Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Esophageal Cancer Associated Immune Genes as Biomarkers for Predicting Outcome in Upper Gastrointestinal Tumors.

ABSTRACT: Esophageal cancer (EC) is the seventh most common tumor in the world, ranking the sixth leading cause of cancer death, with a 5-year survival rate of 15-25%. Therefore, reliable prognostic biomarkers are needed to effectively predict the prognosis of EC. In this study, the gene profile information of the EC cohort served as a training set, which was derived from TCGA and Immport databases. GO and KEGG enrichment analysis was performed on the differential genes in normal and tumor groups of EC. The immune genes in differentially expressed genes (DEGs) were further obtained for univariate and multivariate Cox and Lasso regression analysis, and 6 independent immune genes (S100A3, STC2, HSPA6, CCL25, GPER1, and OSM) associated with prognosis were obtained to establish an immune risk score signature (IRSS). The signature was validated using head and neck cancers (HNSC) and gastric cancer (GC)in upper gastrointestinal malignancies as validation sets. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly favorable than that of the low-risk group in both the training set (P < 0.001; HR = 3.68, 95% CI = 2.14-6.35) and the validation set (P = 0.010; HR = 1.43, 95% CI = 1.09-1.88). A nomogram combining multiple clinical information and IRSS was more effective than a single independent prognostic factor in predicting outcome. This study explored the potential link between immunity and EC, and established and validated prognostic biomarkers that can effectively predict the prognosis of EC, HNSC and GC based on six immune genes.

SUBMITTER: Zhu C

PROVIDER: S-EPMC8327216 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Profiling upper gastrointestinal microbiota in esophageal disease

Project description:Esophageal microbiota in metaplasia

| PRJEB48716 | ENA

Gastrointestinal cancers: current biomarkers in esophageal and gastric adenocarcinoma.

Project description:Esophageal and gastric adenocarcinomas are frequently diagnosed at an advanced stage and have a dismal prognosis. Even in patients with potentially curative cancer, nearly 50% will develop recurrent disease despite aggressive treatments. A number of biomarkers currently guide treatment decisions for patients with esophageal and gastric adenocarcinoma and include human epidermal growth factor receptor 2 (HER2) amplification, mismatch repair deficiency/microsatellite instability (dMMR/MSI-H) and program death-ligand 1 (PD-L1) expression. This review will focus on the function, testing and FDA-approved targeted therapies for HER2, dMMR/MSI-H and PD-L1. In addition, a number of novel targets in esophageal and gastric cancer are being studied in clinical trials. Neurotrophic-tropomyosin receptor kinase (NTRK), claudin-18 (CLDN18)/Rho GTPase activating protein 26 (ARHGAP26) gene fusion, fibroblast growth factor receptor (FGFR), lymphocyte-activation gene 3 (LAG3) and T cell immunoglobulin and mucin-domain containing-3 (TIM3) will be briefly reviewed. Despite several biomarkers used in the selection of treatment therapies, treatment outcomes remain poor. Future research efforts will focus on the identification of new biomarkers, moving existing biomarkers into earlier lines of therapy, and evaluating new combinations of existing biomarkers and therapies.

| S-EPMC7530320 | biostudies-literature

Gene-Expression Profiling of Mucinous Ovarian Tumors and Comparison with Upper and Lower Gastrointestinal Tumors Identifies Markers Associated with Adverse Outcomes.

Project description:PurposeAdvanced-stage mucinous ovarian carcinoma (MOC) has poor chemotherapy response and prognosis and lacks biomarkers to aid stage I adjuvant treatment. Differentiating primary MOC from gastrointestinal (GI) metastases to the ovary is also challenging due to phenotypic similarities. Clinicopathologic and gene-expression data were analyzed to identify prognostic and diagnostic features.Experimental designDiscovery analyses selected 19 genes with prognostic/diagnostic potential. Validation was performed through the Ovarian Tumor Tissue Analysis consortium and GI cancer biobanks comprising 604 patients with MOC (n = 333), mucinous borderline ovarian tumors (MBOT, n = 151), and upper GI (n = 65) and lower GI tumors (n = 55).ResultsInfiltrative pattern of invasion was associated with decreased overall survival (OS) within 2 years from diagnosis, compared with expansile pattern in stage I MOC [hazard ratio (HR), 2.77; 95% confidence interval (CI), 1.04-7.41, P = 0.042]. Increased expression of THBS2 and TAGLN was associated with shorter OS in MOC patients (HR, 1.25; 95% CI, 1.04-1.51, P = 0.016) and (HR, 1.21; 95% CI, 1.01-1.45, P = 0.043), respectively. ERBB2 (HER2) amplification or high mRNA expression was evident in 64 of 243 (26%) of MOCs, but only 8 of 243 (3%) were also infiltrative (4/39, 10%) or stage III/IV (4/31, 13%).ConclusionsAn infiltrative growth pattern infers poor prognosis within 2 years from diagnosis and may help select stage I patients for adjuvant therapy. High expression of THBS2 and TAGLN in MOC confers an adverse prognosis and is upregulated in the infiltrative subtype, which warrants further investigation. Anti-HER2 therapy should be investigated in a subset of patients. MOC samples clustered with upper GI, yet markers to differentiate these entities remain elusive, suggesting similar underlying biology and shared treatment strategies.

| S-EPMC9751776 | biostudies-literature

Hypoxia imaging in upper gastrointestinal tumors and application to radiation therapy.

Project description:Survival for upper gastrointestinal tumors remains poor, likely in part due to treatment resistance associated with intratumoral hypoxia. In this review, we highlight advances in nuclear medicine imaging that allow for characterization of in vivo tumor hypoxia in esophageal, pancreatic, and liver cancers. Strategies for adaptive radiotherapy in upper gastrointestinal tumors are proposed that would apply information gained through hypoxia imaging to the creation of personalized radiotherapy treatment plans able to overcome hypoxia-induced treatment resistance, minimize treatment-related toxicities, and improve patient outcomes.

| S-EPMC6286953 | biostudies-literature

Project description:Background: Ferroptosis has been identified as a potent predictor of cancer prognosis. Currently, cervical cancer ranks among the most prevalent malignant tumors in women. Enhancing the prognosis for patients experiencing metastasis or recurrence is of critical importance. Consequently, investigating the potential of ferroptosis-related genes (FRGs) as prognostic biomarkers for cervical cancer patients is essential. Methods: In this study, 52 FRGs were obtained from the GSE9750, GSE7410, GSE63514, and FerrDb databases. Six genes possessing prognostic characteristics were identified: JUN, TSC22D3, SLC11A2, DDIT4, DUOX1, and HELLS. The multivariate Cox regression analysis was employed to establish and validate the prognostic model, while simultaneously performing a correlation analysis of the immune microenvironment. Results: The prediction model was validated using TCGA-CESC and GSE44001 datasets. Furthermore, the prognostic model was validated in endometrial cancer and ovarian serous cystadenocarcinoma cases. KM curves revealed significant differences in OS between high-risk and low-risk groups. ROC curves demonstrated the stability and accuracy of the prognostic model established in this study. Concurrently, the research identified a higher proportion of immune cells in patients within the low-risk group. Additionally, the expression of immune checkpoints (TIGIT, CTLA4, BTLA, CD27, and CD28) was elevated in the low-risk group. Ultimately, 4 FRGs in cervical cancer were corroborated through qRT-PCR. Conclusion: The FRGs prognostic model for cervical cancer not only exhibits robust stability and accuracy in predicting the prognosis of cervical cancer patients but also demonstrates considerable prognostic value in other gynecological tumors.

| S-EPMC10175786 | biostudies-literature

Identification of diagnostic upper gastrointestinal cancer tissue type-specific urinary biomarkers.

Project description:Several potential urinary biomarkers exhibiting an association with upper gastrointestinal tumour growth have been previously identified, of which S100A6, S100A9, rabenosyn-5 and programmed cell death 6-interacting protein (PDCD6IP) were further validated and found to be upregulated in malignant tumours. The cancer cohort from our previous study was subclassified to assess whether distinct molecular markers can be identified for each individual cancer type using a similar approach. Urine samples from patients with cancers of the stomach, oesophagus, oesophagogastric junction or pancreas were analysed by surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry using both CM10 and IMAC30 (Cu2+-complexed) chip types and LC-MS/MS-based mass spectrometry after chromatographic enrichment. This was followed by protein identification, pattern matching and validation by western blotting. We found 8 m/z peaks with statistical significance for the four cancer types investigated, of which m/z 2447 and 2577 were identified by pattern matching as fragments of cathepsin-B (CTSB) and cystatin-B (CSTB); both molecules are indicative of pancreatic cancer. Additionally, we observed a potential association of upregulated α-1-antichymotrypsin with pancreatic and gastric cancers, of PDCD6IP, vitelline membrane outer layer protein 1 homolog (VMO1) and triosephosphate isomerase (TPI1) with oesophagogastric junctional cancers, and of complement C4-A, prostatic acid phosphatase, azurocidin and histone-H1 with oesophageal cancer. Furthermore, the potential pancreatic cancer biomarkers CSTB and CTSB were validated independently by western blotting. Therefore, the present study identified two new potential urinary biomarkers that appear to be associated with pancreatic cancer. This may provide a simple, non-invasive screening test for use in the clinical setting.

| S-EPMC6423495 | biostudies-literature

Immune microenvironment profiling of gastrointestinal stromal tumors (GIST) shows gene expression patterns associated to immune checkpoint inhibitors response.

Project description:Few studies were conducted investigating the immunological profiles in gastrointestinal stromal tumors (GIST). Adaptive and innate immune cells are present in the tumor microenvironment, indicating GIST as inflamed tumors. In addition, murine models suggested a potential interaction between immune components and imatinib. In this retrospective study, the GIST immunological profile was investigated through in silico analysis and immunohistochemistry (IHC), exploring the basis for immunotherapy approaches. Gene expression profiles (GEP) from 31 KIT/PDGFRA-mutant GIST were analyzed to evaluate the tumor microenvironment and immunotherapy predictive signatures such as the expanded IFN-γ-induced immune signature (EIIS) and the T-cell-inflamed signature (TIS). GEP and IHC supported the presence of immune infiltrate in GIST, with dominance of CD4+ and CD8+ T cells and M2 macrophages showing a remarkable similarity with melanoma microenvironment. The EIIS genes were expressed in most of GIST samples and positively correlated with PD-L1 abundance (p < .0001). Co-expression was also found between PD-L1 and CD8A (p < .0001) or CD8B (p = .0003). Moreover, the median TIS score for GIST was between the 65th and 70th percentile of the Cancer Genome Atlas dataset, in the same range of tumors responding to anti-PD-1/PD-L1. Analysis of the Gene Expression Omnibus database GIST samples pre- and post-treatment confirmed that imatinib downregulates PD-L1 and IRF1 expression through the inhibition of KIT and PDGFRA, thus contributing to counteract the suppressed adaptive immune response against GIST. The presence of a rich immune infiltrate in GIST along with the presence of TIS and EIIS suggests that GIST may benefit from immunotherapy along with tyrosine kinase inhibitors.

| S-EPMC6685519 | biostudies-literature

The efficacy-associated biomarkers for immune checkpoint inhibitors in gastrointestinal cancer: a literature review.

Project description:Background and objectiveImmune checkpoint inhibitors (ICIs) have been widely applied and studied in the treatment of gastrointestinal (GI) cancers, and have achieved good results. However, in clinical practice, it has been observed that only some patients respond well to ICIs, and some patients may experience various degrees of adverse reactions during the treatment. Timely evaluation of the potential therapeutic effects and adverse reactions of ICIs for patients has important clinical significance. This review aimed to summarize recent progress regarding efficacy-associated biomarkers for ICIs in GI cancer.MethodsThe literature on ICI treatment in GI cancers was searched in the PubMed, Embase, and Cochrane Library databases for publications up to April 2023.Key content and findingsClinical practice and research has gradually revealed some biomarkers related to the treatment of GI cancers with ICIs, which can be roughly divided into three types: biomarkers that predict the effectiveness of ICIs treatment, biomarkers associated with resistance to ICIs, and biomarkers associated with immune related adverse events (irAEs). This review article provides a literature review on biomarkers related to the efficacy of ICIs in the treatment of GI cancers.ConclusionsAccording to existing clinical research results, there are multiple biomarkers that can be used for predicting and monitoring the efficacy and risk of adverse events of ICIs in the treatment of digestive system malignant tumors.

| S-EPMC10932650 | biostudies-literature

Gene expression profiling of mucinous ovarian tumors and comparison with upper and lower gastrointestinal tumors

Project description:Advanced stage MOC have poor chemotherapy response and prognosis and biomarkers to help with Stage I adjuvant treatment are lacking. In addition, differentiating primary mucinous ovarian carcinoma (MOC) from gastrointestinal (GI) metastases to the ovary is challenging due to phenotypic similarities. Clinicopathological and gene expression data were analysed to identify prognostic and diagnostic features.

2022-08-26 | GSE203611 | GEO

GWAS follow-up study of esophageal squamous cell carcinoma identifies potential genetic loci associated with family history of upper gastrointestinal cancer.

Project description:Based on our initial genome-wide association study (GWAS) on esophageal squamous cell carcinoma (ESCC) in Han Chinese, we conducted a follow-up study to examine the single nucleotide polymorphisms (SNPs) associated with family history (FH) of upper gastrointestinal cancer (UGI) cancer in cases with ESCC. We evaluated the association between SNPs and FH of UGI cancer among ESCC cases in a stage-1 case-only analysis of the National Cancer Institute (NCI, 541 cases with FH and 1399 without FH) and Henan GWAS (493 cases with FH and 869 without FH) data (discovery phase). The top SNPs (or their surrogates) from discovery were advanced to a stage-2 evaluation in additional Henan subjects (2801 cases with FH and 3136 without FH, replication phase). A total of 19 SNPs were associated with FH of UGI cancer in ESCC cases with P?<?10-5 in the stage-1 meta-analysis of NCI and Henan GWAS data. In stage-2, the association for rs79747906 (located at 18p11.31, P?=?5.79?×?10-6 in discovery) was replicated (P?=?0.006), with a pooled-OR of 1.59 (95%CI: 1.11-2.28). We identified potential genetic variants associated with FH of UGI cancer. Our findings may provide important insights into new low-penetrance susceptibility regions involved in the susceptibility of families with multiple UGI cancer cases.

| S-EPMC5498542 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data