Tumor Cancer Genome Atlas Research Articles

Abstract PO-021: TCGA differentially expressed genes between Caucasians and African Americans at variable analytic stringencies

Abstract Introduction: In interrogating large dimensional datasets, many methods have evolved to correctly identify differentially expressed genes at acceptable false discovery rates. We examined the NCI TCGA head and neck gene set with open source programs (at varying stringencie)s between Caucasian and African-Americans (AA) specimens and catalogued differentially expressed genes. Procedures: cBioPortal and UALCAN were utilized to investigate the Tumor Cancer Genome Atlas (TCGA). We compared AA and Caucasian samples for this analysis. Tumor samples for both groups were controlled for tumor stage (T3 and T4), HPV status (-), and age (40-75). Three different methods were utilized to identify potential genes at acceptable high stringency (defined as probability <2 false positives out of 20,000 genes being tested). First, those genes differentially expressed in AA vs Cau populations, as well as, those significantly differentially expressed in lymph node-positive (N+) vs negative (N0) patients were investigated (p<0.01 in both groups). Secondly, Benjamin-Hochberg (BH) analysis analyzed differentially expressed genes between just AA vs Cau groups (q<0.05). Finally, genes between these two groups possessing a p-value <0.0001, but not included in the BH analysis, were included in a third analysis. Results: Benjamin-Hochberg analysis identified four differentially expressed genes (q<0.05). There were 15 additional genes in this groups with p-values <0.0001. Analysis of genes differentially expressed in both AA N+ vs N0, as well as, those differentially expressed in AA vs Cau (p<0.01) identified one additional gene (PPIL2). Expression of this gene was found to be increased in a step-wise fashion with increased tumor grade (p<0.05). Three of the four genes identified by BH analysis were differentially expressed in HNSCC tumors vs control (LIN52, CTNNA2, PRMT6). Increased expression of LIN52 and PRMT6 were grade dependent (p<0.05). In the third analysis (AA vs Cau differentially expressed genes with p<0.0001), seven of 15 genes were overexpressed in tumor vs normal tissue; five of these were associated with increased tumor grade (p<0.05). All genes associated with increased tumor grade were demonstrated increased expression in AA groups vs Cau. Conclusions: We conclude open source TCGA programs could be readily employed to identify differentially expressed genes between both groups at varying stringencies. The high-stringency Benjamin-Hochberg analytical tool identified 4 genes differentially expressed between AA vs Cau T3/T4 HPV- tumors. Genes differentially expressed in both AA N+ vs N0, as well as, AA vs Cau revealed one potential gene of interest. When applying a p-value of < 0.0001, 15 additional genes were identified. The low overall number of AA specimens in this dataset was limiting and future biobanks should favor enriched collection of all stages of head and neck cancer in under represented groups. Citation Format: Seth Buryska, Jacob Tupa, Frank Ondrey. TCGA differentially expressed genes between Caucasians and African Americans at variable analytic stringencies [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-021.

Bioinformatics-based analysis of programmed cell death pathway and key prognostic genes in gastric cancer: Implications for the development of therapeutics.

Gastric cancer (GC) represents a major global health burden as a result of its high incidence and poor prognosis. The present study examined the role of the programmed cell death (PCD) pathway and identified key genes influencing the prognosis of patients with GC. Bioinformatics analysis, machine learning techniques and survival analysis were systematically integrated to identify core prognostic genes from the The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset. A prognostic model was then developed to stratify patients into high-risk and low-risk groups, and further validated in the GSE84437 dataset. The model also demonstrated clinical relevance with tumor staging and histopathology. Immune infiltration analysis and the potential benefits of immunotherapy for each risk group were assessed. Finally, subgroup analysis was performed based on the expression of three key prognostic genes. Three core prognostic genes (CAV1, MMP9 and MAGEA3) were identified. The prognostic model could effectively differentiate patients into high-risk and low-risk groups, leading to significantly distinct survival outcomes. Increased immune cell infiltration was observed in the high-risk group, and better potential for immunotherapy outcomes was observed in the low-risk group. Pathways related to cancer progression, such as epithelial-mesenchymal transition and tumor necrosis factor-α signaling via nuclear factor-kappa B, were enriched in the high-risk group. By contrast, the low-risk group showed a number of pathways associated with maintenance of cell functionality and immune responses. The two groups differed in gene mutation patterns and drug sensitivities. Subgroup analysis based on the expression of the three key genes revealed two distinct clusters with distinct survival outcomes, tumor immune microenvironment characteristics and pathway enrichment. The present study offers novel insights into the significance of PCD pathways and identifies key genes associated with the prognosis of patients with GC. This robust prognostic model, along with the delineation of distinct risk groups and molecular subtypes, provides valuable tools for risk stratification, treatment selection and personalized therapeutic interventions for GC.

Integrated single-cell RNA sequencing analysis reveals a mesenchymal stem cell-associated signature for estimating prognosis and drug sensitivity in gastric cancer.

Mesenchymal stem cells (MSCs) play an important role in regulating all stages of the immune response, angiogenesis, and transformation of matrix components in the tumor microenvironment. The aim of this study was to identify the prognostic value of MSC-related signatures in patients with gastric cancer (GC). MSC marker genes were identified by analyzing single-cell RNA sequencing (scRNA-seq) data for GC from the Gene Expression Omnibus (GEO) database. Using bulk sequencing data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD), as a training cohort, and data from GEO, as a validation cohort, we developed a risk model consisting of MSC prognostic signature genes, and classified GC patients into high- and low-MSC risk subgroups. Multifactorial Cox regression was used to evaluate whether MSC prognostic signature was an independent prognostic factor. An MSC nomogram was constructed combining clinical information and risk grouping. Subsequently, we evaluated the effect of MSC prognostic signature on immune cell infiltration, antitumor drugs and immune checkpoints and verified the expression of MSC prognostic signature by in vitro cellular assays. In this study, 174 MSC marker genes were identified by analyzing scRNA-seq data. We identified seven genes (POSTN, PLOD2, ITGAV, MMP11, SDC2, MARCKS, ANXA5) to construct MSC prognostic signature. MSC prognostic signature was an independent risk factor in the TCGA and GEO cohorts. GC patients in the high-MSC risk group had worse prognoses. In addition, the MSC nomogram has a high clinical application value. Notably, the MSC signature can induce the development of a poor immune microenvironment. GC patients in the high MSC-risk group were more sensitive to anticancer drugs and tended to have higher levels of immune checkpoint markers. In qRT-PCR assays, the MSC signature was more highly expressed in GC cell lines. The MSC marker gene-based risk signature developed in this study can not only be used to predict the prognosis of GC patients, but also has the potential to reflect the efficacy of antitumor therapies.

Bioinformatics Analysis Reveals the Vital Role of AKR1B1 in Immune Infiltration and Clinical Outcomes of Gastric Cancer.

Infiltrated immune cells are an important constitute of tumor microenvironment, which exert complex effects on gastric cancer (GC) pathogenesis and progression. By using weighted gene co-expression network analysis, integrating the data from The Cancer Genome Atlas-stomach adenocarcinoma and GSE62254, we identify Aldo-Keto Reductase Family 1 Member B (AKR1B1) as a hub gene for immune regulation in GC. Notably, AKR1B1 is associated with higher immune infiltration and worse histologic grade of GC. In addition, AKR1B1 is an independent factor for predicting the survival rate of GC patients. In vitro experiments further demonstrated that AKR1B1-overexpressed THP-1-derived macrophages promoted the proliferation and migration of GC cells. Taken together, AKR1B1 plays an important role in GC progression by regulating immune microenvironment, which could be a biomarker for predicting GC prognosis as well as a potential therapeutic target for GC treatment.

Fibroblast Growth Factor 11 Enables Tumor Cell Immune Escape by Promoting T Cell Exhaustion and Predicts Poor Prognosis in Patients with Lung Adenocarcinoma.

Fibroblast growth factor 11 (FGF11) accelerates tumor proliferation in a variety of cancer types. This study aimed to examine the link between FGF11 and the prognosis of lung adenocarcinoma. FGF11 was searched in the Tumor Cancer Genome Atlas (TCGA) and ImmProt databases. The link between FGF11 and lung cancer clinical data was investigated using TCGA and Kaplan-Meier (KM)-plotter databases, and we developed a prediction model. Putative mechanisms of action were investigated using Gene Ontology (GO) and KEGG enrichment analyses. The GeneMANIA and STRING databases were used to search for genes that interact with FGF11, and the Tumor Immune Estimation Resource (TIMER) database was used to discover connections between FGF11 and immune cells, as well as any correlations with immune-related genes. We found that FGF11 expression was higher in the lung adenocarcinoma tissue than in the paracancerous tissue, and patients with high FGF11 expression had a lower overall survival, progression-free survival, and disease specific survival rate than those with low FGF11 expression. The expression of FGF11 was inversely linked to six types of infiltrating immune cells in the TIMER database and was associated with EGFR, VEGFA, BRAF, and MET expressions. The FGF11 gene is negatively correlated with the expression of most immune cells, mainly with various functional T cells including Th1, Th1-like, Treg, and Resting Treg characterization genes. These results indicate that FGF11 has the potential to be a new lung adenocarcinoma biomarker. It increases tumor cell immune escape by boosting T cell exhaustion in the tumor microenvironment, contributing to the poor prognosis of the patients with lung adenocarcinoma. These results provide incentive to further research FGF11 as a possible biomarker and drug target for patients with lung adenocarcinoma.

The characteristics and clinical relevance of tumor fusion burden in non-EBV (+) gastric cancer with MSS

BackgroundNext-generation sequencing (NGS) is maturely applied for gene fusion detection. Although tumor fusion burden (TFB) has been identified as an immune marker for cancer, the relationship between these fusions and the immunogenicity and molecular characteristics of gastric cancer (GC) patients remains unclear. GCs have different clinical significance depending on their subtypes, and thus, this study aimed to investigate the characteristics and clinical relevance of TFB in non-Epstein–Barr-virus-positive (EBV+) GC with microsatellite stability (MSS).MethodsA total of 319 GC patients from The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD) and a cohort of 45-case from ENA (PRJEB25780) were included. The cohort characteristics and distribution of TFB among the patients were analyzed. Additionally, the correlations of TFB with mutation characteristics, pathway differences, relative abundance of immune cells, and prognosis were examined in the TCGA-STAD cohort of MSS and non-EBV (+) patients.ResultsWe observed that in the MSS and non-EBV (+) cohort, the TFB-low group exhibited significantly lower gene mutation frequency, gene copy number, loss of heterozygosity score, and tumor mutation burden than in the TFB-high group. Additionally, the TFB-low group exhibited a higher abundance of immune cells. Furthermore, the immune gene signatures were significantly upregulated in the TFB-low group, 2-year disease-specific survival was markedly increased in the TFB-low group compared with to the TFB-high group. The rates of TFB-low cases were significantly higher TFB-than high cases in durable clinical benefit (DCB) and response groups with pembrolizumab treatment. Low TFB may serve as a predictor of GC prognosis, and the TFB-low group exhibits higher immunogenicity.ConclusionIn conclusion, this study reveals that the TFB-based classification of GC patient may be instructive for individualized immunotherapy regimens.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs.

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study. Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score. Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways. Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

Development and validation of an inflammatory response-related gene and clinical factor-based signature for predicting prognosis in gastric cancer.

Gastric cancer (GC) is an aggressive disease that requires prognostic tools to aid in clinical management. The prognostic power of clinical features is unsatisfactory, which might be improved by combining mRNA-based signatures. Inflammatory response is widely associated with cancer development and treatment response. It is worth exploring the prognostic performance of inflammatory-related genes plus clinical factors in GC. An 11-gene signature was trained using the least absolute shrinkage and selection operator (LASSO) based on the messenger RNA (mRNA) and overall survival (OS) data of The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort. A nomogram was established using the signature and clinical factors with a significant linkage with OS and was validated in 3 independent cohorts (GSE15419, GSE13861, and GSE66229) via calculating the area under the receiver operator characteristic curve (AUC). The association between the signature and immunotherapy efficacy was explored in the ERP107734 cohort. A high risk score was associated with shorter OS in both the training and the validation sets (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.691, 0.644, and 0.707; GSE15459: 0.602, 0.602, and 0.650; GSE13861: 0.648, 0.611, and 0.647; GSE66229: 0.661, 0.630, and 0.610). Its prognostic power was improved by combining clinical factors including age, sex, and tumor stage (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.759, 0.706, and 0.742; GSE15459: 0.773, 0.786, and 0.803; GSE13861: 0.749, 0.881, and 0.795; GSE66229: 0.773, 0.735, and 0.722). Moreover, a low-risk score was associated with a favorable response to pembrolizumab monotherapy in the advanced setting (AUC =0.755, P=0.010). In GCs, the inflammatory response-related gene-based signature was related to immunotherapy efficacy, and its risk score plus clinical features yielded robust prognostic power. With prospective validation, this model may improve the management of GC by enabling risk stratification and the prediction of response to immunotherapy.

Integrative analysis of DNA methylation and gene expression through machine learning identifies stomach cancer diagnostic and prognostic biomarkers.

DNA methylation is an early event in tumorigenesis. Here, by integrative analysis of DNA methylation and gene expression and utilizing machine learning approaches, we introduced potential diagnostic and prognostic methylation signatures for stomach cancer. Differentially-methylated positions (DMPs) and differentially-expressed genes (DEGs) were identified using The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) data. A total of 256 DMPs consisting of 140 and 116 hyper- and hypomethylated positions were identified between 443 tumour and 27 nontumour STAD samples. Gene expression analysis revealed a total of 2821 DEGs with 1247 upregulated and 1574 downregulated genes. By analysing the impact of cis and trans regulation of methylation on gene expression, a dominant negative correlation between methylation and expression was observed, while for trans regulation, in hypermethylated and hypomethylated genes, there was mainly a negative and positive correlation with gene expression, respectively. To find diagnostic biomarkers, we used 28 hypermethylated probes locating in the promoter of 27 downregulated genes. By implementing a feature selection approach, eight probes were selected and then used to build a support vector machine diagnostic model, which had an area under the curve of 0.99 and 0.97 in the training and validation (GSE30601 with 203 tumour and 94 nontumour samples) cohorts, respectively. Using 412 TCGA-STAD samples with both methylation and clinical data, we also identified four prognostic probes by implementing univariate and multivariate Cox regression analysis. In summary, our study introduced potential diagnostic and prognostic biomarkers for STAD, which demands further validation.

Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer.

Altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs), as genomic sequences with 100% conservation between the genomes of human, mouse, and rat, in the pathophysiology of neoplasms has already been investigated. Nevertheless, the relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, we first used a genome-wide profiling approach to analyze the expression of T-UCRs in GC patients. Then, we constructed a three-component regulatory network and investigated potential diagnostic and prognostic values of the T-UCRs. The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. FeatureCounts was utilized to quantify the number of reads mapped to each T-UCR. Differential expression analysis was then conducted using DESeq2. In the following, interactions between T-UCRs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were combined into a three-component network. Enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The R Survival package was utilized to identify survival-related significantly differentially expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was furthermore experimentally verified. Our results showed that several T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to nontumoral counterparts. The three-component network was constructed which composed of DET-UCRs, miRNAs, and mRNAs nodes. Functional enrichment and PPI network analyses revealed important enriched signaling pathways and gene ontologies such as "pathway in cancer" and regulation of cell proliferation and apoptosis. Five T-UCRs were significantly correlated with the overall survival of GC patients. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression, although not statistically significant, in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets.

Hypercapnic Tissue Gene Expression and Survival in Early-Stage Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer. Hypercapnic tumor microenvironments were previously shown to promote cancer chemoresistance. In this study, we aimed to investigate the impact of tissue hypercapnia on PDAC prognosis. PDAC cancer-cell lines were cultured in normocapnic (5% CO 2 ) and hypercapnic conditions (10% CO 2 ). RNA was extracted, and whole-exome transcriptome was sequenced. Differentially expressed genes were identified and used to construct a "hypercapnic gene set." PDAC transcriptomic patient data from the Tumor Cancer Genome Atlas was used to calculate single-sample gene set enrichment scores based on each patient's tissue expression of the hypercapnic gene set. Tissue hypercapnic scores (HSs) in PDAC patients (TMN stages Ia-IIb) were determined and correlated with clinicopathological parameters and overall survival. A cohort of 135 resected stage I-II PDAC patients were assessed in this study. The average age was 65 ± 11.0 years, and the male:female ratio was 74:61. Median overall survival was 19.5 ± 1.4 months. High HSs were associated with increased tumor stage (p < 0.05) and higher lymph-node ratio (p < 0.05). In active smokers, high HS also correlated with smoking pack-years (p < 0.05). Cox regression analysis revealed high HS to be an independent prognostic factor for overall survival (hazard ratio [HR] 2.66, p = 0.004), along with lymph-node ratio (HR 4.2, p = 0.002) and age at diagnosis (HR 2.63, p = 0.01). The pancreatic tumor microenvironment plays an integral role in tumor aggressiveness, and our previous in vitro data suggest that hypercapnia promotes an aggressive, more resistant phenotype. Herein, we show that in early-stage pancreatic cancer, hypercapnic tissue signatures corresponded with a worse overall survival.

Transcriptomic characterization and construction of M2 macrophage-related prognostic and immunotherapeutic signature in ovarian metastasis of gastric cancer.

Ovarian metastasis (OM) poses a major threat to the outcome of gastric cancer (GC) patients. Recently, immunotherapy emerged as a novel promising therapeutic strategy to treat late-stage GC, whereas its efficacy is influenced by tumor immune microenvironment (TIME). M2 macrophage, a key subset within TIME, plays dual immunosuppressive and pro-tumorigenic roles in cancer progression and is recognized as a potential therapeutic target. However, molecular mechanisms underlying OM remain elusive and the TIME-related prognostic and immunotherapeutic index for these patients is yet to establish. Differential expressed genes (DEGs) between paired normal mucosa, primary GC and OM of patients from Fudan University Shanghai Cancer Center (FUSCC) cohort (n = 6) were identified by transcriptome sequencing, followed by the functional annotation of enriched hallmark pathways of DEGs between them. CIBERSORT was used to profile the relative expression level of 22 immune cell subsets in normal tissues, primary and metastatic tumors, followed by weighted gene coexpression network analysis (WGCNA) uncovering immune cell-correlated gene sets. The intersected genes between DEGs and M2 macrophage-related genes were processed by least absolute shrinkage and selection operator (LASSO) regression analysis to construct a predictive signature, M2GO, which was further validated by training set and test set of The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD), GSE62254 and GSE84437 cohorts. GC patients were divided into M2GO-high and -low subgroup according to the optimal cutoff value of the M2GO score. Furthermore, the clinical, molecular and immune features between M2GO-high and -low subgroups were analyzed. Clinical cohorts of immunotherapy were used to validate the predictive value of M2GO in regard to immunotherapy effectiveness. Transcriptomic sequencing and follow-up analyses of triple-matched normal tissues, primary and ovarian metastatic tumors identified distinctive sets of DEGs and enriched immune-, cancer- and metastasis-related pathways between them. Of note, M2 macrophage, a major immunosuppressive and pro-tumorigenic component within TIME, was significantly up-regulated in OMs. WGCNA and LASSO regression were applied to establish a novel OM- and M2 macrophage-related predictive signature, M2GO, based on M2 macrophage-related prognostic genes including GJA1, MAGED1 and SERPINE1. M2GO served as an independent prognostic factor of GC patients. Comprehensive molecular and immune characterization of M2GO-based subgroups uncovered their distinctive features in terms of enriched functional pathways, tumor mutation burden, key immune checkpoints, major regulators of natural immune cGAS-STING pathway, infiltrated subsets of immune cells and tumor immune exclusion/dysfunction (TIDE) score. Notably, the M2GO score was significantly lower in responsive group than non-responsive group (P < 0.05) in clinical cohort of metastatic GC patients undergoing immunotherapy. Transcriptomic characterization of paired normal mucosae, primary and ovarian metastatic tumors revealed their unique molecular and immune features. Follow-up analyses established a novel OM- and M2 macrophage-related signature, M2GO, which served as a promising prognostic and immunotherapeutic biomarker to distinguish the clinical outcome, molecular and immune features of GC patients and predict their differential responses to immunotherapy.

Therapeutic Targets and Prognostic Biomarkers Among CXC Chemokines in Pancreatic Ductal Adenocarcinoma Microenvironment.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by occult onset, rapid progression, and poor prognosis. CXC chemokines play an important role in tumor microenvironment and development. However, the potential mechanistic values of CXC chemokines as clinical biomarkers and therapeutic targets in PDAC have not been fully clarified. The altered expression, interaction network, and clinical data of CXC chemokines in patients with PDAC were analyzed by the data from the Gene Expression Omnibus and the Tumor Cancer Genome Atlas. CXCL5 transcriptional level was significantly elevated in PDAC tissues. A significant correlation was found between the expression of CXC1/3/5/8 and the pathological stage of PDAC patients. The PDAC patients with low transcriptional levels of CXCL5/9/10/11/17 were associated with a significantly better prognosis. The functions of differentially expressed CXC chemokines are primarily related to the chemokine signaling pathway, cytokine-cytokine receptor interaction, and viral protein interaction with cytokine and cytokine receptor. RELA, NFKB1, and SP1 are key transcription factors for CXC chemokines, and the SRC family of tyrosine kinases, mitogen-activated protein kinases, CDK5, PRKCQ, ROCK1, ITK, IKBKE, JAK3, and NTRK2 are CXC chemokine targets. The results indicated that CXC chemokines might serve as therapeutic targets and prognostic biomarkers in PDAC.

Machine learning models demonstrate that clinicopathologic variables are comparable to gene expression prognostic signature in predicting survival in uveal melanoma

PurposeSince molecular assays are not accessible to all uveal melanoma patients, we aim to identify cost-effective prognostic tool in risk stratification using machine learning models based on routine histologic and clinical variables. Experimental designWe identified important prognostic parameters in a discovery cohort of 164 enucleated primary uveal melanomas from 164 patients without prior therapies. We then validated the prognostic prediction of top important parameters identified in the discovery cohort using 80 uveal melanomas from the Tumor Cancer Genome Atlas database with available gene expression prognostic signature (GEPS). The performance of three different survival analysis models (Cox proportional hazards (CPH), random survival forest (RSF), and survival gradient boosting (SGB)) was compared against GEPS using receiver operating curves (ROC). ResultsIn all three selection methods, BAP1 status, nucleoli size, age, mitotic rate per 1 mm2, and ciliary body infiltration were identified as significant overall survival (OS) predictors; and BAP1 status, nucleoli size, largest basal tumor diameter, tumor-infiltrating lymphocyte density, and tumor-associated macrophage density were identified as significant progression-free survival (PFS) predictors. ROC plots for the median survival time point showed that significant parameters in SGB studied model can predict OS better than GEPS. For PFS, SGB model performed similarly to GEPS. The time-dependent area under the curve (AUC) showed SGB model performing better than GEPS in predicting OS and metastatic risk. ConclusionsOur study shows that routine histologic and clinical variables are adequate for patient risk stratification in comparison with not readily accessible GEPS.

Identification of candidate biomarkers associated with gastric cancer prognosis based on an integrated bioinformatics analysis.

BackgroundThis study sought to identify candidate biomarkers associated with gastric cancer (GC) prognosis based on an integrated bioinformatics analysis.MethodsFirst, the GSE54129 and GSE79973 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) identified between the 2 data sets were screened using the limma software package in R, and the intersection DEGs were obtained by a Venn analysis. Subsequently, gene clustering and a functional analysis were performed to explore the roles of the DEGs. The protein-protein interaction (PPI) network of the genes in clusters was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. A survival analysis evaluated the associations between the candidate genes and the overall survival of GC patients. A drug-gene interaction analysis and an external data set analysis were conducted using The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) data set to validate the prognostic genes.ResultsWe extracted 421 intersection DEGs from the 2 GEO data sets. There were 5 gene clusters, and the functional analysis revealed that they were mainly associated with the extracellular matrix-receptor interaction pathway. The PPI interaction analysis identified the top 36 hub genes. The survival analysis revealed that 7 upregulated genes [i.e., platelet-derived growth factor receptor beta (PDGFRB), angiopoietin 2 (ANGPT2), vascular endothelial growth factor C (VEGFC), collagen type IV alpha 2 chain (COL4A2), collagen type IV alpha 1 chain (COL4A1), thrombospondin 1 (THBS1), and fibronectin 1 (FN1)] were associated with the survival prognosis of GC patients. The 20 drug-gene interaction pairs among the 4 genes and 18 drugs were obtained. Finally, TCGA-STAD data set was used to validate the expression levels of COL4A1, PDGFRB, and FN1.ConclusionsWe found that 7 upregulated genes (i.e., PDGFRB, ANGPT2, VEGFC, COL4A2, COL4A1, THBS1, and FN1) were promising markers of prognosis in GC patients.

ITGB1 as a prognostic biomarker correlated with immune suppression in gastric cancer.

Gastric cancer is one of the common malignant tumors with a high incidence and mortality in China. Prognostic biomarkers and potential predictors of the treatment efficacy of gastric cancer urgently need to be identified. Integrin-β (ITGB) is a superfamily of integrins and is involved in cell adhesion, tissue repair, immune response, and tumor metastasis. We analyzed ITGB1 expression in our hospital samples of the gastric cancer cohort. And the public data of The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD), The Asian Cancer Research Group (ACRG)/GSE62254, and GSE15459 data sets were analyzed by using the bioinformatic methods. The relationships between ITGB1 expression and clinicopathological features, patient prognosis, activation of the Wnt/β-catenin signaling pathway, and tumor immunosuppressive factors were also explored. The positive rate of ITGB1 expression in the Fudan University Shanghai Cancer Center gastric cancer tumor tissues was 61.4% (258/420) and correlated with deep invasion (p=0.017), an advanced clinical stage (p=0.011), and a poor prognosis (p < 0.05). The TCGA-STAD/ACRG/GSE15459 cohorts also showed similar results. ITGB1 is one of the upstream molecules of the Wnt/β-catenin signaling pathway and is correlated with tumor immune suppression. In gastric cancer, we found a correlation between ITGB1 expression and Wnt/β-catenin signaling pathway activity. In the TCGA-STAD/ACRG/GSE15459 cohorts, ITGB1 expression was positively associated with immunosuppressive factors and negatively associated with immunoactive factors. Patients with low ITGB1 expression exhibited a significantly high immunotherapy response ratio according to an analysis of tumor immune dysfunction and exclusion (TIDE), which may indicate that ITGB1 is a potential predictor of immunotherapy efficacy. ITGB1 affects the prognosis in gastric cancer patients and plays a core role in immune suppression in gastric cancer.

Abstract 5785: Tumors with higher heterogeneity were associated with poorer survival outcome in early stage lung adenocarcinoma cancer patients with low tumor mutational burden

Abstract Background: Response to immune checkpoint inhibitors is associated with tumor mutational burden (TMB). Pembrolizumab has recently been approved by the FDA for use in patients with high TMB. However low TMB patients are not considered good responders and could benefit from stratification. The predictive value of other biomarkers such as intratumor heterogeneity (ITH) has been studied across a range of cancer indications. The utility of ITH in Lung Adenocarcinoma (LUAD) has not been established. Here we evaluate the relationship between ITH and outcome in early stage low TMB patients and explore differences in immune cell enrichment and predicted driver genes. Stratifying early stage patients may yield an opportunity to improve Recurrence Free Survival (RFS) and lower the risk of progression to late stage disease. Methods: All molecular data was obtained from the Tumor Cancer Genome Atlas via the GDC Data Portal. Clinical data was from the PanCancer Atlas dataset through cBioPortal. We estimated ITH using PyClone, an algorithm that estimates the number of tumor clones. We generated our ITH values in triplicate across 3 separate days, yielding an average PyClone clone number. Clones with &amp;gt;2 mutations were counted. We used p-value minimization to choose 1.4 for the TMB cutoff and 2.6 for the PyClone cutoff. We generated 4 groups: HTHI, HTLI, LTHI, and LTLI, where H - high, L- low, T-TMB, and I-ITH. Immune cell enrichment analysis was obtained from the xCell webtool. Driver gene prediction was performed with OncoDriveClust via the R package maftools. Results: We found a statistically significant difference in RFS between early stage (Stage I and Stage II) LTHI (N = 18) and LTLI (N = 40) patients (16.69 months vs. 33.27 months, p-value = 0.0056). Interestingly the predicted driver genes did not significantly differ between these two groups. One driver (EGFR) and two drivers (EGFR and TP53) genes were predicted for LTHI and LTLI, respectively. Gene set enrichment analysis showed statistically significant differences in key immune subtypes. B-cells were depressed and Th1 cells were elevated in LTHI, both of which are associated with poorer survival outcomes. Conclusion: We found an association between high ITH and poor survival in low TMB early stage LUAD patients. Early stage patients can be stratified using heterogeneity metrics like PyClone. Given the presence of immune subtypes known to associate with poorer outcomes, early stage LTHI patients may benefit from additional adjuvant therapies. Citation Format: Stanislav Fridland, Young Kwang Chae. Tumors with higher heterogeneity were associated with poorer survival outcome in early stage lung adenocarcinoma cancer patients with low tumor mutational burden [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 5785.

Abstract 2517: Characterization of the immune microenvironment of Epstein-Barr virus associated gastric cancer

Abstract Consistent with better clinical outcome, Epstein-Barr Virus associated gastric cancer (EBVaGC) displays a particular expression pattern of immunological genes and is highly infiltrated by effector immune cells. To decipher potential mechanisms linking tumor infiltrating lymphocytes (TILs) with an expression of immune-related genes in EBVaGC we utilized RNAseq gene expression data from The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma (STAD) dataset and performed cellular deconvolution analyses by a mixed approach including two complementary approaches (MIXTURE and xCell). In accordance with previous studies, we observed an increase in CD8+ cells and Interferon activity. Further analysis by gene set variation analysis (GSVA) showed an increased exhausted CD8+ signature accompanied by a higher intratumoral Treg signature. Accordingly, an enrichment in both stimulatory and inhibitory immune checkpoint levels on EBVaGC tumors was observed. We next performed differential expression analysis comparing EBVaGC to other STAD subtypes (CIN, GS, HM-SNV, HM-indel) individually. As a result, from this approach, we generated a list of unique EBVaGC-specific genes that was used to construct EBVaGC-specific cell-cell interaction networks. To infer cell-cell interactions underlying the specific tumor immune microenvironment, we implemented the NicheNetR algorithm by including only differentially expressed ligands and targets from the list of EBVaGC genes obtaining a ligand-interaction matrix from which we constructed a EBVaGC-like gene signature. Functional annotation of this ligand-target network showed ligands and targets principally involved in chemotaxis, T-cell apoptosis, and cellular responses to bacterial infection. This signature correlated with better survival not only in EBVaGC but also in other TCGA cohorts such as colon adenocarcinoma and kidney renal clear cell carcinoma. Our findings identified an increase in exhausted CD8+ signature, high intratumoral Treg signature and enrichment in inhibitory immune checkpoint transcripts. We herein propose a new EBVaGC-like gene expression signature that correlates with better survival in multiple cancer types. In-vitro and in-vivo assays will be required for functional validation of these findings as well as clinical trials to confirm the role of our novel EBVaGC-like gene signature in a precision oncology setting. Grant Support: ANID/FONDECYT/POSTDOCTORADO/3201028, CONICYT-FONDAP 15130011, FONDECYT 1191928 &amp; 1180241, IMII P09/016-F, CONICET and Universidad Católica de Córdoba 80020180100029CC, grant 33620180100993CB from the Universidad Nacional de Córdoba. Citation Format: Keila E. Torres, Charlotte N. Hill, Dario Rocha, Elmer Fernández, Ignacio A. Wichmann, Gareth I. Owen, Alejandro H. Corvalan. Characterization of the immune microenvironment of Epstein-Barr virus associated gastric cancer [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 2517.

Bioinformatic analysis of cancer-associated fibroblast related gene signature as a predictive model in clinical outcomes and immune characteristics of gastric cancer.

BackgroundGastric cancer (GC) has a high incidence and high mortality rate among Asian countries, and distinguishing predictive prognosis biomarkers for GC are essential. Cancer-associated fibroblasts (CAFs) play a significant role in the progression, immune evasion, and therapeutic resistance of GC. Therefore, CAF-associated genes might have huge potential as prognostic biomarkers for predicting tumor progression and survival rate in GC pateints.MethodsA sum of 1,134 GC patients from the The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), GSE62254, and GSE84437 datasets as well as GC cohorts from Xijing hospital were included. Firstly, we performed univariate Cox regression analysis to identify CAF-associated prognostic genes. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to develop a CAF gene signature (CAFGS) in the TCGA-STAD training cohort. CAFGS’s predictive performance was examined in both the training and validation cohorts, and the relationship between CAFGS and the tumor microenvironment (TME) was investigated by ssGSEA, CIBERSORT, TIMER, and ESTIMATE. Finally, a nomogram of CAFGS was established.ResultsTen CAF-associated genes (ANGPTL4, CPNE8, CST2, HTR1F, IL1RAP, NR1D1, NTAN1, OLFML2B, TMEM259, and VTN) were identified to develop CAFGS. A high CAFGS score represented a worse outcome for GC patients in four cohorts, and a strong correlation was found between CAFGS and the infiltration of immune cells. We showed that CAFs contribute to immune evasion and unfavorable prognoses of GC patients by promoting the formation of an immunosuppressive microenvironment, and a high level of CAF infiltration may attenuate the efficacy of immunotherapy. The nomogram based on CAFGS showed reasonable predictive ability and may deliver great clinical net benefits.ConclusionsWe established a CAFGS model with 10 CAF-associated genes that had a great predictive value for GC prognosis and survival rate evaluation. This study could provide a novel insight for investigating the role of CAFs in GC.

Melanopsin (Opn4) is an oncogene in cutaneous melanoma

The search for new therapeutical targets for cutaneous melanoma and other cancers is an ongoing task. We expanded this knowledge by evaluating whether opsins, light- and thermo-sensing proteins, could display tumor-modulatory effects on melanoma cancer. Using different experimental approaches, we show that melanoma cell proliferation is slower in the absence of Opn4, compared to Opn4WT due to an impaired cell cycle progression and reduced melanocyte inducing transcription factor (Mitf) expression. In vivo tumor progression of Opn4KO cells is remarkably reduced due to slower proliferation, and higher immune system response in Opn4KO tumors. Using pharmacological assays, we demonstrate that guanylyl cyclase activity is impaired in Opn4KO cells. Evaluation of Tumor Cancer Genome Atlas (TCGA) database confirms our experimental data as reduced MITF and OPN4 expression in human melanoma correlates with slower cell cycle progression and presence of immune cells in the tumor microenvironment (TME). Proteomic analyses of tumor bulk show that the reduced growth of Opn4KO tumors is associated with reduced Mitf signaling, higher translation of G2/M proteins, and impaired guanylyl cyclase activity. Conversely, in Opn4WT tumors increased small GTPase and an immune-suppressive TME are found. Such evidence points to OPN4 as an oncogene in melanoma, which could be pharmacologically targeted.