for gastric cancer patients.
Zhang Baoyi B Yao Kevin K Cheng Chao C
Clinical and translational discovery 20220220 1
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Project description:BackgroundIn the past decade, considerable research efforts on gastric cancer (GC) have been expended, however, little advancement has been made owing to the lack of effective biomarkers and treatment options. Herein, we aimed to examine the levels of expression, mutations, and clinical relevance of HMGs in GC to provide sufficient scientific evidence for clinical decision-making and risk management.MethodsGC samples were obtained from The Cancer Genome Atlas (TCGA). University of California Santa Cruz (UCSC) XENA, Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, cBioPortal, GeneMANIA, STRING, LinkedOmics, and DAVID databases were employed. The "ggplot2" package in the R software (×64 3.6.3) was used to thoroughly analyze the effects of HMGs. qRT-PCR was performed to assess HMG levels in GC cell lines.ResultsA total of 375 GC tissues and 32 paraneoplastic tissues were analyzed. The levels of HMGA1, HMGA2, HMGB1, HMGB2, HMGB3, HMGN1, HMGN2, and HMGN4 expression were increased in GC tissues relative to normal gastric tissues. HMGA1, HMGA2, HMGB1, HMGB2, and HMGB3 were highly expressed in GC cell lines. The OS was significantly different in the group showing low expressions of HMGA1, HMGA2, HMGB1, HMGB2, HMGB3, HMGN2, HMGN3, and HMGN5. There was a significant difference in RFS between the groups with low HMGA2, HMGB3, and high HMGN2 expression. The levels of HMGA2, HMGB3, and HMGN1 had a higher accuracy for prediction to distinguish GC from normal tissues (AUC value > 0.9). HMGs were tightly associated with immune infiltration and tumor immune escape and antitumor immunity most likely participates in HMG-mediated oncogenesis in GC. GO and KEGG enrichment analyses showed that HMGs played a vital role in the cell cycle pathway.ConclusionsOur results strongly suggest a vital role of HMGs in GC. HMGA2 and HMGB3 could be potential markers for prognostic prediction and treatment targets for GC by interrupting the cell cycle pathway. Our findings might provide renewed perspectives for the selection of prognostic biomarkers among HMGs in GC and may contribute to the determination of the optimal strategy for the treatment of these patients.
| S-EPMC9780705 | biostudies-literature
Project description:Gastric cancer (GC) is a highly fatal and common malignancy of the digestive system. Recent therapeutic advancements have significantly improved the clinical outcomes in GC, but due to the unavailability of suitable molecular targets, a large number of patients do not respond to the immune checkpoint inhibitors (ICI) therapy. To identify and validate potential therapeutic and prognostic targets of gastric cancer, we used the "inferCNV" R package for analyzing single-cell sequencing data (GSE112302) of GC and normal epithelial cells. First, by using LASSO, we screened genes that were highly correlated with copy number variations (CNVs). Therefrom, five gene signature (CPVL, DDC, GRTP1, ONECUT2, and PRSS21) was selected by cross-validating the prognosis and risk management with the GC RNA-seq data obtained from GEO and TCGA. Moreover, the correlation analyses between CNVs of these genes and immune cell infiltration in gastric cancer identified CPVL as a potential prognostic marker. Finally, CPVL showed high expression in gastric cancer samples and cell lines, then siRNA-mediated silencing of CPVL expression in gastric cancer cells showed significant proliferation arrest in MGC803 cells. Here, we conclude that CNVs are key regulators of the immune cells infiltration in gastric TME as well as cancer development, and CPVL could potentially be used as a prognostic and therapeutic marker in gastric cancer.
| S-EPMC9214698 | biostudies-literature
Project description:IntroductionIn recent years, the proportion of patients with NSCLC diagnosed at an early stage has increased continuously.MethodsIn this study, we analyzed samples and data collected from 119 samples from 67 early stage patients with NSCLC, including 52 pairs of tumor and adjacent non-neoplastic samples, and performed RNA-sequencing analysis with high sequencing depth.ResultsWe found that immune-related genes were highly enriched among the differentially expressed genes and observed significantly higher inferred immune infiltration levels in adjacent non-neoplastic samples than in tumor samples. In survival analysis, the infiltration of certain immune cell types in tumor, but not adjacent non-neoplastic, samples were associated with overall patient survival, and excitingly, the differential infiltration between paired samples (tumor minus non-neoplastic) was more prognostic than expression in either non-neoplastic or tumor tissues. We also performed B cell receptor (BCR) and T cell receptor (TCR) repertoire analysis and observed more BCR/TCR clonotypes and increased BCR clonality in tumor than in non-neoplastic samples. Finally, we carefully quantified the fraction of the five histologic subtypes in our adenocarcinoma samples and found that higher histologic pattern complexity was associated with higher immune infiltration and low TCR clonality in the tumor-proximal regions.ConclusionsOur results indicated significantly differential immune characteristics between tumor and adjacent non-neoplastic samples and suggested that the two regions provided complementary prognostic values in early-stage NSCLCs.
| S-EPMC10528252 | biostudies-literature
Project description:The effect of POC1 centriolar protein A (POC1A) on gastric cancer (GC) has not been clearly defined. In this study, POC1A expression and clinical information in patients with GC were analyzed. Multiple databases were used to investigate the genes that were co-expressed with POC1A and genes whose changes co-occurred with genetic alternations of POC1A. Moreover, the TISIDB and TIMER databases were used to analyze immune infiltration. The GSE54129 GC dataset and LASSO regression model (tumor vs. normal) were employed, and 6 significant differentially expressed genes (LAMP5, CEBPB, ARMC9, PAOX, VMP1, POC1A) were identified. POC1A was selected for its high expression in adjacent tissues, which was confirmed with IHC. High POC1A expression was related to better overall and recurrence-free survival. GO and KEGG analyses demonstrated that POC1A may regulate the cell cycle, DNA replication and cell growth. Furthermore, POC1A was found to be correlated with immune infiltration levels in GC according to the TISIDB and TIMER databases. These findings indicate that POC1A acts as a tumor suppressor in GC by regulating the cell cycle and cell growth. In addition, POC1A preferentially regulates the immune infiltration of GC via several immune genes. However, the specific mechanism requires further study.
| S-EPMC7732308 | biostudies-literature
Project description:BackgroundIn identifying prognostic markers in cancer, the roles of tumor-adjacent normal tissues are often confined to drawing expression differences between tumor and normal tissues rather than being treated as the main targets of investigations. Thus, differential expression analysis between tumors and adjacent normal tissues is performed prior to prognostic analysis in previous studies. However, recent studies have suggested that the prognostic relevance of differentially expressed genes (DEGs) is insignificant for some cancers, contradicting conventional approaches METHODS: This study investigated the prognostic efficacy of transcriptomic data from tumors and adjacent normal tissues using The Cancer Genome Atlas dataset. Prognostic analysis using Cox regression models and survival prediction using machine-learning models and feature selection methods were employed.ResultsThe results revealed that for kidney, liver, and head and neck cancer, adjacent normal tissues harbored higher proportions of prognostic genes and exhibited better survival prediction performance than tumor tissues and DEGs in machine-learning models. Furthermore, the application of a distance correlation-based feature selection method to kidney and liver cancer using external datasets revealed that the selected genes for adjacent normal tissues exhibited higher prediction performance than those for tumor tissues. The study results suggest that the expression levels of genes in adjacent normal tissues are potential prognostic markers. The source code of this study is available at https://github.com/DMCB-GIST/Survival_Normal.
| S-EPMC10242347 | biostudies-literature
Project description:AimTo explore the correlations between the expression of zinc finger protein 521 (ZNF521) with immune invasion and prognosis of gastric cancer.MethodsExpression of ZNF521 was examined by immunohistochemistry in gastric cancer cases. Kaplan-Meier plotter was used to determine the relationships between ZNF521 and prognosis. TIMER and GEPIA were used to analyze the correlation between ZNF521 expression and gene markers of immune cell infiltration.ResultsThe expression of ZNF521 was up-regulated in gastric cancer samples. Kaplan-Meier analysis indicated that higher expression of ZNF521 was associated with poor prognosis. The expression of ZNF521 was correlated with infiltrating levels of CD4+ T and CD8+ T cells, macrophages, neutrophils, and dendritic cells in gastric cancer, which also correlated with diverse immune marker sets.ConclusionsZNF521 is correlated significantly with immune cell infiltration and is a valuable biomarker for prognosis in gastric cancer.
| S-EPMC9606838 | biostudies-literature
Project description:Atherosclerotic plaque instability contributes to ischaemic stroke and myocardial infarction. This study is to compare the abundance and difference of immune cell subtypes within unstable atherosclerotic tissues. CIBERSORT was used to speculate the proportions of 22 immune cell types based on a microarray of atherosclerotic carotid artery samples. R software was utilized to illustrate the bar plot, heat map and vioplot. The immune cell landscape in atherosclerosis was diverse, dominated by M2 macrophages, M0 macrophages, resting CD4 memory T cells and CD8 T cells. There was a significant difference in resting CD4 memory T cells (p = 0.032), T cells follicular helper (p = 0.033), M0 (p = 0.047) and M2 macrophages (p = 0.012) between stable and unstable atherosclerotic plaques. Compared with stable atherosclerotic plaques, unstable atherosclerotic plaques had a higher percentage of M2 macrophages. Moreover, correlation analysis indicated that the percentage of naïve CD4 T cells was strongly correlated with that of gamma delta T cells (r = 0.93, p < 0.001), while memory B cells were correlated with plasma cells (r = 0.85, p < 0.001). In summary, our study explored the abundance and difference of specific immune cell subgroups at unstable plaques, which would aid new immunotherapies for atherosclerosis.
| S-EPMC8642673 | biostudies-literature
Project description:Background: Iron is an essential nutrient involved in the redox cycle and the formation of free radicals. The reprogramming of iron metabolism is the main link to tumor cell survival. Ferroptosis is an iron-dependent form of regulated cell death associated with cancer; the characteristics of ferroptosis in cancers are still uncertain. This study aimed to explore the application value and gender difference of ferroptosis in prognosis and immune prediction to provide clues for targeted therapy of gastric cancer. Methods: We comprehensively evaluated the ferroptosis levels of 1,404 gastric cancer samples from six independent GC cohorts based on ferroptosis-related specific genes and systematically correlated ferroptosis with immune cell infiltrating and gender characteristics. The ferroptosis score was constructed to quantify the ferroptosis levels of individual tumors using principal component analysis (PCA) algorithms. Results: We identified two distinct ferroptosis subtypes in gastric cancer, namely Subtype-A and Subtype-B. We found that male patients in Subtype-B had the worst prognosis in contrast with the other groups. Three sex hormone receptors (AR, ER, and PR) in Subtype-B tumor patients were higher than in Subtype-A tumor patients in GC, while the HER2 displayed an opposite trend. We developed a risk model termed ferroptosis score to evaluate ferroptosis levels within individual tumors. The low-ferroptosis score group was characterized by activation of immune cells and increased mutation burden, which is also linked to increased neoantigen load and enhanced response to anti-PD-1/L1 immunotherapy. The patients with a low-ferroptosis score showed a high microsatellite instability status (MSI-H) and had a higher response to immunotherapy. Furthermore, the patients with low-ferroptosis scores have a lower estimated IC50 in the several chemotherapy drugs, including paclitaxel, gemcitabine, and methotrexate. Conclusions: We revealed that sex hormone receptors and immune cell infiltration were markedly different between ferroptosis subtypes in GC patients. The results suggested that gender difference may be critical when the ferroptosis-related strategy is applied in GC treatment. Further, ferroptosis levels were identified with an extreme variety of prognosis and tumor immune characteristics, which might benefit GC individualized treatment.
| S-EPMC8716917 | biostudies-literature
Project description:BackgroundCancer prognosis-related signatures have traditionally been constructed based on gene expression profiles derived from tumor or normal tissues. However, the potential benefits of incorporating gene expression profiles from both tumor and normal tissues to improve signature performance have not been explored.MethodsIn this study, we developed three prognostic models for lung adenocarcinoma (LUAD) using gene expression profiles from tumor tissues, normal tissues, and a combination (COM) of both, sourced from The Cancer Genome Atlas (TCGA). To ensure comparability, the same workflow was followed for all three models.ResultsWhen applied to the TCGA LUAD dataset, the tumor-derived model exhibited the best overall performance, except in calibration analysis, where the normal-derived model performed better. The COM-derived model demonstrated intermediate performance. Validation on three independent test datasets revealed that the COM-derived model showed the best performance, while the normal-derived model showed the worst. In overall survival (OS) analysis, the low-risk group defined by the COM-derived model consistently exhibited longer mean survival times. The tumor-derived model did not consistently show this trend, and the normal-derived model produced opposite results. In discrimination analysis, no significant differences were observed. The COM-derived model demonstrated good discrimination ability for short periods, while the tumor-derived model performed better for longer periods. In calibration analysis, both the COM and tumor-derived models had similar absolute prediction errors, which were better than those of the normal-derived model. However, the tumor-derived model tended to underestimate survival rates. The clinical feature analysis and validation in GSE229705 indicate that the risk score (RS) from the COM model is the most clinically significant. These results demonstrate that the COM model's RS aligns more closely with clinical data, maintaining stable performance and the strongest generalizability.ConclusionsOverall, the COM-derived model demonstrated the best generalization ability. The superior performance of the tumor-derived model in the TCGA LUAD dataset might be due to overfitting. Our results suggest that appropriate combinations of gene expression data from tumor and normal tissues can enhance the predictive power of prognostic signatures.
| S-EPMC11466592 | biostudies-literature
Project description:BackgroundThe HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of HLA genes is to recognize "non-self" antigens and to present them on the cell surface to T cells, which instigate an immune response toward infected or transformed cells. While sequence variation in the antigen-binding groove of HLA may modulate the repertoire of immunogenic antigens presented to T cells, alterations in HLA expression can significantly influence the immune response to pathogens and cancer.MethodsRNA sequencing was used here to accurately genotype the HLA region and quantify and compare the level of allele-specific HLA expression in tumors and patient-matched adjacent normal tissue. The computational approach utilized in the study types classical and non-classical Class I and Class II HLA alleles from RNA-seq while simultaneously quantifying allele-specific or personalized HLA expression. The strategy also uses RNA-seq data to infer immune cell infiltration into tumors and the corresponding immune cell composition of matched normal tissue, to reveal potential insights related to T cell and NK cell interactions with tumor HLA alleles.ResultsThe genotyping method outperforms existing RNA-seq-based HLA typing tools for Class II HLA genotyping. Further, we demonstrate its potential for studying tumor-immune interactions by applying the method to tumor samples from two different subtypes of breast cancer and their matched normal breast tissue controls.ConclusionsThe integrative RNA-seq-based HLA typing approach described in the study, coupled with HLA expression analysis, neoantigen prediction and immune cell infiltration, may help increase our understanding of the interplay between a patient's tumor and immune system; and provide further insights into the immune mechanisms that determine a positive or negative outcome following treatment with immunotherapy such as checkpoint blockade.
| S-EPMC10821267 | biostudies-literature