Dataset Information

Exploitation and Verification of a Stroma- and Metastasis-Associated Risk Prognostic Signature in Pancreatic Adenocarcinoma.

ABSTRACT: Pancreatic adenocarcinoma (PAAD), one of the most malignant tumors, not only has abundant mesenchymal components, but is also characterized by an extremely high metastatic risk. The purpose of this study was to construct a model of stroma- and metastasis-associated prognostic signature, aiming to benefit the existing clinical staging system and predict the prognosis of patients. First, stroma-associated genes were screened from the TCGA database with the ESTIMATE algorithm. Subsequently, transcriptomic data from clinical tissues in the RenJi cohort were screened for metastasis-associated genes. Integrating the two sets of genes, we constructed a risk prognostic signature by Cox and LASSO regression analysis. We then obtained a risk score by a quantitative formula and divided all samples into high- and low-risk groups based on the scores. The results demonstrated that patients with high-risk scores have a worse prognosis than those with low-risk scores, both in the TCGA database and in the RenJi cohort. In addition, tumor mutation burden, chemotherapeutic drug sensitivity and immune infiltration analysis also exhibited significant differences between the two groups. In exploring the potential mechanisms of how stromal components affect tumor metastasis, we simulated different matrix stiffness in vitro to explore its effect on EMT key genes in PAAD cells. We found that cancer cells stimulated by high matrix stiffness may trigger EMT and promote PAAD metastasis.

SUBMITTER: Zheng JH

PROVIDER: S-EPMC9696859 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Exploitation and Verification of a Stroma- and Metastasis-Associated Risk Prognostic Signature in Pancreatic Adenocarcinoma.

Zheng Jia-Hao JH Yao Hong-Fei HF Duan Zong-Hao ZH Ji Pei-Xuan PX Yang Jian J Zhu Yu-Heng YH Jia Qin-Yuan QY Yang Jian-Yu JY Liu De-Jun DJ Sun Yong-Wei YW Chen Peng-Cheng PC Shi Pei-Dong PD Chen Li L

Pharmaceuticals (Basel, Switzerland) 20221028 11

Pancreatic adenocarcinoma (PAAD), one of the most malignant tumors, not only has abundant mesenchymal components, but is also characterized by an extremely high metastatic risk. The purpose of this study was to construct a model of stroma- and metastasis-associated prognostic signature, aiming to benefit the existing clinical staging system and predict the prognosis of patients. First, stroma-associated genes were screened from the TCGA database with the ESTIMATE algorithm. Subsequently, transcr ...[more]

PMID: 36355508

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Project description:BackgroundGenomic instability (GI) is a critical feature of cancer which plays a key role in the occurrence and development of pancreatic adenocarcinoma (PAAD). Long non-coding RNA (LncRNA) is an emerging prognostic biomarker because it is involved in regulating GI. Recently, researchers used such GI-related LncRNAs (GILncRNAs) to establish a prognostic signature for patients with cancer and helped in predicting the overall prognosis of the patients. However, it is evident that patients with PAAD still lack such prognostic signature constructed with GILncRNA.MethodsThe present study screened GILncRNAs from 83 patients with PAAD. Prognosis-related GILncRNAs were identified by univariate Cox regression analysis. The correlation coefficients of these GILncRNAs were obtained by multivariate Cox regression analysis and used to construct a signature. The signature in the present study was then assessed through survival analysis, mutation correlation analysis, independent prognostic analysis, and clinical stratification analysis in the training set and validated in the testing as well as all TCGA set. The current study performed external clinical relevance validation of the signature and validated the effect of AC108134.2 in GILncSig on PAAD using in vitro experiments. Finally, the function of GILncRNA signature (GILncSig) dependent on Gene Ontology enrichment analysis was explored and chemotherapeutic drug sensitivity analysis was also performed.ResultsResults of the present study found that a total of 409 GILncRNAs were identified, 5 of which constituted the prognostic risk signature in this study, namely, AC095057.3, AC108134.2, AC124798.1, AL606834.1, and AC104695.4. It was found that the signature of the present study was better than others in predicting the overall survival and applied to patients with PAAD of all ages, genders, and tumor grades. Further, it was noted that the signature of the current study in the GSE102238, was correlated with tumor length, and tumor stage of patients with PAAD. In vitro, functional experiments were used in the present study to validate that AC108134.2 is associated with PAAD genomic instability and progression. Notably, results of the pRRophetic analysis in the current study showed that the high-risk group possessed reverse characteristics and was sensitive to chemotherapy.ConclusionsIn conclusion, it was evident that the GILncSig used in the present study has good prognostic performance. Therefore, the signature may become a potential sensitive biological indicator of PAAD chemotherapy, which may help in clinical decision-making and management of patients with cancer.

Project description:Objective: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Although progress has been made in the treatment of PDAC, its prognosis remains unsatisfactory. This study aimed to develop novel prognostic genes related to glycolysis in PDAC and to apply these genes to new risk stratification. Methods: In this study, based on the Cancer Genome Atlas (TCGA) PAAD cohort, the expression level of glycolysis-related gene at mRNA level in PAAD and its relationship with prognosis were analyzed. Non-negative matrix decomposition (NMF) clustering was used to cluster PDAC patients according to glycolytic genes. Prognostic glycolytic genes, screened by univariate Cox analysis and LASSO regression analysis were established to calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group and the low-risk group were analyzed, and the signal pathway was further enriched to analyze the correlation between glycolysis genes. In addition, based on RNA-seq data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC samples, and ESTIMATE was used to calculate the immune score of the samples. Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples were divided into two clusters by NMF cluster analysis. Survival analysis showed that PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with cluster 2 samples (P < 0.001). A risk prediction model based on 11 glycolysis genes was constructed, according to which patients were divided into two groups, with significantly poorer prognosis in high-risk group than in low-risk group (P < 0.001). Both internal validation and external dataset validation demonstrate good predictive ability of the model (AUC = 0.805, P < 0.001; AUC = 0.763, P < 0.001). Gene aggregation analysis showed that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk group showed immunosuppressed status and infiltrated by relatively more macrophages and less CD8+T cell. Conclusions: These findings suggested that the gene signature based on glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value for PDAC.

Project description:BackgroundPancreatic adenocarcinoma (PDAC) is a malignant tumor with high heterogeneity and poor prognosis. In this study, we sought to identify the value of platelet-related genes in prognosis and heterogeneity of PDAC through multiple transcriptomic methods.MethodsBased on datasets from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), platelet-related genes were screened out, and the TCGA cohort (n = 171) was identified into two subtypes by unsupervised clustering. The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves. The results were validated in two other external validation sets, ICGC-CA (n = 140) and GSE62452 (n = 66). Furthermore, predictive nomogram containing clinical characteristics and PLRScore was established. In addition, we determined the possible correlation between PLRScore and immune infiltration and response of immunotherapy. Finally, we analyzed the heterogeneity of our signature in various types of cells using single-cell analysis.ResultsPlatelet-related subtypes that have significant difference of overall survival and immune states (p < 0.05) were identified. PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis. The AUCs of training cohort were 0.697, 0.687 and 0.675 for 1-, 3-and 5-year, respectively. Further evaluation of the validation cohorts yielded similar results. In addition, PLRScore was associated with immune cell infiltration and immune checkpoint expression, and had promising ability to predict response to immunotherapy of PDAC.ConclusionsIn this study, the platelet-related subtypes were identified and the four-gene signature was constructed and validated. It may provide new insights into the therapeutic decision-making and molecular targets of PDAC.

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

Dataset Information

Exploitation and Verification of a Stroma- and Metastasis-Associated Risk Prognostic Signature in Pancreatic Adenocarcinoma.

Publications

Exploitation and Verification of a Stroma- and Metastasis-Associated Risk Prognostic Signature in Pancreatic Adenocarcinoma.

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