Molecular Signatures Database Research Articles

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Amplification of Hippo Signaling Pathway Genes Is Governed and Implicated in the Serous Subtype-Specific Ovarian Carcino-Genesis.

Among women, ovarian cancer ranks as the fifth most common cause of cancer-related deaths. This study examined the impact of Hippo signaling pathway on ovarian carcinogenesis. Therefore, the signatures related to Hippo signaling pathway were derived from the molecular signatures database (MSigDB) and were used for further analysis. The Z score-based pathway activation scoring method was employed to investigate the expression patterns of these signatures in the mRNA expression profiles of ovarian cancer cohorts. Compared to other subtype tumors, the results of this study show that the Hippo signaling pathway signatures are dysregulated prominently in serous subtype-specific ovarian carcinogenesis. A receiver operating characteristic (ROC) curve-based results of the Hippo gene set, yes-associated protein 1 (YAP1), and mammalian sterile 20-like kinases 1 (MST1) genes can predict the serous subtype tumors by higher specificity and sensitivity with significant areas under the curve values also further reconfirmed these signaling dysregulations. Moreover, these gene sets were studied further for mutation analysis in the profile of high-grade serous ovarian adenocarcinoma in the cBioPortal database. The OncoPrint results reveal that these Hippo signaling pathway genes are amplified highly during the grade three and stage third or fourth of serous type ovarian tumors. In addition, the results of the Dependency Map (DepMap) plot also clearly show that these genes are amplified significantly across the ovarian cancer cell lines. Finally, overall survival (OS) curve plot investigations also revealed that these gene expressions show poor survival patterns linked to highly expressed conditions in serous subtypes of ovarian cancer patients with significant p-values (p < 0.05). Thus, the current finding would help to develop the targeted therapies treatment for serous subtype ovarian carcinogenesis.

Abstract PO2-09-05: Building genomic and transcriptomic data among African American and Black patients with triple-negative inflammatory breast cancer

Abstract Background: Inflammatory breast cancer (IBC) is an aggressive breast cancer associated with poor response and early metastases. It represents a health disparity, with higher incidence and worse outcomes in Black compared to White patients. Further, IBC is enriched for the more aggressive triple-negative receptor subtype, TN-IBC, and this subtype is more prevalent among Black IBC patients. It is critical to identify genetic and molecular features that may guide better treatments for these aggressive cancers across races, and thus imperative to identify and increase racial representation among bioinformatics datasets. We previously performed comprehensive genomic and transcriptomic analyses for TN-IBC patient samples and here examine the differences among self-identified African American or Black (AA/B) and other races among IBC patients. Methods: As previously described, we collected matched blood and baseline tumor samples before treatment from 19 patients with primary TN-IBC. We performed whole exome and RNA sequencing (RNA-Seq) on these samples and compared mutations and differentially expressed genes (DEGs). Results were compared between 3 AA/B patient tissues vs the remaining 16 (Other) cases. Results: Mutations shared by all AA/B patients included amplification of the five most commonly mutated hallmark genes in the complete cohort, ARNT, BCL9, DDR2, FCGR2B and LMNA (all 100% in AA/B patients versus all 50% in the Other cohort). All AA/B patients had a Notch1 mutation (two deletions and one missense mutation, 100% AA/B vs 6% Other (one deletion)). Comparing differentially expressed genes and gene set enrichment analyses of the Broad molecular signatures database (MSigDB) demonstrates the majority of DEGs are downregulated in these AA/B patients. Six of the top 20 downregulated gene sets from the MSigDB C2 group relate to breast cancer subtype and among this TNIBC cohort AA/B tumor expression was downregulated for luminal and normal subtype related genes in multiple gene sets and enriched in the Lien metaplastic related genes. In the MSigDB C5 sets, over half of the top 20 enriched gene ontology sets among AA/B patients related to chromosomes, chromatin, spindle assembly or histone binding, while 16 of the top 20 downregulated enriched sets related to immune function. Multiple KEGG pathways enriched in AA/B cases related to RNA processing and expression and DNA replication and repair. Lastly, hallmark gene sets enriched in AA/B patients were cell cycle related, and targets of Myc, Hedgehog, B-catenin, TGFb, and TNFa via NFKb, while top significantly downregulated sets related to metabolism and immune response. Conclusion: While findings from a small sample size can only be considered hypothesis generating, some patterns emerge from the data among this rare cohort of AA/B TN IBC patients. These tumors appear to be devoid of luminal signals, characterized by cell cycle and chromatin remodeling signals, enriched for signaling typical of stem cell self-renewal, and perhaps more immune-deprived than other TN-IBC. Additional work to build racially diverse IBC transcriptomic data are needed to develop strategies to understand and improve outcomes for these patients. Citation Format: Wencai Ma, Li Zhao, Gayathri Devi, Savitri Krishnamurthy, Larry Coffer, Angela Alexander, Megumi Kai, Xiaoping Wang, Huong Le-Petross, Miral Patel, Bisrat Debeb, Chandra Bartholomeusz, Jianhua Zhang, Xingzhi Song, Andrew Futreal, Naoto Ueno, Rachel Layman, Anthony Lucci, Jing Wang, Wendy Woodward. Building genomic and transcriptomic data among African American and Black patients with triple-negative inflammatory breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-09-05.

Abstract PO3-03-01: Impact of Environmental Temperature on Clinical Outcomes and Tumor Microenvironment of Early-Stage Breast Cancer

Abstract Background Preclinical evidence using mouse model suggests that thermal/cold stress increases tumor growth by modulating the tumor microenvironment (TME); however, the clinical relevance of temperature on breast cancer (BC) outcomes is unknown. Studies show that residing in cold regions is associated with higher incidence of BC. We aim to study the impact of environmental temperature on the pathological complete response (pCR) and survival of early-stage BC patients (pts). Methodology A multi-institutional study was conducted within the Oncology Research Information Exchange Network (ORIEN). We analyzed the clinical and genomic data for early-stage BC pts from 12 centers in different environment zones (5 warm and 7 cold) (based on average annual regional temperature obtained from National Centers for Environmental Information). Cox regression was used to measure the association of climate and overall/relapse-free survival (OS/RFS) after adjusting for age, race, body mass index, grade, stage, treatments, comorbidities, and subtype. Raw feature counts were normalized, and differential expression analysis was carried out using DESeq2, while adjusting for all of the covariates. Differential expression rank order was used for subsequent gene set enrichment analysis (GSEA), performed using the cluster profile package in R. Gene sets queried included the Hallmark, Canonical pathways, and GO Biological Processes Ontology collections available through the Molecular Signatures Database (MSigDB). Results Out of the 1,304 early-stage BC pts, 271 pts received neoadjuvant chemotherapy (NAC) (186 warm, 85 cold). Higher clinical T- and N-stages were observed in pts from warm compared to cold regions (p&amp;lt; 0.001). Pts residing in cold regions had more comorbidities (57.6% vs 4.8%, p&amp;lt; 0.001). Pts in warm regions had higher pCR, though not statistically significant (8% vs 2.5%, p= 0.1). In the overall population, the OS (univariate (UV) HR= 0.48, 95% CI 0.27-0.64, p &amp;lt; 0.001; adjusted HR (aHR)= 0.56, 95% CI 0.32 - 0.96, p= 0.03) and RFS (UV HR= 0.51, 95% CI 0.38 - 0.68, p&amp;lt; 0.001; aHR= 0.52, 95% CI 0.36 - 0.75, p= 0.0005) were higher in pts from warm compared to cold regions (Table 1). Among the patient subgroup treated with NAC, the OS (aHR= 0.35, p= 0.02) and RFS (aHR= 0.49, p= 0.02) of pts from warmer regions was also higher than cold regions. RNA sequencing was performed on 826 tumors (443 warm, 383 cold) using pre-treatment samples. Naïve B-cells (mean: 0.09 vs 0.08, p=0.004), CD4 naïve T cells (0.03 vs 0.02, p= 0.0008), CD4+ memory T cells (0.03 vs 0.02, p= 0.04), gamma-delta T-cells (0.007 vs 0.004, p= 0.006) were higher in pts residing in cold compared to warm regions while CD8 T cells, T regulatory cells, macrophages, dendritic cells, natural killer cells were similar in both regions. Macrophage signaling, cholesterol metabolism and the pathway for negative regulation of cell migration in angiogenesis were upregulated in pts living in warm compared to cold regions. Conclusion Early-stage BC pts living in cold have worse OS and RFS compared to warm regions. Our study is the first to report significant differences in the TME among these pts. However, it is important to acknowledge that the limited availability of comprehensive data regarding patients' relocation to different environmental temperature zones during treatments, and individual temperature preferences may be considered a potential limitation of our study. To ensure the validity and robustness of these intriguing findings, larger multi-center studies should be conducted, encompassing detailed information on the duration of exposure to both warm and cold weather conditions. Table 1: OS and RFS of patients living in warm vs cold environments Citation Format: Arya Mariam Roy, Spencer Rosario, Anthony George, Qiang Hu, Brian Czerniecki, John Carpten, Virginia Borges, Bryan Schneider, Jill Kolesar, Christopher Moskaluk, Craig Shriver, Cindy Matsen, Shridar Ganesan, Sneha Phadke, Wajeeha Razaq, Kristopher Attwood, Shipra Gandhi. Impact of Environmental Temperature on Clinical Outcomes and Tumor Microenvironment of Early-Stage Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-03-01.

Integrated single-cell and bulk RNA-sequencing data reveal molecular subtypes based on lactylation-related genes and prognosis and therapeutic response in glioma

ObjectivesGlioma, the most common and aggressive form of brain cancer, possesses a complex biology, which makes elucidating its underlying mechanisms and developing effective treatment strategies challenging. Lactylation is a recently discovered post-translational modification and has emerged as a novel research target to understand its role in various biological processes and diseases. Herein, we explored the role of lactylation in gliomas. MethodsSingle-cell RNA-sequencing (scRNA-seq) data were downloaded from the Tumour Immune Single-Cell Hub database. The R package ‘Seurat’ was used for processing the scRNA-seq data. Lactylation-related genes were identified from published literature and the Molecular Signatures Database. An unsupervised clustering method was used to identify glioma subtypes based on identified lactylation-related genes. Differences among the various clusters were examined, including clinical features, differentially expressed genes (DEGs), enriched pathways and immune cell infiltrates. A lactylation score was generated to predict the overall survival (OS) of patients with glioma using DEGs between the two clusters. ResultsThe lactylation-related genes were obtained from the scRNA-seq data, identifying two molecular subtypes, and a prognostic signature was established to stratify patients with glioma into high- and low-score groups. Analysis of the tumour immune microenvironment revealed that patients in the high-score group exhibited increased immune cell infiltration, chemokine expression and immune checkpoint expression but exhibited worse OS and better response to immunotherapy. ConclusionsAltogether, we established a novel signature based on lactylation-related clusters for robust survival prediction and immunotherapeutic response in gliomas.

An inflammation-related subtype classification for analyzing tumor microenvironment and clinical prognosis in colorectal cancer.

The inflammatory response plays an essential role in the tumor microenvironment (TME) of colorectal cancer (CRC) by modulating tumor growth, progression, and response to therapy through the recruitment of immune cells, production of cytokines, and activation of signaling pathways. However, the molecular subtypes and risk score prognostic model based on inflammatory response remain to be further explored. Inflammation-related genes were collected from the molecular signature database and molecular subtypes were identified using nonnegative matrix factorization based on the TCGA cohort. We compared the clinicopathological features, immune infiltration, somatic mutation profile, survival prognosis, and drug sensitivity between the subtypes. The risk score model was developed using LASSO and multivariate Cox regression in the TCGA cohort. The above results were independently validated in the GEO cohort. Moreover, we explored the biological functions of the hub gene, receptor interacting protein kinase 2 (RIPK2), leveraging proteomics data, in vivo, and in vitro experiments. We identified two inflammation-related subtypes (inflammation-low and inflammation-high) and have excellent internal consistency and stability. Inflammation-high subtype showed higher immune cell infiltration and increased sensitivity to common chemotherapeutic drugs, while inflammation-low subtype may be more suitable for immunotherapy. Besides, the two subtypes differ significantly in pathway enrichment and biological functions. In addition, the 11-gene signature prognostic model constructed from inflammation-related genes showed strong prognostic assessment power and could serve as a novel prognostic marker to predict the survival of CRC patients. Finally, RIPK2 plays a crucial role in promoting malignant proliferation of CRC cell validated by experiment. This study provides new insights into the heterogeneity of CRC and provides novel opportunities for treatment development and clinical decision making.

Identification of genes related to ribosomal proteins in colorectal cancer: exploring their potential as biomarkers, prognostic indicators, and therapeutic targets.

Colorectal cancer (CRC) ranks as the third most commonly diagnosed cancer in both females and males, underscoring the need for the identification of effective biomarkers. We assessed the expression levels of ribosomal proteins (RPs) at both mRNA and protein levels. Subsequently, leveraging the STRING database, we constructed a protein-protein interaction network and identified hub genes. The co-expression network of differentially expressed genes associated with CRC and their target hub RPs was constructed using the weighted gene co-expression network analysis algorithm. Gene ontology and molecular signatures database were conducted to gain insights into the biological roles of genes associated with the identified module. To confirm the results, the expression level of the candidate genes in the CRC samples compared to the adjacent healthy was evaluated by the RT-qPCR method. Our findings indicated that the genes related to RPs were predominantly enriched in biological processes associated with Myc Targets, Oxidative Phosphorylation, and cell proliferation. Also, results demonstrated that elevated levels of GRWD1, MCM5, IMP4, and RABEPK that related to RPs were associated with poor prognostic outcomes for CRC patients. Notably, IMP4 and RABEPK exhibited higher diagnostic value. Moreover, the expression of IMP4 and RABEPK showed a significant association with drug resistance using cancer cell line encyclopedia and genomics of drug sensitivity in cancer databases. Also, the results showed that the expression level of IMP4 and RABEPK in cancerous samples was significantly higher compared to the adjacent healthy ones. The general results of this study have shown that many genes related to RPs are increased in cancer and could be associated with the death rate of patients. We also highlighted the therapeutic and prognostic potentials of RPs genes in CRC.

Endoplasmic reticulum stress-related genes as prognostic and immunogenic biomarkers in prostate cancer

BackgroundThe metastasis and aggressive nature of prostate cancer (PCa) has become a major malignancy related threat that concerns men’s health. The efficacy of immune monotherapy against PCa is questionable due to its lymphocyte-suppressive nature.MethodEndoplasmic reticulum stress- (ERS-) and PCa-prognosis-related genes were obtained from the Molecular Signatures Database and the Cancer Genome Atlas database. The expression, prognosis and immune infiltration values of key genes were explored by “survival R package”, “rms”, “xCELL algorithm”, and univariate–multivariate Cox and LASSO regression analyses. The “consensus cluster plus R package” was used for cluster analysis.ResultAs ERS-related genes, ERLIN2 and CDK5RAP3 showed significant expressional, prognostic and clinic-pathologic values. They were defined as the key genes significantly correlated with immune infiltration and response. The nomogram was constructed with T-stage and primary treatment outcome, and the risk-prognostic model was constructed in the following way: Riskscore = (− 0.1918) * ERLIN2 + (0.5254) * CDK5RAP3. Subsequently, prognostic subgroups based on key genes classified the high-risk group as a pro-cancer subgroup that had lower mutation rates of critical genes (SPOP and MUC16), multiple low-expression immune-relevant molecules, and differences in macrophages (M1 and M2) expressions. Finally, ERLIN2 as an anti-oncogene and CDK5RAP3 as a pro-oncogene were further confirmed by cell phenotype assays and immunohistochemistry.ConclusionWe identified ERLIN2 and CDK5RAP3 as ERS-related genes with important prognostic and immunologic values, and classified patients between high- and low-risk subgroups, which provided new prognostic markers, immunotherapeutic targets, and basis for prognostic assessments.

Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets.

Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches. LN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN. The study identified four hub genes, namely STAT1, PRODH, TXN2 and SETX, associated with OS related to LN. These genes were validated for their diagnostic potential, and their involvement in LN pathogenesis was elucidated through ROC and nomogram. Additionally, alterations in immune cell composition in LN correlated with hub OSEG expression were observed. Immunohistochemical analysis reveals that the hub gene is most correlated with activated B cells and CD8 T cells. Finally, we uncovered that the enriched pathways of OSEGs were mainly involved in the PI3K-Akt pathway and the Janus kinase-signal transducer and activator of transcription pathway. These findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets.

A hypoxia–glycolysis–lactate-related gene signature for prognosis prediction in hepatocellular carcinoma

BackgroundLiver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia–glycolysis–lactate-related gene signature for assessing the prognosis of HCC.MethodsThis study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by “GSVA” R package. Single-cell analysis conducted by “SingleR”, “celldex”, “Seurat”, and “CellCha” R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes.ResultsWe classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1.ConclusionThe hypoxia–glycolysis–lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

Exploration of prognostic and treatment markers in hepatocellular carcinoma via GPCR-related genes analysis

BackgroundG protein-coupled receptors (GPCRs), the biggest family of signaling receptors, account for 34 % of all the drug targets approved by the Food and Drug Administration (FDA). It has been gradually recognized that GPCRs are of significance for tumorigenesis, but in-depth studies are still required to explore specific mechanisms. In this study, the role of GPCRs in hepatocellular carcinoma (HCC) was elucidated, and GPCR-related genes were employed for building a risk-score model for the prognosis and treatment efficacy prediction of HCC patients. MethodsPatients’ data on HCC were sourced from the Liver Hepatocellular Carcinoma-Japan (LIRI-JP) and The Cancer Genome Atlas (TCGA) databases, while GPCR-related genes were obtained from the Molecular Signatures Database (MSigDB). Univariant and multivariant Cox regression analyses, as well as least absolute shrinkage and selection operator (LASSO) were performed with the aim of identifying differentially expressed GPCR-related genes and grouping patients. Differential expression and functional enrichment analyses were performed; protein-protein interaction (PPI) mechanisms were explored; hub genes and micro ribonucleic acid (miRNA)-target gene regulatory networks were constructed. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to evaluate immune infiltration levels and genetic variations. Sensitivity to immunotherapy and common antitumor drugs was predicted via the database Genomics of Drug Sensitivity in Cancer (GDSC). ResultsA GPCR-related risk score containing eight GPCR-related genes (atypical chemokine receptor 3 (ACKR3), C–C chemokine receptor type 3 (CCR3), CCR7, frizzled homolog 5 (FZD5), metabotropic glutamate receptor 8 (GRM8), hydroxycarboxylic acid receptor 1 (HCAR1), 5-hydroxytryptamine receptor 5A (HTR5A) and nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 6 (NLRP6)) was set up. In addition, patients were classified into groups with high and low risks. Patients in the high-risk group exhibited a worse prognosis but demonstrated a more favorable immunotherapy response rate compared with those in the low-risk group. Distinct sensitivity to chemotherapeutic drugs was observed. A clinical prediction model on the basis of GPCR-related risk scores was constructed. Areas under the curves (AUC) corresponding to one-, three- and five-year survival were 0.731, 0.765 and 0.731, respectively. ConclusionsIn this study, an efficient HCC prognostic prediction model was constructed by only GPCR-related genes, which are all potential targets for HCC treatment.

Single cell transcriptomic analyses reveal diverse and dynamic changes of distinct populations of lung interstitial macrophages in hypoxia-induced pulmonary hypertension.

Hypoxia is a common pathological driver contributing to various forms of pulmonary vascular diseases leading to pulmonary hypertension (PH). Pulmonary interstitial macrophages (IMs) play pivotal roles in immune and vascular dysfunction, leading to inflammation, abnormal remodeling, and fibrosis in PH. However, IMs' response to hypoxia and their role in PH progression remain largely unknown. We utilized a murine model of hypoxia-induced PH to investigate the repertoire and functional profiles of IMs in response to acute and prolonged hypoxia, aiming to elucidate their contributions to PH development. We conducted single-cell transcriptomic analyses to characterize the repertoire and functional profiles of murine pulmonary IMs following exposure to hypobaric hypoxia for varying durations (0, 1, 3, 7, and 21 days). Hallmark pathways from the mouse Molecular Signatures Database were utilized to characterize the molecular function of the IM subpopulation in response to hypoxia. Our analysis revealed an early acute inflammatory phase during acute hypoxia exposure (Days 1-3), which was resolved by Day 7, followed by a pro-remodeling phase during prolonged hypoxia (Days 7-21). These phases were marked by distinct subpopulations of IMs: MHCIIhiCCR2+EAR2+ cells characterized the acute inflammatory phase, while TLF+VCAM1hi cells dominated the pro-remodeling phase. The acute inflammatory phase exhibited enrichment in interferon-gamma, IL-2, and IL-6 pathways, while the pro-remodeling phase showed dysregulated chemokine production, hemoglobin clearance, and tissue repair profiles, along with activation of distinct complement pathways. Our findings demonstrate the existence of distinct populations of pulmonary interstitial macrophages corresponding to acute and prolonged hypoxia exposure, pivotal in regulating the inflammatory and remodeling phases of PH pathogenesis. This understanding offers potential avenues for targeted interventions, tailored to specific populations and distinct phases of the disease. Moreover, further identification of triggers for pro-remodeling IMs holds promise in unveiling novel therapeutic strategies for pulmonary hypertension.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer.

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI. Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes. This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells. We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

Identification and validation of tryptophan metabolism-related lncRNAs in lung adenocarcinoma prognosis and immune response

BackgroundTryptophan (Trp) is an essential amino acid. Increasing evidence suggests that tryptophan metabolism plays a complex role in immune escape from Lung adenocarcinoma (LUAD). However, the role of long non-coding RNAs (lncRNAs) in tryptophan metabolism remains to be investigated.MethodsThis study uses The Cancer Genome Atlas (TCGA)-LUAD dataset as the training cohort, and several datasets from the Gene Expression Omnibus (GEO) database are merged into the validation cohort. Genes related to tryptophan metabolism were identified from the Molecular Signatures Database (MSigDB) database and further screened for lncRNAs with Trp-related expression. Subsequently, a prognostic signature of lncRNAs related to tryptophan metabolism was constructed using Cox regression analysis, (Least absolute shrinkage and selection operator regression) and LASSO analysis. The predictive performance of this risk score was validated by Kaplan–Meier (KM) survival analysis, (receiver operating characteristic) ROC curves, and nomograms. We also explored the differences in immune cell infiltration, immune cell function, tumor mutational load (TMB), tumor immune dysfunction and exclusion (TIDE), and anticancer drug sensitivity between high- and low-risk groups. Finally, we used real-time fluorescence quantitative PCR, CCK-8, colony formation, wound healing, transwell, flow cytometry, and nude mouse xenotransplantation models to elucidate the role of ZNF8-ERVK3-1 in LUAD.ResultsWe constructed 16 tryptophan metabolism-associated lncRNA prognostic models in LUAD patients. The risk score could be used as an independent prognostic indicator for the prognosis of LUAD patients. Kaplan–Meier survival analysis, ROC curves, and risk maps validated the prognostic value of the risk score. The high-risk and low-risk groups showed significant differences in phenotypes, such as the percentage of immune cell infiltration, immune cell function, gene mutation frequency, and anticancer drug sensitivity. In addition, patients with high-risk scores had higher TMB and TIDE scores compared to patients with low-risk scores. Finally, we found that ZNF8-ERVK3-1 was highly expressed in LUAD tissues and cell lines. A series of in vitro experiments showed that knockdown of ZNF8-ERVK3-1 inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest in the G0/G1 phase and increased apoptosis. In vivo experiments with xenografts have shown that knocking down ZNF8-ERVK3-1 can significantly inhibit tumor size and tumor proliferation.ConclusionWe constructed a new prognostic model for tryptophan metabolism-related lncRNA. The risk score was closely associated with common clinical features such as immune cell infiltration, immune-related function, TMB, and anticancer drug sensitivity. Knockdown of ZNF8-ERVK3-1 inhibited LUAD cell proliferation, migration, invasion, and G0/G1 phase blockade and promoted apoptosis.

Establishment and verification of a prognostic model based on coagulation and fibrinolysis-related genes in hepatocellular carcinoma.

Studies have shown that coagulation and fibrinolysis (CFR) are correlated with Hepatocellular carcinoma (HCC) progression and prognosis. We aim to build a model based on CFR-correlated genes for risk assessment and prediction of HCC patient. HCC samples were selected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. The Molecular Signatures Database (MSigDB) was used to select the CFR genes. RiskScore model were established by single sample gene set enrichment analysis (ssGSEA), weighted correlation network analysis (WGCNA), multivariate Cox regression analysis, LASSO regression analysis. PCDH17, PGF, PDE2A, FAM110D, FSCN1, FBLN5 were selected as the key genes and designed a RiskScore model. Those key genes were Differential expressions in HCC cell and patients. Overexpression PDE2A inhibited HCC cell migration and invasion. The higher the RiskScore, the lower the probability of survival. The model has high AUC values in the first, third and fifth year prediction curves, indicating that the model has strong prediction performance. The difference analysis of clinicopathological features found that a great proportion of high clinicopathological grade samples showed higher RiskScore. RiskScore were positively correlated with immune scores and TIDE scores. High levels of immune checkpoints and immunomodulators were observed in high RiskScore group. High RiskScore groups may benefit greatly from taking traditional chemotherapy drugs. We screened CFR related genes to design a RiskScore model, which could accurately evaluate the prognosis and survival status of HCC patients, providing certain value for optimizing the clinical treatment of cancer in the future.

151 Exceptional Responders to Base Excision Repair (BER) Inhibition for Recurrent Glioblastoma Display Enrichment for DNA Damage Response Pathways: RNA Sequencing Analysis From a Multicenter Trial

INTRODUCTION: Therapies for glioblastoma recurrence after surgery and chemoradiation are warranted. TRC102 (methoxyamine), a small molecule DNA base-excision repair (BER) inhibitor, reverses temozolomide (TMZ) resistance in preclinical glioma models. We had investigated efficacy of TRC102+TMZ for recurrent glioblastoma (rGBM) through multicenter trial. METHODS: A two-arm, two-stage, non-randomized, Phase II trial of TRC102+TMZ for adults with rGBM was conducted. Arm 1 included bevacizumab-naive patients at first recurrence, with primary aim of estimating response rates using RANO criteria. If sufficient activity was identified, arm 2 was planned in bevacizumab-refractory patients. Secondary aims were to determine overall survival (OS), progression-free survival (PFS), PFS at six months, and toxicity using CTCAE v4.0. Exploratory aims were correlating clinical outcomes with MPG expression and MGMT methylation. Differential gene expression profiling was analyzed using DESeq2 GSVA (RRID:SCR_015687) and transformed into enrichment scores for 'hallmark of DNA repair' pathways from Molecular Signatures Database (MSigDB, RRID:SCR_016863) to assign mechanistic signatures of therapeutic vulnerability. RESULTS: Arm 1 enrolled 19 patients with median of two treatment cycles, but objective responses were not observed. Hence, arm 2 did not open. Median OS was 11.1 months (95%CI 8.2-17.9). Median PFS was 1.9 months (95%CI 1.8-3.7). MGMT promoter was unmethylated in 14/19 patients, and MPG expression was present in 8/14 cases with available tumor tissue. MGMT methylation and MPG non-expression were associated with better OS and PFS. However, two ‘exceptional responders’ had PFS = 11 months, with MGMT methylated and MPG expressed. RNA sequencing of their tumor tissue demonstrated significant enrichment for DNA damage response (DDR) pathways (MSigDB), chromosomal instability gene signatures (CIN70,CIN25), and proliferative gene signatures (PCNA25). CONCLUSIONS: rGBM patients with elevated levels of MPG and DDR molecular signature may have impaired BER and respond better to TRC102+TMZ.

Identification of Anoikis-Related Genes in Spinal Cord Injury: Bioinformatics and Experimental Validation.

Spinal cord injury (SCI) is a serious disease without effective therapeutic strategies. To identify the potential treatments for SCI, it is extremely important to explore the underlying mechanism. Current studies demonstrate that anoikis might play an important role in SCI. In this study, we aimed to identify the key anoikis-related genes (ARGs) providing therapeutic targets for SCI. The mRNA expression matrix of GSE45006 was downloaded from the Gene Expression Omnibus (GEO) database, and the ARGs were downloaded from the Molecular Signatures Database (MSigDB database). Then, the potential differentially expressed ARGs were identified. Next, correlation analysis, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) analysis were employed for the differentially expressed ARGs. Moreover, miRNA-gene networks were constructed by the hub ARGs. Finally, RNA expression of the top ten hub ARGs was validated in the SCI cell model and rat SCI model. A total of 27 common differentially expressed ARGs were identified at different time points (1, 3, 7, and 14days) following SCI. The GO and KEGG enrichment analysis of these ARGs indicated several enriched terms related to proliferation, cell cycle, and apoptotic process. The PPI results revealed that most of the ARGs interacted with each other. Ten hub ARGs were further screened, and all the 10 genes were validated in the SCI cell model. In the rat model, only seven genes were validated eventually. We identified 27 differentially expressed ARGs of the SCI through bioinformatic analysis. Seven real hub ARGs (CCND1, FN1, IGF1, MYC, STAT3, TGFB1, and TP53) were identified eventually. These results may expand our understanding of SCI and contribute to the exploration of potential SCI targets.

Abstract 6253: Single-cell RNA sequencing reveals metastatic site-specific biomarkers for predicting response to CDK4/6 inhibition in advanced HR+/HER2- breast cancer

Abstract Background: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i), in combination with endocrine therapy, are established as standard treatment for advanced HR+/HER2- breast cancer (BC). However, response heterogeneity is observed, with approximately one-third of patients exhibiting intrinsic resistance. This study investigates the mechanisms of variable responses to CDK4/6i through single-cell RNA sequencing of metastatic site biopsies, revealing distinct biomarkers for predicting CDK4/6i response. Methods: Single-cell RNA sequencing analyzed metastatic tumors from advanced HR+/HER2- BC patients pre-CDK4/6i treatment at baseline (BL) and/or at disease progression. BL samples were from CDK4/6i responders (n = 8, median progression-free survival [mPFS] = 17 months), while progressors were categorized as early-progressors (EP, n = 3, mPFS = 3 months) and late-progressors (LP, n = 8, mPFS = 10 months). Metastatic sites included liver (10), pleural effusions (6), ascites (2), and bone (1). InferCNV distinguished tumor cells, and functional analysis utilized the Molecular Signatures Database. Results: Both EP and LP tumor cells exhibited reduced estrogen response and increased IFN-γ pathways compared to BL tumors. LP tumors displayed enhanced Myc targets, EMT, TNF-α, and inflammatory pathways compared to EP tumors, revealing differential mechanisms of early versus late progression. Predictive signatures, including TFF3 and MGP, were significantly upregulated in BL tumor cells across different metastatic sites. Metastasis-specific markers included APOA and SERPINA1 (liver), and AGR2 (pleural effusion). BL and LP responders showed increased tumor infiltrating CD8+ T cell and NK cells compared to EP non-responders. Notably, despite a high CD8+ T frequency in responder tumors, functional analysis revealed a significant upregulation of genes associated with stress/apoptosis and exhaustion in proliferative CD4+ and CD8+ T cells from BL compared to LP tumors, including HSP90 and HSPA8, associated with poor response to immune checkpoint inhibitors. These findings suggest potential benefits of immunotherapy for CDK4/6i late progressors. Longitudinal biopsies further revealed dynamic NK cell expansion and enhanced cytotoxic T cell activity alongside the downregulation of stress/apoptosis gene expression at progression compared to BL tumors. Conclusion: This study underscores the significance of metastatic site-specific biomarkers in predicting outcomes to CDK4/6i. Tumor-infiltration lymphocytes may also serve as baseline predictors, while stress/apoptosis gene signatures suggest checkpoint inhibitor usage in CDK4/6i late progressors. These potentially actionable findings emphasize the need to optimize therapeutic approaches based on microenvironment-specific changes following disease progression. Citation Format: Linjie Luo, Peng Yang, Sofia Mastoraki, Yan Wang, Nicole M. Kettner, Akshara Singareeka Raghavendra, Debasish Tripathy, Senthil Damodaran, Kelly K. Hunt, Ziyi Li, Khandan Keyomarsi. Single-cell RNA sequencing reveals metastatic site-specific biomarkers for predicting response to CDK4/6 inhibition in advanced HR+/HER2- breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6253.

Comprehensive analysis of a NAD+ metabolism-derived gene signature to predict the prognosis and immune landscape in endometrial cancer.

As a crucial regulator influencing tumor progression, nicotinamide adenine dinucleotide (NAD+) is widely acknowledged. However, its role in endometrial cancer (EC) is not completely understood. In this study, we aimed to develop an NAD+metabolic-related genes (NMRGs) risk signature that could reflect the prognosis of EC patients and their responsiveness to immunotherapy and chemotherapy. Data from The Cancer Genome Atlas (TCGA) databases and the Molecular Signatures Database (MSigDB) confirmed two distinct NMRG subtypes in EC patients using consensus clustering, and a risk score was constructed utilizing an NAD+-related prognostic signature depending on the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Receiver operating characteristic (ROC) curves were employed to assess the model's precision. Additionally, we used Gene Set Enrichment Analysis (GSEA) to predict the biological signaling pathways that might be involved. We also explored the role of the risk score in immune cell infiltration, tumor mutation burden (TMB), immunotherapy, and chemotherapy. Our study established a prognostic risk signature based on six NMRGs, and we observed that the high-risk group was associated with a poorer prognosis. Furthermore, we identified a strong correlation between the high-risk group and several pathways, including DNA replication, cell cycle, and mismatch repair. Lastly, our findings highlighted the influence of NMRGs on the regulation of immune infiltration in EC. Therefore, this signature holds potential value in predicting the prognosis of EC patients and guiding their management, including decisions regarding immunotherapy and chemotherapy, ultimately improving the accuracy of EC patient care.

Bioinformatics identifies key genes and potential drugs for energy metabolism disorders in heart failure with dilated cardiomyopathy.

Background: Dysfunction in myocardial energy metabolism plays a vital role in the pathological process of Dilated Cardiomyopathy (DCM). However, the precise mechanisms remain unclear. This study aims to investigate the key molecular mechanisms of energy metabolism and potential therapeutic agents in the progression of dilated cardiomyopathy with heart failure. Methods: Gene expression profiles and clinical data for patients with dilated cardiomyopathy complicated by heart failure, as well as healthy controls, were sourced from the Gene Expression Omnibus (GEO) database. Gene sets associated with energy metabolism were downloaded from the Molecular Signatures Database (MSigDB) for subsequent analysis. Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis were employed to identify key modules and genes related to heart failure. Potential biological mechanisms were investigated through Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the construction of a competing endogenous RNA (ceRNA) network. Molecular docking simulations were then conducted to explore the binding affinity and conformation of potential therapeutic drugs with hub genes. Results: Analysis of the left ventricular tissue expression profiles revealed that, compared to healthy controls, patients with dilated cardiomyopathy exhibited 234 differentially expressed genes and 2 genes related to myocardial energy metabolism. Additionally, Benzoylaconine may serve as a potential therapeutic agent for the treatment of dilated cardiomyopathy. Conclusion: The study findings highlight the crucial role of myocardial energy metabolism in the progression of Dilated Cardiomyopathy. Notably, Benzoylaconine emerges as a potential candidate for treating Dilated Cardiomyopathy, potentially exerting its therapeutic effects by targeted modulation of myocardial energy metabolism through NRK and NT5.