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Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model.

ABSTRACT: Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in KRAS. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., NEU2, GMPS, PRIM2, PNPT1, LDHA, INPP4B, DPYD, PYGL, CA12, DHRS9, SULT1E1, ENPP2, PDE1C, TPH1, CHST12, POLR3GL, DNMT3A, and PGS1). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.

SUBMITTER: Gao Y

PROVIDER: S-EPMC8383117 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThere is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa.MethodsWe downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases.ResultsFour PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8+ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases.ConclusionPCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.

Project description:BackgroundDespite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa.MethodsThe activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan-Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work.ResultsThree metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p <0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity.ConclusionsThe identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.

Project description:Background: Evidence has shown that lactate, an immune signaling molecule, is associated with hepatocellular carcinoma (HCC) progression and immune suppression. Therefore, identifying lactate metabolism-related molecules is a promising therapeutic strategy to inhibit the development of HCC and overcome chemotherapy resistance. Long noncoding RNAs (lncRNAs) are related to tumorigenesis and metastasis. Hence, verifying the molecular subtypes of lncRNAs related to lactate metabolism will play a critical role in managing HCC. Methods: Based on HCC data in The Cancer Genome Atlas (TCGA), lactate metabolic pathway-related genes were enriched by gene collection and enrichment analysis (GSEA). Lactate metabolism-related lncRNAs (LM_lncRNAs) were identified by correlation analysis, HCC molecular subtypes were determined using nonnegative matrix factorization (NMF) clustering, and the response of the three subtypes to chemotherapeutics was further evaluated using the Genomic Tumor Sensitive Cell Line (GDSC) dataset. LM_lncRNAs were examined via Lasso-Cox regression analysis to determine prognosis for patients. A Nomagram plot was used to predict patient survival time. Results: Three molecular subtypes of HCC were identified. The survival rate of patients with C1 subtype was higher than that of those with C2 and C3. Additionally, patients with C3 subtype have higher levels of immune cell infiltration and high expression of genes related to immune checkpoints. The GDSC results indicated that patients with C3 subtypes were more sensitive to chemotherapy drugs such as sorafenib and sunitinib. The prognostic risk assessment model consisted of six risk factors (AC034229.4, AC131009.1, MYOSLID, AC008667.1, AC012073.1, AC068025.1) and two protective factors (LINC00402 and AC103858.1). Based on Kaplan-Meier analysis, low-risk HCC patients had a high survival rate, and the receiver operating characteristic curve (ROC), calibration curve, and C-index confirmed good prediction ability. Conclusion: In this study, the molecular subtyping method and prediction model of lactate metabolism-related lncRNAs (LM_lncRNAs) were constructed for the prognosis of HCC patients. This work demonstrated the potential targets of LM_lncRNAs and provided a novel perspective and therapeutic paradigm for future clinical translation.

Project description:BackgroundOvarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response.Methods15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients.ResultsIn the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.

Project description:BackgroundDiffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment.MethodsGene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC).ResultsConsensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses.ConclusionsThe molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

Project description:PurposeKnowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue.MethodsHere, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues.ResultsThe results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies.ConclusionsMolecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.

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Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model.

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