Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer

Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer

BACKGROUND

Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and progression of pancreatic cancer as essential building blocks for protein synthesis and key regulators of cellular metabolism. Understanding the interplay between pancreatic cancer and amino acid metabolism offers potential avenues for improving patient clinical outcomes.

METHODS

A comprehensive analysis integrating 10 machine learning algorithms was executed to pinpoint amino acid metabolic signature. The signature was validated across both internal and external cohorts. Subsequent GSEA was employed to unveil the enriched gene sets and signaling pathways within high- and low-risk subgroups. TMB and drug sensitivity analyses were carried out via Maftools and oncoPredict R packages. CIBERSORT and ssGSEA were harnessed to delve into the immune landscape disparities. Single-cell transcriptomics, qPCR, and Immunohistochemistry were performed to corroborate the expression levels and prognostic significance of this signature.

RESULTS

A four gene based amino acid metabolic signature with superior prognostic capabilities was identified by the combination of 10 machine learning methods. It showed that the novel prognostic model could effectively distinguish patients into high- and low-risk groups in both internal and external cohorts. Notably, the risk score from this novel signature showed significant correlations with TMB, drug resistance, as well as a heightened likelihood of immune evasion and suboptimal responses to immunotherapeutic interventions.

CONCLUSION

Our findings suggested that amino acid metabolism-related signature was closely related to the development, prognosis and immune microenvironment of pancreatic cancer.

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.

METHODS

Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.

RESULTS

A total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.

CONCLUSIONS

We constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell-cell communication in the tumor microenvironment.

Establishment and validation of a prognostic signature for pancreatic ductal adenocarcinoma based on lactate metabolism-related genes

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal malignancy with poor prognosis. To improve patient outcomes, it is necessary to gain a better understanding of the oncogenesis and progression of this disease. Metabolic reprogramming, particularly the regulation of lactate metabolism, is known to have a significant impact on tumor microenvironment and could provide valuable insights for the management of PDAC patients. In this study, we aimed to investigate the prognostic potential of lactate metabolism-related genes (LMRGs). Methods: Transcriptomic data of patients with PDAC along with the clinical outcomes were retrieved from The Cancer Genome Atlas database, and the expression data in normal pancreas from Genotype-Tissue Expression dataset were adopted as the normal control. By using Cox and LASSO regression models, we identified key genes that are differentially expressed in cancerous tissues and related to prognosis. To determine the prognostic value of LMRGs in PDAC, we evaluated their clinical significance and model performance using both the area under the receiver operator characteristic curve (AUC) and calibration curves. In addition, we evaluated the drug sensitivity prediction and immune infiltration by using oncoPredict algorithm, single sample gene set enrichment analysis and Tumor Immune Estimation Resource. Results: A total of 123 LMRGs were identified through differential gene screening analysis, among which 7 LMRGs were identified to comprise a LMRGs signature that independently predict overall survival of these PDAC patient. The AUC values for the LMRGs signature were 0.786, 0.820, 0.837, and 0.816 for predicting 1-, 2-, 3- and 5-year overall survival respectively. Furthermore, this prognostic signature was used to stratify patients into high-risk and low-risk groups, with the former having worse clinical outcomes. This observation was further validated through analysis of the International Cancer Genome Consortium database. In addition, lower sensitivity to gemcitabine and infiltration of immune effector cells were observed in the cancer tissue of patients in the high-risk group. Conclusion: In conclusion, our data suggests that a genomic signature comprised of these LMRGs may be a novel predictor of overall clinical outcomes and present therapeutic potential for PDAC patients.

Development and validation of a gene signature for pancreatic cancer: based on inflammatory response-related genes

Pancreatic cancer (PC) is one of the most common malignant tumors in the world with a poor prognosis. There were limited studies investigating the genetic signatures associated with inflammatory responses, tumor microenvironment (TME), and tumor drug sensitivity prediction. In the Cancer Genome Atlas (TCGA) dataset, we constructed an inflammatory response-related genes prognostic signature for PC, and predictive ability of the model was assessed via the International Cancer Genome Consortium (ICGC) database. Then, we explored the differences of TME, immune checkpoint genes and drug resistance genes, and the cancer cell sensitivity to chemotherapy drugs between different risk score group. Based on the TCGA and ICGC databases, we constructed and validated a prognostic model, which consisted of 5 genes (including AHR, F3, GNA15, IL18, and INHBA). Moreover, the prognostic model was independent prognostic factors affecting overall survival (OS). The low-risk score group had better OS, and lower stromal score, compared with patients in the high-risk score group. The difference of antigen-presenting cells, T cell regulation, and drug resistance genes between different risk score groups was found. In addition, the immune checkpoint genes were positively correlation to risk score. The expression levels of AHR, GNA15, IL18, and INHBA were related to the sensitivity of anti-tumor chemotherapy drugs. Gene set enrichment analysis (GSEA) showed significant pathway such as calcium signaling pathway and p53 signaling pathway. We successfully constructed a 5-inflammatory response-related gene signature to predict survival, TME, and cancer cell sensitivity to chemotherapy drugs in PC patients. Furthermore, substantiation was warranted to verify the role of these genes in tumorigenesis.

ER stress-related mRNA-lncRNA co-expression gene signature predicts the prognosis and immune implications of esophageal cancer

BACKGROUND

Esophageal cancer (EC) is one of the most common malignant cancers in the world. Endoplasmic reticulum (ER) stress is an adaptive response to various stress conditions and has been implicated in the development of various types of cancer. Long noncoding RNAs (lncRNAs) refer to a group of noncoding RNAs (ncRNAs), which regulate gene expression by interacting with DNA, RNA and proteins. Accumulating evidence suggests that lncRNAs are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. However, the prognostic model of EC based on ER stress-related mRNA and lncRNA has not been reported.

METHODS

Firstly, we downloaded RNA expression profiles from The Cancer Genome Atlas (TCGA) and obtained ER stress-related genes from the Molecular Signature Database (MSigDB). Next, Weighted Correlation Network Analysis (WGCNA) co-expression analysis was used to identify survival-related ER stress-related modules. Prognostic models were developed using univariate and Least absolute shrinkage and selection operator (LASSO) regression analyses on the training set and validated on the test set. Afterwards, The Receiver Operating Characteristic (ROC) curve and nomogram were used to evaluate the performance of risk prediction models. Differentially expressed gene (DEG) and enrichment analysis were performed between different groups in order to identify the biological processes correlated with the risk score. Finally, the fraction of immune cell infiltration and the difference of tumor microenvironment were identified in high-risk and low-risk groups.

RESULTS

The WGCNA co-expression analysis identified 49 ER genes that are highly associated with EC prognosis. Using univariate Cox regression and LASSO regression analysis, we developed prognostic risk models based on nine signature genes (four mRNAs and five lncRNAs). Both in the training and in the test sets, the overall survival (OS) of EC patients in the high-risk group was significantly lower than that in the low-risk group. The Kaplan-Meier curve and the ROC curve demonstrate the prognostic model we built can precisely predict the survival with more than 70% accuracy. The correlation analysis between the risk score and the infiltration of immune cells showed that the model can indicate the state of the immune microenvironment in EC.

CONCLUSION

In this study, we developed a novel prognostic model for esophageal cancer based on ER stress-related mRNA-lncRNA co-expression profiles that could predict the prognosis, immune cell infiltration, and immunotherapy response in patients with EC. Our results also may provide clinicians with a quantitative tool to predict the survival time of patients and help them individualize treatment strategies for the patients with EC.

Construction of a Pyroptosis-Related Genes Signature to Improve the Prognostic Prediction and Therapeutic Drugs Selection in Patients with Pancreatic Cancer

Background

Effective prognostic assessment and appropriate drug selection are important for the clinical management of pancreatic cancer (PaC). Here, we aimed to establish a pyroptosis-associated genes (PRGs) signature to predict the prognostic outcomes of PaC and guide clinical drug therapy.

Methods

We identified the differentially expressed PRGs between pancreatic adenocarcinoma (n = 178) and control pancreas samples (n = 171) obtained from different databases, and performed Lasso and Cox regression analysis to create a prognosis signature. Kaplan–Meier (K-M) survival curves and time-dependent receiver operating characteristics were further constructed to assess the utility of the risk model. The International Cancer Genome Consortium (ICGC) PACA-AU cohort (n = 95) was used as a validation dataset to examine the validity of this prognostic model. The correlations of risk score (RS) with clinical features, immune cell infiltration, tumor mutation burden and half-maximal inhibitory concentrations (IC50) of chemotherapeutic drugs were analyzed, and the expression levels of PRGs in cell lines were detected.

Results

A prognostic signature was constructed, which consisted of 4 PRGs (AIM2, IL18, GSMDC and PLCG1). K-M analysis demonstrated a remarkable difference in overall survival (OS) time between low-risk (LR) and high-risk (HR) groups (P < 0.001). The RS contributed to the progression of PaC, and could be a significant independent factor for prognostic prediction. The validation of the ICGC cohort confirmed the effectiveness of the proposed signature. The patients with a HR score in the TCGA cohort had higher tumor mutation burden and more sensitivity to paclitaxel, gemcitabine, 5-fluorouracil and cisplatin than those with a LR score. The differential expression levels of signature genes were verified in vitro.

Conclusion

The PRGs signature can be applied for predicting the prognosis of PaC, and may provide useful information for selection of therapeutic drugs.