Signature Database Research Articles

Abstract 5387: Discovery of plasma protein biomarkers associated with overall survival in R/R DLBCL patients treated with loncastuximab tesirine

Abstract Plasma proteomics is a non-invasive source of potential biomarkers associated with cancer treatment outcomes, including disease progression and overall survival (OS). Loncastuximab tesirine (lonca) is an antibody-drug conjugate, composed of a humanized anti-CD19 antibody conjugated to a pyrrolobenzodiazepine dimer cytotoxin, for the treatment of relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL). Here, we investigated the association of plasma protein biomarkers with OS of R/R DLBCL patients prior to treatment with lonca (phase 2 trial, NCT3589469, LOTIS-2). Abundances of 888 plasma proteins, including inflammation, cancer and DNA repair associated markers, were measured for 69 patients with R/R DLBCL from plasma samples collected at baseline. Protein markers predictive of OS were selected using a L1 regularized Cox proportional hazards model (CoxPH) incorporating survival time and censoring status. Protein modules associated with OS were discovered by performing principal component analysis (PCA) on scaled protein abundances. Gene Set Enrichment Analysis (GSEA) was performed using Molecular Signatures Database (MSigDB) sets. We initially applied a regularized COX proportional hazards model to our proteomics dataset to identify proteins associated with OS, which revealed 5 protein markers (associated with decreased survival = REG3A, LAP3; associated with increased survival = NPTXR, C1QTNF1, FLT3) which stratified patients into distinct survival groups (p = 2.3e-4). As a complement to this approach and to extend our biological understanding of this cohort, we then applied PCA to the proteomics data to reveal underlying protein modules associated with survival. Our analysis identified principal component 3 (PC3) to have the strongest association to OS (rho= -0.63, FDR = 8.7e-07); with its top loadings consisting of known markers of cancer progression (top 3 proteins: SIGLEC10, IL6, TNFRSF6B). GSEA on PC3 loadings identified 68 significantly enriched gene sets (FDR < 5%). Gene sets reflective of changes in immune activation, increased cytokine activity and interleukin mediated activation (IL6, IL10) were negatively associated with OS, including activation of the following Hallmark pathways: IL6/JAK/STAT3 signaling and inflammatory response pathways.Overall, our results identify proteomics markers associated with DLBCL survival upon treatment with lonca, highlighting the potential of plasma proteins as a source of relevant biomarkers pending additional validation. [FV and VC contributed equally to this work] Citation Format: Francesco Vallania, Victoria Cheung, Anupriya Tripathi, Maggie Louie, Thomas Snyder, Jimmy Lin, Karin Havenith, Yajuan Qin, Serafino Pantano, Jens Wuerthner, Patrick H. van Berkel. Discovery of plasma protein biomarkers associated with overall survival in R/R DLBCL patients treated with loncastuximab tesirine. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5387.

Abstract 1399: Genomic profiling of gastric cancer with peritoneal metastasis identifies molecular subtypes and a potential molecular-guided therapeutic strategy for intractable gastric cancer

Abstract Patients with peritoneal metastasis frequently identified in advanced gastric cancer (GC) have a poor prognosis with a median survival of 3-6 months. Because of innate resistance to treatment, these patients have limited therapeutic efficacy and contribute to high mortality. Therefore, it is necessary to understand the molecular characteristics of peritoneal metastasis and search for applicable drugs. This study aims to understand the biological characteristics of peritoneal metastasis in GC through integrative genomic analysis.A total of 66 GC cell lines were used, of which 11 were derived from the stomach, 49 from peritoneal and pleural fluids, and 6 from other origins. Targeted sequencing and RNA sequencing data were obtained from the Songdang Institute for Cancer Research (SICR) database. Cell lines were clustered into EMT and Non-EMT groups according to the expression profiles of 200 genes belonging to the EMT gene set (Nature Cancer, 2021, 2.9: 962-977.). We used gene sets from the Molecular Signatures Database v.7.0.Hierarchical clustering separated the 49 cell lines derived from peritoneal and pleural fluids into two distinct clusters (EMT group, n=20 and Non-EMT group, n=29). Cell lines in the EMT group were classified as GS (65%, 13/20) and CIN (35%, 7/20), whereas cell lines in the Non-EMT group were classified as EBV-positive (3.4%, 1/29), MSI-H (6.9%, 2/29), GS (37.9%, 11/29), and CIN (51.7%, 15/29). The gene set enrichment analysis (GSEA) revealed that gene sets (HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, CORDENONSI_YAP_CONSERVED_SIGNATURE, and FRIDMAN_SENESCENCE_UP) shows statistically significant differences between the two compared groups. Gene expression levels were significantly different between the EMT and Non-EMT group. In detail, YAP/TEAD pathway related genes (TEAD1, TEAD3, CYR61, DKK1, SLC7A5, SLC2A3) and TGF-β pathway related genes (SMAD3, TGFβ1, TGFβ2, BMP1, INHBA) showed a higher expression level in the EMT group compared to the Non-EMT group (p<0.05). Interestingly, GSEA between the EMT and Non-EMT groups of the GS subtype revealed that gene set CORDENONSI YAP CONSERVED SIGNATURE was enriched in the EMT group. Gene set FOROUTAN_TGFB_EMT_UP, on the other hand, was enriched in the EMT group of the CIN subtype (p<0.05).In conclusion, we identified differences in gene expression levels according to molecular subtypes in GC cell lines with peritoneal metastasis. It can provide useful information to gain new insight into the mechanism of peritoneal metastasis and improve successful treatment options. Furthermore, our results suggest that YAP/TEAD may be considered a potential therapeutic target for GC with peritoneal metastasis. Citation Format: Juin Park, Woo Sun Kwon, Jingmin Che, Tae Soo Kim, Sang Woo Cho, Eun Seo Kim, Hyun Cheol Chung, Sun Young Rha. Genomic profiling of gastric cancer with peritoneal metastasis identifies molecular subtypes and a potential molecular-guided therapeutic strategy for intractable gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1399.

Abstract 4281: Data driven refinement of gene signatures for enrichment analysis and cell state characterization

Abstract The use of gene expression data has been crucial to the functional characterization of changes in molecular pathway activity and for identifying targets for novel treatments. However, the interpretation of this data is complicated by its high dimensionality and the difficulty of identifying biological signals within a list of differentially expressed genes. Gene Set Enrichment Analysis (GSEA) is a standard method for identifying pathway enrichment in gene expression data by testing whether a set of genes whose expression would indicate the activity of a specific process or phenotype are coordinately up- or downregulated more than would be expected by chance. As GSEA relies on high quality gene sets with coordinately regulated member genes, we maintain the Molecular Signatures Database (MSigDB) which contains 9 collections of curated gene sets representing different biological pathways and processes. Over time, we have observed that some of the MSigDB gene sets, especially those that are manually curated or defined in a very specific biological context, may not provide a sensitive and specific enough co-regulation signature. In response, we have created a data-driven, matrix-factorization-based refinement method to build more sensitive and specific gene sets. This method incorporates large-scale datasets from multiple sources such as the Cancer Dependency Map as well as curated protein-protein interaction networks. We will present the initial results of this refinement method and our ongoing work which will yield a new collection of refined gene sets that will be made freely available in MSigDB for use with GSEA and many other applications. Citation Format: Alexander T. Wenzel, Pablo Tamayo, Jill P. Mesirov. Data driven refinement of gene signatures for enrichment analysis and cell state characterization. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4281.

Abstract 6568: Extended support for model organisms: The next frontier for the molecular signatures database

Abstract In 2005, we introduced the Gene Set Enrichment Analysis (GSEA) method to enable the identification and estimation of significance of activated biological pathways and processes in molecular data. Serving a community of over 300,000 registered users, and contributing scientific analysis to more than 30,000 publications, GSEA has become ubiquitous in gene expression analysis and particularly in cancer research. The power of GSEA relies, in a large part, on the Molecular Signatures Database (MSigDB) that provides tens of thousands of expertly curated and annotated gene sets representing cellular processes, canonical pathways, response signatures, etc., derived from prior studies, public data, and curated pathway databases. Here we describe an important recent addition to the GSEA/MSigDB resource. Historically, the GSEA-MSigDB resource has focused specifically on human data, offering guidance and support for analysis of model organism data through limited provision of orthology mapping files. In recognition of the importance of the mouse as a model organism for cancer research, we recently significantly expanded our support for mouse data in two ways. (1) We introduced Mouse MSigDB with a total of about 15,000 sets that are provided in the native mouse gene symbol space and are derived from published mouse datasets and mouse-focused resources. This allows GSEA analysis of mouse datasets without the need for orthology conversion. (2) For investigators who wish to use MSigDB’s human gene sets to analyze mouse data, or vice versa, we also implemented a new procedure for orthology mapping gene identifiers in an input dataset to match the identifiers of the gene sets. The new mapping considers the particular requirements of gene set enrichment analysis, and incorporates high confidence gene ortholog data from the Alliance of Genome Resources and ortholog-match refinement utilizing Ensembl’s ortholog datasets. Together, these new additions offer a substantial step forward for making mouse a first-class citizen of the GSEA-MSigDB ecosystem. The new Mouse MSigDB, and the full GSEA-MSigDB resource, is available at gsea-msigdb.org. Citation Format: Anthony S. Castanza, Jill M. Recla, David Eby, Alexander T. Wenzel, Helga Thorvaldsdottir, Carol J. Bult, Jill P. Mesirov. Extended support for model organisms: The next frontier for the molecular signatures database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6568.

Abstract 1904: Somatic copy number alterations and gene expression signatures are associated with aggressive tumor biology in African Americans and European Americans with renal cell carcinoma

Abstract Background: Renal cell carcinoma (RCC) is the deadliest of all urologic cancers in the US. RCC survival is highly dependent on histology. Survival rates range between 55-60% for clear cell RCC (ccRCC) and 80-90% for papillary RCC (pRCC) patients. African Americans (AAs) have lower survival rates when compared to their European American (EA) counterparts for both histologies (ccRCC 67.1 vs 72.6, pRCC 80.5 vs 87.5). RCC survival disparities may be accredited to population differences in tumor biology, such as somatic copy number alteration (SCNA) profiles of tumor suppressor and oncogenes, and differential gene expression signatures with associated cellular pathway dysregulation. SCNAs are the most common structural change in the human genome and ccRCC and pRCC-specific SCNAs have been associated with clinical outcomes. ClearCode34, a 34 gene expression signature, was developed to classify ccRCC tumors into two distinct ccRCC subtypes (ccA and ccB). The ccB subtype is associated with worse survival and features like increased tumor size, grade, and metastasis rates. We hypothesize there are more aggressive tumor biology phenotypes in AA RCC patients compared to their European counterparts. Methods: Comparative genome-wide, chromosomal, and gene SCNA analyses were performed in AA and EA ccRCC and pRCC patients in TCGA using Partek Genomics Suite 7 (1-way ANOVA, P<0.05). Prediction analysis of microarray in R was used to classify ccRCC patients into ccA and ccB subtypes by race based on the ClearCode34 gene expression signature (t-test, P<0.05). Differentially expressed genes (DEGs) between AA and EA patients were identified using Partek Genomics Suite 7 (1-way ANOVA, P<0.05). Gene Set Enrichment Analysis (GSEA) was performed using a pre-ranked gene list (Fold changes >2 or <-2, P<0.01) and two Molecular Signatures Database collections (C6 oncogenic signatures, C7 immunologic signatures). Results: AA-enriched SCNVs were identified on chromosomes 1, 3, 6, 7, and 12 in ccRCC patients, and chromosomes 1, 4, 6, 7, 8, 10, 16 and 20 in pRCC patients. Six cytobands and genes experienced a SCNV across both histologies (1p21.1, 1p21.3, 6p12.1, 6p12.3, 6p21.1, 6q12), BMP5 (on 6p12.1), CD2AP (on 6p12.3), ENPP4 (on 6p21.1), and SLC44A3-AS1 (on 1p21.3). AAs and EAs had significantly different frequencies of ccA (AA 33.9% vs EA 52.2%) and ccB subtypes (AA 66.1% vs EA 47.8%). Unique DEGs were found between AA and EA ccA and ccB patients (131 ccA only, 165 ccB only), which corresponded with up- and down-regulation in kidney cancer with oncogenic KRAS (C6) and various immunologic pathways (C7). Conclusions: Population-specific SCNAs, ccB subtypes, and pathways may help explain differences in RCC tumor biology amongst AA and EA patients. Future Directions: Replicate these findings in the Cooperative Human Tissue Network/Geisinger validation cohort. Citation Format: Samantha Greenberg, Alex Bart, Destiny Ortiz Fernandez, Heinric Williams, Khadijah A. Mitchell. Somatic copy number alterations and gene expression signatures are associated with aggressive tumor biology in African Americans and European Americans with renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1904.

Abstract 6082: Assessing difference in the tumor and immune microenvironment of BRCA1/2 mutated versus BRCA wild type high grade serous ovarian cancers

Abstract Ovarian cancer (OvCa) is the deadliest gynecological malignancy in the United States. Surgery and chemotherapy are the primary treatments for OvCa, but 80% of late-stage OvCa patients experience chemo-resistant recurrence, which necessitates the development of new treatment strategies. The genomic diversity within a tumor and the interactions among various cell types within its microenvironment are considered key factors contributing to therapeutic efficacy. Specifically, women who harbor germline BRCA1 or BRCA2 mutations are at increased risk of developing high grade serous ovarian cancer (HGSC). To understand the genomic consequences, including somatic alterations, associated with defective DNA repair, tumor DNA and RNA was isolated from fresh frozen OCT embedded sections from 15 HGSC patients with BRCA1/2 mutation and 10 without any alterations in the BRCA genes (BRCA-wt). Whole exome sequencing (WES) and total RNA-seq was performed to determine the association of homologous recombination (HR) DNA repair gene defects with tumor mutation burden (TMB), neoantigen load (NL), and immunological assessment of tumor microenvironment. TMB was calculated from mutation rates of somatic WES. Germline exome analysis was used to validate inherited BRCA mutations. To identify the somatic mutations, somatic single nucleotide variants and InDels were detected using Strelka. TP53 mutations were most common with several other genes showing only modest mutation frequency. Mutation signatures were also detected with maftools and compared to the COSMIC mutational signature database. BRCA-mutated samples displayed a unique mutational signature associated with defects in HR DNA repair pathway. We also observed heterogeneity in copy number variation profiles amongst cases, as determined by the tool Sequenza. Furthermore, gene set enrichment analysis indicated differential enrichment of P53 pathway, MAPK pathway, and PTEN pathway, in addition to numerous pathways related to immune cell signaling and immune response. RNA-seq data was analyzed with CIBERSORTx and revealed different proportions of 22 immune effector cell types that accompany tumor cells within the HGSC microenvironment. Additional analyses are underway, including measuring differences in NL in BRCA-mutated versus BRCA-wt cases. This work will represent a significant advancement in our fundamental knowledge regarding the complexities of the TME and effective immunological responses in OvCa. Citation Format: Jing Qian, Lynda D. Roman, Seeta Rajpara, Monica Neuman, Varun Khetan, David W. Craig, Joseph Carlson, John D. Carpten. Assessing difference in the tumor and immune microenvironment of BRCA1/2 mutated versus BRCA wild type high grade serous ovarian cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6082.

Abstract 3007: Psychosocial stressors and breast cancer gene expression in the Black Women's Health Study

Abstract Introduction: Chronic sympathetic nervous system (SNS) activation following exposure to psychosocial stressors may influence cancer risk through dysregulation of immune function or adrenergic signaling pathways. Prior research out of the Black Women’s Health Study (BWHS) reported associations between psychosocial stressors and breast cancer risk. We hypothesized that women with greater exposure to psychosocial stressors before their breast cancer diagnosis have higher tumor gene expression of immune-related and adrenergic signaling pathways. Methods: The BWHS is a large, prospective cohort study that has collected health and lifestyle information biennially since 1995. We included data from 417 BWHS breast cancer cases with RNA sequencing data and information on early-life trauma and neighborhood disadvantage. We used the R package DESeq2 to conduct age-adjusted differential gene expression analyses by levels of each stress exposure and described the top differentially expressed pathways using the Molecular Signature Database REACTOME subset of canonical pathways. Targeted analyses of immune-related pathways used CIBERSORT to quantify the proportions of infiltrating immune cells and the R package GSVA for single-sample gene set enrichment analysis (ssGSEA) of genes comprising a T cell exhaustion signature. We also used ssGSEA to evaluate a set of genes involved in adrenergic signaling. Given the variability in gene expression for ER+ versus ER- breast cancers, all analyses were run separately for ER+ (n=299) and ER- (n=118) tumors. Results: Pathways related to nervous system development and the metabolism of RNA were differentially expressed by levels of stress exposures among ER+ and ER- cases, while pathways related to immune function and adrenergic signaling were differentially expressed only among ER- cases, and only when evaluating differential gene expression by levels of neighborhood disadvantage. Targeted analyses of tumor immune infiltration showed significantly higher B cell fractions among ER+ cases without exposure to early trauma compared to those with early trauma. The relative fractions of other immune cells did not differ by stress exposure for ER+ or ER- tumors. Findings from ssGSEA gene set analyses indicated similar expression of genes involved in T cell exhaustion and adrenergic signaling pathways by stress exposure levels for both ER+ and ER- breast cancers. Conclusions: Our findings provide evidence of differential gene expression by levels of stress exposure; however, the differential expression may be driven by multiple pathways and not just the hypothesized immune-related and adrenergic signaling pathways. Additional studies are needed to describe mechanisms by which psychosocial stressors may influence breast cancer risk, both overall and by gene expression profile. Citation Format: Mollie E. Barnard, Xutao Wang, Jessica L. Petrick, W. Evan Johnson, Julie R. Palmer. Psychosocial stressors and breast cancer gene expression in the Black Women's Health Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3007.

A novel pyroptosis-related prognostic signature for lung adenocarcinoma: Identification and multi-angle verification.

Background: Lung adenocarcinoma (LUAD) is an aggressive disease of heterogeneous characteristics with poor prognosis and high mortality. Pyroptosis, a newly uncovered type of programmed cell death with an inflammatory nature, has been determined to hold substantial importance in the progression of tumors. Despite this, the knowledge about pyroptosis-related genes (PRGs) in LUAD is limited. This study aimed to develop and validate a prognostic signature for LUAD based on PRGs. Methods: In this research, gene expression information from The Cancer Genome Atlas (TCGA) served as the training cohort and data from Gene Expression Omnibus (GEO) was utilized as the validation cohort. PRGs list was taken from the Molecular Signatures Database (MSigDB) and previous studies. Univariate Cox regression and Lasso analysis were then conducted to identify prognostic PRGs and develop a LUAD prognostic signature. The Kaplan-Meier method, univariate and multivariate Cox regression models were employed to assess the independent prognostic value and forecasting accuracy of the pyroptosis-related prognostic signature. The correlation between prognostic signature and immune infiltrating was analyzed to examine the role in tumor diagnosis and immunotherapy. Further, RNA-seq as well as quantitative real-time polymerase chain reaction (qRT-PCR) analysis in separate data sets was applied in order to validate the potential biomarkers for LUAD. Results: A novel prognostic signature based on 8 PRGs (BAK1, CHMP2A, CYCS, IL1A, CASP9, NLRC4, NLRP1, and NOD1) was established to predict the survival of LUAD. The prognostic signature proved to be an independent prognostic factor of LUAD with satisfactory sensitivity and specificity in the training and validation sets. High-risk scores subgroups in the prognostic signature were significantly associated with advanced tumor stage, poor prognosis, less immune cell infiltration, and immune function deficiency. RNA sequencing and qRT-PCR analysis confirmed that the expression of CHMP2A and NLRC4 could be used as biomarkers for LUAD. Conclusion: We have successfully developed a prognostic signature consisting of eight PRGs that providing a novel perspective on predicting prognosis, assessing infiltration levels of tumor immune cells, and determining the outcomes of immunotherapy for LUAD.

A Genetic Algorithm Based One Class Support Vector Machine Model for Arabic Skilled Forgery Signature Verification

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER).

Characterization of butyrate-metabolism in colorectal cancer to guide clinical treatment

Colorectal cancer (CRC) is the third most prevalent one in the world among the most common malignant tumors. Numerous studies have shown that butyrate has demonstrated promise as an antitumor agent in a variety of human cancer types. However, butyrate remains understudied in CRC tumorigenesis and progression. In this study, we explored therapeutic strategies to treat CRC by examining the role of butyrate metabolism. First, from the Molecular Signature Database (MSigDB), we identified 348 butyrate metabolism-related genes (BMRGs). Next, we downloaded 473 CRC and 41 standard colorectal tissue samples from The Cancer Genome Atlas (TCGA) database and the transcriptome data of GSE39582 dataset from Gene Expression Omnibus (GEO) database. Then we evaluated the expression patterns of butyrate metabolism-related genes with difference analysis in CRC. Through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, a prognostic model was constructed based on differentially expressed BMRGs. In addition, we discovered an independent prognostic marker for CRC patients. According to the expression levels and coefficients of identified BMRGs, the risk scores of all CRC samples were calculated. Utilizing differentially expressed genes in the high- and low-risk groups, we also constructed a Protein–Protein Interaction (PPI) network to visualize the interactions between proteins. Through the results of PPI network, we screened out differentially expressed target butyrate metabolism-related genes from ten hub genes. Finally, we performed clinical correlation analysis, immune cell infiltration analysis, and mutation analysis for these target genes. One hundred and seventy three differentially expressed butyrate metabolism-related genes were screened out in all the CRC samples. The prognostic model was established with univariate Cox regression and LASSO regression analysis. CRC patients’ overall survival was significantly lower in the high-risk group than in the low-risk group for both training and validation set. Among the ten hub genes identified from the PPI network, four target butyrate metabolism-related genes were identified containing FN1, SERPINE1, THBS2, and COMP, which might provide novel markers or targets for treating CRC patients. Eighteen butyrate metabolism-related genes were used to develop a risk prognostic model that could be helpful for doctors to predict CRC patients’ survival rate. Using this model, it is beneficial to forecast the response of CRC patients to immunotherapy and chemotherapy, thus making it easier to custom tailor cancer chemotherapy and immunotherapy to the individual patient.

A novel zinc metabolism-related gene signature to predict prognosis and immunotherapy response in lung adenocarcinoma.

Zinc is a key mineral element in regulating cell growth, development, and immune system. We constructed the zinc metabolism-related gene signature to predict prognosis and immunotherapy response for lung adenocarcinoma (LUAD). Zinc metabolism-associated gene sets were obtained from Molecular Signature Database. Then, the zinc metabolism-related gene signature (ZMRGS) was constructed and validated. After combining with clinical characteristics, the nomogram for practical application was constructed. The differences in biological pathways, immune molecules, and tumor microenvironment (TME) between the different groups were analyzed. Tumor Immune Dysfunction and Exclusion algorithm (TIDE) and two immunotherapy datasets were used to evaluate the immunotherapy response. The signature was constructed according to six key zinc metabolism-related genes, which can well predict the prognosis of LUAD patients. The nomogram also showed excellent prediction performance. Functional analysis showed that the low-risk group was in the status of immune activation. More importantly, the lower risk score of LUAD patients showed a higher response rate to immunotherapy. The state of zinc metabolism is closely connected to prognosis, tumor microenvironment, and response to immunotherapy. The zinc metabolism-related signature can well evaluate the prognosis and immunotherapy response for LUAD patients.

GPX3 and GSTT1 as biomarkers related to oxidative stress during renal ischemia reperfusion injuries and their relationship with immune infiltration.

Renal ischemia reperfusion injuries (IRIs) are very common in clinical diagnoses and treatments, which are a common cause of impaired renal functions, worsening pathological damage, affecting disease progression and hindering recovery. Renal IRIs are an inflammatory disease mediated by the adaptive and innate immune system. There is a complex interaction between oxidative stress and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with oxidative stress during renal IRIs and their relationship with immune cell infiltration. A differential gene expression analysis was made based on the GSE148420 dataset from the NCBI Gene Expression Comprehensive Database (GEO) combined with 92 oxidative-stress (OS)-related genes identified in the Molecular Signatures Database. Then we identified differentially-expressed genes (DEOSGs) associated with oxidative stress, which were used for gene ontology (GO) and a Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. At the same time, we used PPI protein interaction networks and Lasso regression analysis to identify key genes, which were verified by the validation sets GSE58438 and GSE71647, as well as Western Blot detection on rat renal IRI models. At the same time, PAS staining, HE staining and immunohistochemistry were used to detect tissue damage and expression of markers related to oxidative stress during renal ischemia-reperfusion. Single-gene enrichment analysis (GSEA) was used to further clarify the underlying biological functions of key genes. Cibersort was used to analyze the immune cell infiltration during renal IRI and the correlation of key genes with immune cells. At the same time, we constructed a network of transcription-factor (TF)-Hub genes and miRNA-Hub genes. DGIDB was used to predict drugs and molecular compounds that might interact with the Hub genes. Compared with the control group, a total of 5456 differential genes (DEGs) were measured in the renal IRI group, 2486 of which were upregulated and 2970 were down-regulated. Among them, we found 30 DEGs (DEOSGs) associated with oxidative stress. The results of GO and KEGG enrichment analysis showed that these DEOSGs were mainly enriched in glutathione metabolism, the response to oxidative stress stimulation, the regulation of T cell activation and apoptosis signaling pathways. Through a protein interaction network (PPI) and a LASSO regression analysis, a total of two Hub genes were identified, namely GPX3 and GSTT1, which were validated through external validation sets and animal experiments. Through pathological methods, we found that the pathological damage of renal tissue and the expression of oxidative stress markers increased after renal ischemia-reperfusion. The results of GSEA showed that the Hub genes were related to oxidative stress pathways, apoptosis signaling pathways and immune-response-related signaling pathways. An immunoinfiltration correlation analysis showed that genes GPX3 and GSTT1 were significantly positively correlated with plasma cells and macrophage M0, while were negatively correlated with monocytes and macrophages M1 and M2. Using the Strust, Starbase and DGIDB database, we predicted that 81 transcription factors, 49 miRNAs and 13 drug or molecular compounds might interact with the Hub genes. Through a comprehensive analysis of gene expression, our findings may provide new potential biomarkers for the pathogenesis of renal IRIs and a reliable basis for its early diagnosis as well as treatment.

Seven bacterial response-related genes are biomarkers for colon cancer

BackgroundColon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis.MethodsThe Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues.ResultsA prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480).ConclusionsWe created a seven-gene bacterial response‐related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Association of Glycosylation-Related Genes with Different Patterns of Immune Profiles and Prognosis in Cervical Cancer.

(1) Background: Although the application of modern diagnostic tests and vaccination against human papillomavirus has markedly reduced the incidence and mortality of early cervical cancer, advanced cervical cancer still has a high death rate worldwide. Glycosylation is closely associated with tumor invasion, metabolism, and the immune response. This study explored the relationship among glycosylation-related genes, the immune microenvironment, and the prognosis of cervical cancer. (2) Methods and results: Clinical information and glycosylation-related genes of cervical cancer patients were downloaded from the TCGA database and the Molecular Signatures Database. Patients in the training cohort were split into two subgroups using consensus clustering. A better prognosis was observed to be associated with a high immune score, level, and status using ESTIMATE, CIBERSORT, and ssGSEA analyses. The differentially expressed genes were revealed to be enriched in proteoglycans in cancer and the cytokine-cytokine receptor interaction, as well as in the PI3K/AKT and the Hippo signaling pathways according to functional analyses, including GO, KEGG, and PPI. The prognostic risk model generated using the univariate Cox regression analysis, LASSO algorithm and multivariate Cox regression analyses, and prognostic nomogram successfully predicted the survival and prognosis of cervical cancer patients. (3) Conclusions: Glycosylation-related genes are correlated with the immune microenvironment of cervical cancer and show promising clinical prediction value.

Lipid metabolism-related miRNAs with potential diagnostic roles in prostate cancer

BackgroundProstate cancer (PCa), the second most prevalent solid tumor among men worldwide, has caused greatly increasing mortality in PCa patients. The effects of lipid metabolism on tumor growth have been explored, but the mechanistic details of the association of lipid metabolism disorders with PCa remain largely elusive.MethodsThe RNA sequencing data of the GSE45604 and The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) datasets were extracted from the Gene Expression Omnibus (GEO) and UCSC Xena databases, respectively. The Molecular Signatures Database (MSigDB) was utilized to identify lipid metabolism-related genes. The limma R package was used to identify differentially expressed lipid metabolism-related genes (DE-LMRGs) and differentially expressed microRNAs (DEMs). Moreover, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and support vector machine-recursive feature elimination (SVM-RFE) were applied to select signature miRNAs and construct a lipid metabolism-related diagnostic model. The expression levels of selected differentially expressed lipid metabolism-related miRNAs (DE-LMRMs) in PCa and benign prostate hyperplasia (BPH) specimens were verified using quantitative real-time polymerase chain reaction (qRT‒PCR). Furthermore, a transcription factor (TF)-miRNA‒mRNA network was constructed. Eventually, Kaplan‒Meier (KM) curves were plotted to illustrate the associations between signature miRNA-related mRNAs and TFs and overall survival (OS) along with biochemical recurrence-free survival (BCR).ResultsForty-seven LMRMs were screened based on the correlation analysis of 29 DE-LMRGs and 56 DEMs, in which 27 LMRMs were stably expressed in the GSE45604 dataset. Subsequently, receiver operating characteristic (ROC) curves and machine learning methods were employed to develop a lipid metabolism-related diagnostic signature, which may be of diagnostic value for PCa patients. qRT‒PCR results showed that all seven key DE-LMRMs were differentially expressed between PCa and BPH tissues. Eventually, a TF-miRNA‒mRNA network was constructed.ConclusionsThese results suggested that 7 key diagnostic miRNAs were closely related to PCa pathological processes and provided new targets for the diagnosis and treatment of PCa. Moreover, CLIC6 and SCNN1A linked to miR-200c-3p had good prognostic potential and provided valuable insights into the pathogenesis of PCa.

Metabolism-related long non-coding RNA in the stomach cancer associated with 11 AMMLs predictive nomograms for OS in STAD.

Background: The metabolic processes involving amino acids are intimately linked to the onset and progression of cancer. Long non-coding RNAs (LncRNAs) perform an indispensable function in the modulation of metabolic processes as well as the advancement of tumors. Non-etheless, research into the role that amino acid metabolism-related LncRNAs (AMMLs) might play in predicting the prognosis of stomach adenocarcinoma (STAD) has not been done. Therefore, This study sought to design a model for AMMLs to predict STAD-related prognosis and elucidate their immune properties and molecular mechanisms. Methods: The STAD RNA-seq data in the TCGA-STAD dataset were randomized into the training and validation groups in a 1:1 ratio, and models were constructed and validated respectively. In the molecular signature database, This study screened for genes involved in amino acid metabolism. AMMLs were obtained by Pearson's correlation analysis, and predictive risk characteristics were established using least absolute shrinkage and selection operator (LASSO) regression, univariate Cox analysis, and multivariate Cox analysis. Subsequently, the immune and molecular profiles of high- and low-risk patients and the benefit of the drug were examined. Results: Eleven AMMLs (LINC01697, LINC00460, LINC00592, MIR548XHG, LINC02728, RBAKDN, LINCOG, LINC00449, LINC01819, and UBE2R2-AS1) were used to develop a prognostic model. Moreover, high-risk individuals had worse overall survival (OS) than low-risk patients in the validation and comprehensive groups. A high-risk score was associated with cancer metastasis as well as angiogenic pathways and high infiltration of tumor-associated fibroblasts, Treg cells, and M2 macrophages; suppressed immune responses; and a more aggressive phenotype. Conclusion: This study identified a risk signal associated with 11 AMMLs and established predictive nomograms for OS in STAD. These findings will help us personalize treatment for gastric cancer patients.

Glutamine metabolism-related genes predict prognosis and reshape tumor microenvironment immune characteristics in diffuse gliomas.

Diffuse gliomas possess a kind of malignant brain tumor with high mortality. Glutamine represents the most abundant and versatile amino acid in the body. Glutamine not only plays an important role in cell metabolism but also involves in cell survival and malignancies progression. Recent studies indicate that glutamine could also affect the metabolism of immune cells in the tumor microenvironment (TME). The transcriptome data and clinicopathological information of patients with glioma were acquired from TCGA, CGGA, and West China Hospital (WCH). The glutamine metabolism-related genes (GMRGs) were retrieved from the Molecular Signature Database. Consensus clustering analysis was used to discover expression patterns of GMRGs, and glutamine metabolism risk scores (GMRSs) were established to model tumor aggressiveness-related GMRG expression signature. ESTIMATE and CIBERSORTx were applied to depict the TME immune landscape. The tumor immunological phenotype analysis and TIDE were utilized for predicting the therapeutic response of immunotherapy. A total of 106 GMRGs were retrieved. Two distinct clusters were established by consensus clustering analysis, which showed a close association with the IDH mutational status of gliomas. In both IDH-mutant and IDH-wildtype gliomas, cluster 2 had significantly shorter overall survival compared with cluster 1, and the differentially expressed genes between the two clusters enriched in pathways related to malignant transformation as well as immunity. In silico TME analysis of the two IDH subtypes revealed not only significantly different immune cell infiltrations and immune phenotypes between the GMRG expression clusters but also different predicted responses to immunotherapy. After the screening, a total of 10 GMRGs were selected to build the GMRS. Survival analysis demonstrated the independent prognostic role of GMRS. Prognostic nomograms were established to predict 1-, 2-, and 3-year survival rates in the four cohorts. Different subtypes of glutamine metabolism could affect the aggressiveness and TME immune features of diffuse glioma, despite their IDH mutational status. The expression signature of GMRGs could not only predict the outcome of patients with glioma but also be combined into an accurate prognostic nomogram.

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer.

Breast cancer (BC) is the most common malignancy among women. Nicotinamide (NAM) metabolism regulates the development of multiple tumors. Herein, we sought to develop a NAM metabolism-related signature (NMRS) to make predictions of survival, tumor microenvironment (TME) and treatment efficacy in BC patients. Transcriptional profiles and clinical data from The Cancer Genome Atlas (TCGA) were analyzed. NAM metabolism-related genes (NMRGs) were retrieved from the Molecular Signatures Database. Consensus clustering was performed on the NMRGs and the differentially expressed genes between different clusters were identified. Univariate Cox, Lasso, and multivariate Cox regression analyses were sequentially conducted to develop the NAM metabolism-related signature (NMRS), which was then validated in the International Cancer Genome Consortium (ICGC) database and Gene Expression Omnibus (GEO) single-cell RNA-seq data. Further studies, such as gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, SubMap, and Immunophenoscore (IPS) algorithm, cancer-immunity cycle (CIC), tumor mutation burden (TMB), and drug sensitivity were performed to assess the TME and treatment response. We identified a 6-gene NMRS that was significantly associated with BC prognosis as an independent indicator. We performed risk stratification according to the NMRS and the low-risk group showed preferable clinical outcomes (P < 0.001). A comprehensive nomogram was developed and showed excellent predictive value for prognosis. GSEA demonstrated that the low-risk group was predominantly enriched in immune-associated pathways, whereas the high-risk group was enriched in cancer-related pathways. The ESTIMATE and CIBERSORT algorithms revealed that the low-risk group had a higher abundance of anti-tumor immunocyte infiltration (P < 0.05). Results of Submap, IPS, CIC, TMB, and external immunotherapy cohort (iMvigor210) analyses showed that the low-risk group were indicative of better immunotherapy response (P < 0.05). The novel signature offers a promising way to evaluate the prognosis and treatment efficacy in BC patients, which may facilitate clinical practice and management.

Lymph node metastasis‐dependent molecular classification in papillary thyroid carcinoma defines aggressive metastatic outgrowth

Lymph node metastasis‐dependent molecular classification in papillary thyroid carcinoma defines aggressive metastatic outgrowth

Abstract P4-09-08: AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer

Abstract Background: Morphological features of cancer cell nuclei are routinely used to assess disease severity and prognosis, and cancer nuclear morphology has been linked to genomic alterations. Quantitative analyses of the nuclear features of cancer cells and other tumor-resident cell types, such as cancer-associated fibroblasts (CAFs), may reveal novel biomarkers for prognosis and treatment response. Here, we applied a pan-cancer nucleus detection and segmentation algorithm and a cell classification model to hematoxylin and eosin (H&amp;E)-stained whole slide images (WSIs) of breast cancer specimens, enabling the measurement of morphological features of nuclei of multiple cell types within a tumor. Methods: Convolutional Neural Network models for 1) nucleus detection and segmentation and 2) cell classification were deployed on H&amp;E-stained WSIs from The Cancer Genome Atlas (TCGA) breast cancer dataset (primary surgical resections; N=890). Separate models were trained to segment regions of stromal subtypes, such as inflamed and fibroblastic stroma. Nuclear features (area, axis length, eccentricity, color, and texture) were computed and aggregated across each slide to summarize slide-level nuclear morphology for each cell type. Next-generation sequencing-based metrics of genomic instability (N=774) and gene expression (N=868) were acquired and paired with TCGA WSIs. Gene set enrichment analysis was performed using the Molecular Signatures Database. Spearman correlation compared nuclear features to genomic instability metrics. Linear regression was used to assess the relationship between nuclear features and bulk gene expression. Multivariable Cox regression with age and ordinal tumor stage as covariates was used to find association between overall survival (OS) and nuclear features. All reported results were significant (p&amp;lt; 0.05) when adjusted for false discovery rate via the Benjamini-Hochberg procedure. Results: Variation in cancer cell nuclear area, a quantitative metric related to pathologist-assessed nuclear pleomorphism, was calculated by the standard deviation of the nuclear area of cancer cells across a WSI. This feature was associated with genomic instability, as measured by aneuploidy score (r=0.448) and homologous recombination deficiency score (r=0.382), and reduced OS. In contrast, the variability in fibroblast and lymphocyte nuclear areas did not correlate with either metric of genomic instability (all r&amp;lt; 0.1, p&amp;gt;0.05). Furthermore, an association between variation in cancer cell nuclear area with the expression of cell cycle and proliferation pathway genes was observed, suggesting that increased nuclear size heterogeneity may indicate a more aggressive cancer phenotype. Features quantifying CAF nuclear morphology were also assessed, revealing that CAF nucleus shape (larger minor axis length) was associated with lower OS, as well as the expression of gene sets involved in extracellular matrix remodeling and degradation. Conclusions: The nuclear morphologies of breast cancer cells and CAFs reflect underlying genomic and transcriptomic properties of the tumor and correlates with patient outcome. The application of digital pathology analysis of breast cancer histopathology slides enables the integrative study of genomics, transcriptomics, tumor morphology, and overall survival to support research into disease biology research and biomarker discovery. Citation Format: John Abel, Christian Kirkup, Filip Kos, Ylaine Gerardin, Sandhya Srinivasan, Jacqueline Brosnan-Cashman, Ken Leidal, Sanjana Vasudevan, Deepta Rajan, Suyog Jain, Aaditya Prakash, Harshith Padigela, Jake Conway, Neel Patel, Benjamin Trotter, Limin Yu, Amaro Taylor-Weiner, Emma L. Krause, Matthew Bronnimann, Laura Chambre, Ben Glass, Chintan Parmar, Stephanie Hennek, Archit Khosla, Murray Resnick, Andrew H. Beck, Michael Montalto, Fedaa Najdawi, Michael G. Drage, Ilan Wapinski. AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-08.