Predictive Gene Signatures Research Articles

Identification of inflammation related gene signatures for bladder cancer prognosis prediction

Early diagnosis and treatment of bladder cancer are crucial, and since inflammation plays a role in all stages of bladder cancer, this study aims to develop a model based on inflammation-related genes to accurately predict patient prognosis. The data were initially processed through differential analysis and prognostic correlation analysis, then a Least absolute shrinkage and selection operator (LASSO) regression model was constructed by M-cohort and a nomogram was designed to increase the model readability. The T-cohort was used for internal validation, with the GSE32894 and Imvigor210 cohorts used as external data to verify the model’s accuracy. The model’s predictive ability was verified for the prognosis of patients of different ages, gender, tumor stage, and tumour grade. The GSE3167, GSE13507 and GeneExpression Profiling Interactive Analysis (GEPIA) datasets and Human Protein Atlas (HPA) database were used to verify the expression of the inflammation-related genes, which were confirmed by real-time Polymerase Chain Reaction (PCR). A comprehensive analysis of the model’s inflammation-related genes, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA) enrichment analysis, and immune-related analysis were also performed. Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations. Hierarchical validation results demonstrated that the model’s predictive power is reliable for various patient stratifications. The expression of inflammation-related genes was consistent across The Cancer Genome Atlas (TCGA) database, GSE3167 dataset, GSE13507 dataset, Gene Expression Profiling Interactive Analysis (GEPIA) database, and the Human Protein Atlas (HPA) database, and was further validated by real-time Polymerase Chain Reaction (PCR). Pathway enrichment analysis indicated that patients in the high-risk (H-risk) group exhibited a variety of tumors. Meanwhile, patients in the low-risk (L-risk) group may be candidates for immunotherapy, whereas those in the high-risk group are more likely to benefit from chemotherapy. The model of inflammation-related genes can accurately predict bladder cancer patient prognosis, with MEST, FASN, KRT6B, and RGS2 anticipated to become new prognostic bladder cancer markers.

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer.

Platelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications. We downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR). A ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750. The novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

Identification of an immune cell infiltration-related gene signature for prognosis prediction in triple-negative breast cancer.

Triple-negative breast cancer TNBC with higher immunogenicity and tumor-infiltrating lymphocyte (TIL) enrichment can benefit from immunotherapy relative to other breast cancer subtypes. Our work was designed to identify the TIL-related hub genes in TNBC and construct a prognostic signature for TNBC. TNBC gene expression files were obtained from the TCGA database. CIBERSORT algorithm and random forest risk model were used for immune infiltration group division. The TIL-related differentially expressed genes (DEGs) were then selected and subject to GO, KEGG analyses and GSEA. Next, Lasso cox regression analyses were adopted for constructing a prognostic risk model, followed by evaluation using time-dependent ROC curves. The copy number variation between the two risk groups was also analyzed, and major genomic mutation types were identified. Additionally, the nomogram was constructed with calibration curve for clinical prognosis analysis. Our results showed that totally 113 TNBC samples were allocated into the high or low-immune risk groups. We identified 243 DEGs between groups, namely TIL-related DEGs, with 128 upregulated and 115 downregulated genes. Among the TIL-related DEGs, 6 hub genes (SLITRK3, PCDHGB3, NELL2, SRRM4, ASIC2 and B4GALNT2) were screened out and constructed a prognostic risk signature, which had good performance for long-term prognosis prediction. Analysis of genomic mutation showed that the TP53, PIK3CA, TTH, etc. showed high mutation frequency in the two prognostic risk groups. Moreover, the higher risk score of the prognostic risk model predicted poor overall survival in TNBC patients, and nomogram and calibration curve confirmed the potent prediction ability of this model. To sum up, six TIL-related biomarkers (SLITRK3, PCDHGB3, NELL2, SRRM4, ASIC2 and B4GALNT2) were identified and used for the construction of the prognostic risk model, which might provide novel insight for the clinical decisions.

Development of a polygenic score predicting drug resistance and patient outcome in breast cancer

Gene expression profiles of hundreds of cancer cell-lines and the cell-lines’ response to drug treatment were analyzed to identify genes whose expression correlated with drug resistance. In the GDSC dataset of 809 cancer cell lines, expression of 36 genes were associated with drug resistance (increased IC50) to many anti-cancer drugs. This was validated in the CTRP dataset of 860 cell lines. A polygenic score derived from the correlation coefficients of the 36 genes in cancer cell lines, UAB36, predicted resistance of cell lines to Tamoxifen. Although the 36 genes were selected from cell line behaviors, UAB36 successfully predicted survival of breast cancer patients in three different cohorts of patients treated with Tamoxifen. UAB36 outperforms two existing predictive gene signatures and is a predictor of outcome of breast cancer patients independent of the known clinical co-variates that affect outcome. This approach should provide promising polygenic biomarkers for resistance in many cancer types against specific drugs.

Ferroptosis-Related Gene Signature for Prognosis Prediction in Acute Myeloid Leukemia and Potential Therapeutic Options.

Limited data were available to understand the significance of ferroptosis in leukemia prognosis, regardless of the genomic background. RNA-seq data from 151 AML patients were analyzed from The Cancer Genome Atlas (TCGA) database, along with 70 healthy samples from the Genotype-Tissue Expression (GTEx) database. Ferroptosis-related genes (FRGs) features were constructed by multivariate COX regression analysis and risk scores were calculated for each sample and a novel prediction model was identified. The validation was carried out using data from 35 AML patients and 13 healthy controls in our cohort. Drug sensitivity analysis was conducted on various chemotherapeutic drugs. A signature of 10 FRGs was identified, as prognostic predictors for AML, and the risk scores were calculated to constructed the prognostic features of FRGs. Significantly lower overall survival was observed in the high-risk group. The predictive ability of these features for AML prognosis was confirmed using Cox regression analysis, ROC curves, and DCA. The prediction model performed well in our clinical practices, and had its potential superiority when comparing to classical NCCN risk stratification. Multiple chemotherapy drugs, including paclitaxel, dactinomycin, cisplatin, etc. had a lower IC50 in FRGs high-risk group than low-risk group. The AML prognosis model based on FRGs accurately predicts AML prognosis and drug sensitivity, and the drugs identified worthy further investigation.

Development and experimental validation of hypoxia-related gene signatures for osteosarcoma diagnosis and prognosis based on WGCNA and machine learning

Osteosarcoma (OS) is the most common primary malignant tumour of the bone with high mortality. Here, we comprehensively analysed the hypoxia signalling in OS and further constructed novel hypoxia-related gene signatures for OS prediction and prognosis. This study employed Gene Set Enrichment Analysis (GSEA), Weighted correlation network analysis (WGCNA) and Least absolute shrinkage and selection operator (LASSO) analyses to identify Stanniocalcin 2 (STC2) and Transmembrane Protein 45A (TMEM45A) as the diagnostic biomarkers, which further assessed by Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and calibration curves in training and test dataset. Univariate and multivariate Cox regression analyses were used to construct the prognostic model. STC2 and metastasis were devised to forge the OS risk model. The nomogram, risk score, Kaplan Meier plot, ROC, DCA, and calibration curves results certified the excellent performance of the prognostic model. The expression level of STC2 and TMEM45A was validated in external datasets and cell lines. In immune cell infiltration analysis, cancer-associated fibroblasts (CAFs) were significantly higher in the low-risk group. And the immune infiltration of CAFs was negatively associated with the expression of STC2 (P < 0.05). Pan-cancer analysis revealed that the expression level of STC2 was significantly higher in Esophageal carcinoma (ESCA), Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Lung squamous cell carcinoma (LUSC), and Stomach adenocarcinoma (STAD). Additionally, the higher expression of STC2 was associated with the poor outcome in those cancers. In summary, this study identified STC2 and TMEM45A as novel markers for the diagnosis and prognosis of osteosarcoma, and STC2 was shown to correlate with immune infiltration of CAFs negatively.

Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer

BackgroundThe role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. MethodsThis study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. ResultsMCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and β-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. ConclusionThe combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Ubiquitination-Related Gene Signature, Nomogram and Immune Features for Prognostic Prediction in Patients with Head and Neck Squamous Cell Carcinoma.

The objective of this research was to create a prognostic model focused on genes related to ubiquitination (UbRGs) for evaluating their clinical significance in head and neck squamous cell carcinoma (HNSCC) patients. The transcriptome expression data of UbRGs were obtained from The Cancer Genome Atlas (TCGA) database, and weighted gene co-expression network analysis (WGCNA) was used to identify specific UbRGs within survival-related hub modules. A multi-gene signature was formulated using LASSO Cox regression analysis. Furthermore, various analyses, including time-related receiver operating characteristics (ROCs), Kaplan-Meier, Cox regression, nomogram prediction, gene set enrichment, co-expression, immune, tumor mutation burden (TMB), and drug sensitivity, were conducted. Ultimately, a prognostic signature consisting of 11 gene pairs for HNSCC was established. The Kaplan-Meier curves indicated significantly improved overall survival (OS) in the low-risk group compared to the high-risk group (p < 0.001), suggesting its potential as an independent and dependable prognostic factor. Additionally, a nomogram with AUC values of 0.744, 0.852, and 0.861 at 1-, 3-, and 5-year intervals was developed. Infiltration of M2 macrophages was higher in the high-risk group, and the TMB was notably elevated compared to the low-risk group. Several chemotherapy drugs targeting UbRGs were recommended for low-risk and high-risk patients, respectively. The prognostic signature derived from UbRGs can effectively predict prognosis and provide new personalized therapeutic targets for HNSCC.

A novel ARHGAP family gene signature for survival prediction in glioma patients.

ARHGAP family genes are often used as glioma oncogenic factors, and their mechanism of action remains unexplained. Our research entailed a thorough examination of the immune microenvironment and enrichment pathways across various glioma subtypes. A distinctive 6-gene signature was developed employing the CGGA cohort, leading to insights into the disparities in clinical characteristics, mutation patterns, and immune cell infiltration among distinct risk categories. Additionally, a unique nomogram was established, grounded on ARHGAPs, with DCA curves illustrating the model's prospective clinical utility in guiding therapeutic strategies. Emphasizing the role of ARHGAP30, integral to our model, its impact on glioma severity and the credibility of our risk assessment model were substantiated through RT-qPCR, Western blot analysis, and cellular functional assays. We identified 6 ARHGAP family genes associated with glioma prognosis. Analysis using the Kaplan-Meier method indicated a correlation between elevated risk levels and adverse outcomes in glioma patients. The risk score, linked with tumour staging and IDH mutation status, emerged as an independent factor predicting prognosis. Patients in the high-risk category exhibited increased immune cell infiltration, enhanced tumour mutational burden, more pronounced expression of immune checkpoint genes, and a better response to ICB therapy. A nomogram, integrating the risk score with the pathological features of glioma patients, was developed. DCA analysis and cellular studies confirmed the model's potential to improve clinical treatment outcomes for patients. A novel ARHGAP family gene signature reveals the prognosis of glioma.

Construction of a circadian rhythm-relevant gene signature for hepatocellular carcinoma prognosis, immunotherapy and chemosensitivity prediction

AimsThis study explored the molecular and biologic mechanisms underlying the association between circadian rhythm disorders (CRD) and increased risk for hepatocellular carcinoma (HCC). BackgroundCRD are linked to increased risk for HCC, but the molecular and biologic mechanisms underlying this association are limited.ObjectiveThe study constructed and validated a CRD related gene model as an independent prognostic factor for HCC, providing insight into the molecular mechanisms linking CRD to increased HCC risk and identifying potential indicators for the efficacy of immunotherapy and anticancer drugs. This helps provide important clues for personalized treatment strategies for HCC patients. MethodsGene sets correlated with circadian rhythm were obtained from the Molecular Signatures Database (MSigDB) to intersect with differentially expressed genes (DEGs) between tumor samples and control samples in The Cancer Genome Atlas (TCGA) and HCCDB18 from Hepatocellular Carcinoma Cell DataBase (HCCDB). The CRD related gene model was developed by univariate Cox and stepwise multivariate analysis. Immune checkpoint blockade (ICB) therapy and anticancer drugs were analyzed using the tumor immune dysfunction and exclusion (TIDE) and pRRophetic, respectively. Seurat determined the cell type of HCC by analyzing single-cell data, and malignant cells were identified using Copykat. To detect the mRNA levels of genes in the CRD related gene model, quantitative real-time polymerase chain reaction (qRT-PCR) was carried out. ResultsThe activity of circadian rhythm in HCC tissue was significantly lower than that in control tissue. Subsequently, EZH2, IMPDH2, TYMS and SERPINE1 were selected to construct the CRD related gene model, which was an independent factor for HCC prognosis. Notably, low-risk patients had lower levels of immune cell infiltration and lower TIDE scores compared to high-risk patients with HCC, indicating that patients with a low risk may derive more benefit from immunotherapy. IMPDH2, TYMS and SERPINE1 expressed significantly higher in malignant cells than in benign epithelial cells. ConclusionsThis study presents a CRD related gene model to reveal the molecular perspective of the dependent mechanism of the association between CRD and cancer, which provides a potential indicator for understanding the preclinical efficacy of ICB and anticancer drugs.

Establishing a Ten Disulfidptosis-related Gene Signature for Prognostic Prediction in Skin Cutaneous Melanoma.

Disulfidptosis is a new metabolic-related regulated cell death associated with cancer growth. This study aimed to investigate the molecular mechanisms associated with disulfidptosis in skin cutaneous melanoma (SKCM) and establish a disulfidptosis-related gene signature for prognostic prediction in SKCM. Disulfidptosis-associated genes were identified from RNA-seq data of SKCM. A risk score signature was developed and validated through univariate Cox and LASSO analyses. Additionally, the immune microenvironment related to the risk score signature was investigated. Finally, a disulfidptosis-related genes-transcription factor -miRNA network was developed, and the expression levels of five disulfidptosis-related genes were initially verified in SKCM cell lines. A total of 107 disulfidptosis-related differentially expressed genes in SKCM samples were identified. A ten-disulfidptosis-gene signature was established, including BIN2, CCL3L3, CCL8, CD79A, CIITA, CXCR3, DEFB1, GPR171, IL2RB, and SOCS1. The SKCM samples were divided into high- and low-risk groups, of which samples in the low-risk group showed better survival performance. The receiver operating characteristic curve analysis confirmed the good potency of the disulfidptosis-related gene prognostic model. Except for DEFB1, the other nine genes were positively related with T cell CD8+, T cell CD4+ memory activated, T cell gamma delta, NK cell activated, and macrophage M1, and they were all negatively related with NK cell resting, macrophage M0, macrophage M2, and mast cell activated. Finally, we verified downregulated levels of SOCS1 and DEFB1 and upregulated CXCR3, BIN2, and CCL3L3 in A875 and A375. We successfully established ten disulfidptosis-related genes' prediction prognostic signatures for SKCM patients.

Identification of a unique stress response state of T cells-related gene signature in patients with gastric cancer.

Gastric cancer (GC), the third most lethal cancer worldwide, is often diagnosed at an advanced stage, leaving limited therapeutic options. Given the diverse outcomes among GC patients with similar AJCC/UICC-TNM characteristics, there is a pressing need for more reliable prognostic tools. Recent advances in targeted therapy and immunotherapy have underscored this necessity. In this context, our study focused on a novel stress response state of T cells, termed TSTR, identified across multiple cancers, which is associated with resistance to immunotherapy. We aimed to develop a predictive gene signature for the TSTR phenotype within the tumor microenvironment (TME) of GC patients. By categorizing GC patients into high and low TSTR groups based on the infiltration states of TME TSTR cells, we observed significant differences in clinical prognosis and characteristics between the groups. Through a multi-step bioinformatics approach, we established an eight-gene signature based on genes differentially expressed between these groups. We conducted functional validations for the signature gene PDGFRL in GC cells. This gene signature effectively stratifies GC patients into high and low-risk categories, demonstrating robustness in predicting clinical outcomes. Furthermore, these risk groups exhibited distinct immune profiles, somatic mutations, and drug susceptibilities, highlighting the potential of our gene signature to enhance personalized treatment strategies in clinical practice.

Predictive gene signature for trabectedin efficacy in advanced soft‐tissue sarcoma: A Spanish group for research in sarcoma (GEIS) study.

Predictive gene signature for trabectedin efficacy in advanced soft‐tissue sarcoma: A Spanish group for research in sarcoma (GEIS) study.

Gene signature to predict hypophysitis in patients with melanoma treated with anti-PD-1 in adjuvant setting.

e21562 Background: Adjuvant anti-PD-1 treatment has shown to improve recurrence free survival and distant metastases free survival in melanoma patients from stage II to IV. Until now ,we have no data about overall survival of these patients nor biomarkers to select them to the best therapy , avoiding in this way severe and some times durable toxicities.Hypophysitis is a rare immune-related adverse event (irAE) and in adjuvant setting may represent a chronic irAE that may affect quality of life , such as fertility in young people.There are no clinical factors able to predict hypophysitis due to adjuvant anti-PD-1 therapy. Methods: Gene profiling analysis was performed using NanoString IO360 panel from basal PBMCs of patients treated with anti-PD1 in adjuvant setting. Patient’s characteristics are reported in table1. ROC curves were used to determine the best cut off. Survival rates were evaluated and compared using the Kaplan–Meier method. Results: Eighty pts treated with adjuvant anti-PD-1were included in a retrospective analysis from December 2019 to September 2021. Among which 12 pts developed hyphosytis. Ten pts completed treatment regularly, while two were discontinued both at week 12 due to metastatic disease. A pts developed liver metastases, the other one liver, multiple lymph nodes and soft tissues metastases. Grade 2 hypophysitis was diagnosed as the only irAE in all pts (100%). Median time of onset was 3 months (9 pts ≤6 months and 7 pts ≤3 months). A specific gene signature for predicting the onset of hypophysitis has been identified and characterized with 4 genes: IFITM2,CTSS,DUSP1,HLA-B that are mainly involved in the activation of innate-like lymphocyte populations, such as natural killer (NK) cells and innate lymphoid cells (ILCs), and adaptive immune cells, such as T helper 1 (TH1) cells and CD8+ cytotoxic T lymphocytes (CTLs). High gene signature was mostly related to hypophysitis event than lower group (HR: 0.11 95%IC 0.03-0.39, p = 0,0007). Moreover, at data lock all pts who developed hypophysitis were censored with mean overall survival of 35.60 months, 95%CI 30,86-40, 41. Conclusions: A predictive gene signature of hypophysitis was found in melanoma patients treated with adjuvant anti-PD1.Moreover, hypophysitis seems to correlate to better outcome. [Table: see text]

Systematic Analysis of Tumor Stem Cell-related Gene Characteristics to Predict the PD-L1 Immunotherapy and Prognosis of Gastric Cancer.

We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC). Tumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC. We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC. We downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram. Early GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy. We developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.

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.

Identifying a Novel Eight-NK Cell-related Gene Signature for Ovarian Cancer Prognosis Prediction.

Ovarian cancer (OVC) is the most common and costly tumor in the world with unfavorable overall survival and prognosis. This study is aimed to explore the prognostic value of natural killer cells related genes for OVC treatment. RNA-seq and clinical information were acquired from the TCGA-OVC dataset (training dataset) and the GSE51800 dataset (validation dataset). Genes linked to NK cells were obtained from the immPort dataset. Moreover, ConsensusClusterPlus facilitated the screening of molecular subtypes. Following this, the risk model was established by LASSO analysis, and immune infiltration and immunotherapy were then detected by CIBERSORT, ssGSEA, ESTIMATE, and TIDE algorithms. Based on 23 NK cell-related genes with prognosis, TCGA-OVC samples were classified into two clusters, namely C1 and C2. Of these, C1 had better survival outcomes as well as enhanced immune infiltration and tumor stem cells. Additionally, it was more suitable for immunotherapy and was also sensitive to traditional chemotherapy drugs. The eight-gene prognosis model was constructed and verified via the GSE51800 dataset. Additionally, a high infiltration level of immune cells was observed in low-risk patients. Low-risk samples also benefited from immunotherapy and chemotherapy drugs. Finally, a nomogram and ROC curves were applied to validate model accuracy. The present study identified a RiskScore signature, which could stratify patients with different infiltration levels, immunotherapy, and chemotherapy drugs. Our study provided a basis for precisely evaluating OVC therapy and prognosis.

Abstract 7663: Identification of high-risk patients in multiple myeloma using a clonal gene signature

Abstract Introduction: Multiple myeloma (MM) is a heterogeneous disease with a small subset of high-risk patients associated with poor prognosis. The identification of these patients is crucial for treatment management and strategic decisions. Methods: We developed a novel computational framework that only requires gene expression profiles to define prognostic gene signatures by selecting genes with expression driven by inferred clonal copy number alterations. We applied this framework to MM and developed a clonal gene signature (CGS) consisting of only 22 genes. This prognostic signature was evaluated in 5 independent datasets with gene expression data of a total of 2,155 MM samples. For each patient, we calculated a prognostic score based on the expression of the signature genes. Multivariate Cox regression analyses were performed to investigate the association of these signature scores with patient prognosis after adjusting established clinical factors. The performances of CGS and the only commercialized MM gene signatures, GEP70 and SKY92, in identifying high-risk patients were carefully compared by multiple different approaches. Results: Our results indicated that CGS provided significant prognostic values after adjusting for well-established prognostic factors including cytogenetic abnormalities, International Staging System (ISS), and Revised ISS (R-ISS). In addition, it showed significant prognostic associations in patient subsets stratified by these conventional prognostic factors. Importantly, CGS demonstrated a better performance in identifying high-risk patients compared to the GEP70 and SKY92 signatures, which have been recommended for prognostic stratification in MM. Moreover, CGS can further stratify patients into subgroups with significantly differential prognoses when applied to the high-risk and low-risk groups already identified by GEP70 and SKY92. The higher CGS score of patients is significantly associated with poor response to dexamethasone, a commonly used drug for MM treatment. Conclusions: In summary, we proposed a computational framework that only requires gene expression data to identify clonal gene signatures for prognosis prediction. The CGS provides a useful biomarker for improving prognostic stratification in MM, especially for the identification of patients with the highest risk. The computational framework used for defining this signature provides a useful tool, which can be readily applied to many other cancer types. Citation Format: Jian-Rong Li, Christiana Wang, Chao Cheng. Identification of high-risk patients in multiple myeloma using a clonal gene signature [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 7663.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer.

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups. To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations. Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed. We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy. In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Programming a Ferroptosis-to-Apoptosis Transition Landscape Revealed Ferroptosis Biomarkers and Repressors for Cancer Therapy.

Ferroptosis and apoptosis are key cell-death pathways implicated in several human diseases including cancer. Ferroptosis is driven by iron-dependent lipid peroxidation and currently has no characteristic biomarkers or gene signatures. Here a continuous phenotypic gradient between ferroptosis and apoptosis coupled to transcriptomic and metabolomic landscapes is established. The gradual ferroptosis-to-apoptosis transcriptomic landscape is used to generate a unique, unbiased transcriptomic predictor, the Gradient Gene Set (GGS), which classified ferroptosis and apoptosis with high accuracy. Further GGS optimization using multiple ferroptotic and apoptotic datasets revealed highly specific ferroptosis biomarkers, which are robustly validated in vitro and in vivo. A subset of the GGS is associated with poor prognosis in breast cancer patients and PDXs and contains different ferroptosis repressors. Depletion of one representative, PDGFA-assaociated protein 1(PDAP1), is found to suppress basal-like breast tumor growth in a mouse model. Omics and mechanistic studies revealed that ferroptosis is associated with enhanced lysosomal function, glutaminolysis, and the tricarboxylic acid (TCA) cycle, while its transition into apoptosis is attributed to enhanced endoplasmic reticulum(ER)-stress and phosphatidylethanolamine (PE)-to-phosphatidylcholine (PC)metabolic shift. Collectively, this study highlights molecular mechanisms underlying ferroptosis execution, identified a highly predictive ferroptosis gene signature with prognostic value, ferroptosis versus apoptosis biomarkers, and ferroptosis repressors for breast cancer therapy.