Prognostic Value and Correlation With Tumor Immune Infiltration of a Novel Metabolism-Related Gene Signature in Pancreatic Cancer

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer

PURPOSE

Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

A glycosylation-related signature predicts survival in pancreatic cancer

BACKGROUND

Tumor initiation and progression are closely associated with glycosylation. However, glycosylated molecules have not been the subject of extensive studies as prognostic markers for pancreatic cancer. The objectives of this study were to identify glycosylation-related genes in pancreatic cancer and use them to construct reliable prognostic models.

MATERIALS AND METHODS

The Cancer Genome Atlas and Gene Expression Omnibus databases were used to assess the differential expression of glycosylation-related genes; four clusters were identified based on consistent clustering analysis. Kaplan-Meier analyses identified three glycosylation-related genes associated with overall survival. LASSO analysis was then performed on The Cancer Genome Atlas and International Cancer Genome Consortium databases to identify glycosylation-related signatures. We identified 12 GRGs differently expressed in pancreatic cancer and selected three genes (SEL1L, TUBA1C, and SDC1) to build a prognostic model. Thereafter, patients were divided into high and low-risk groups. Eventually, we performed Quantitative real-time PCR (qRT-PCR) to validate the signature.

RESULTS

Clinical outcomes were significantly poorer in the high-risk group than in the low-risk group. There were also significant correlations between the high-risk group and several risk factors, including no-smoking history, drinking history, radiotherapy history, and lower tumor grade. Furthermore, the high-risk group had a higher proportion of immune cells. Eventually, three glycosylation-related genes were validated in human PC cell lines.

CONCLUSION

This study identified the glycosylation-related signature for pancreatic cancer. It is an effective predictor of survival and can guide treatment decisions.

Identification and Validation a Necroptosis-Related Prognostic Signature in Cervical Cancer

Necroptosis is a promising novel target for cervical cancer therapy. Nevertheless, differentially expressed necroptosis-related genes (NRGs) in cervical cancer and their associations with prognosis are far from fully clarified. In this study, differentially expressed NRGs (DE-NRGs) were screened out and their bio-function was elucidated. Subsequently, a prognostic scoring model based on the regression coefficients of the screened out NRGs and their corresponding mRNA expressions were constructed and validated. Finally, the survival probability of cervical cancer patients based on the constructed prognostic scoring model in 3 and 5 years was predicted and assessed. We found 17 DE-NRGs in cervical cancer tissues which were closely related to cancer progression, and most of them were significantly highly expressed. Furthermore, 3 NRG were confirmed as the prognostic signature genes from 17 DE-NRGs by regression analysis. Overall survival predicted through our prognostic scoring model was lower in the high-risk group than in the low-risk group (p < 0.05) in both the TCGA cohort and the external GEO44001 validation cohort. What's more, the prediction performance of our prognostic scoring models well verified by the ROC curve, and the risk score calculated could act as an independent prognostic factor for cervical cancer patients. The calibration curve and C-index (0.776) of the nomogram analysis suggested that the predictive performance of the nomogram was satisfactory. Our study identified and validated a necroptosis-related prognostic signature in cervical cancer, which could well predict the prognosis for cervical cancer patients.

Comprehensive Analysis of Necroptosis in Pancreatic Cancer for Appealing its Implications in Prognosis, Immunotherapy, and Chemotherapy Responses

Objective: Necroptosis represents a new target for cancer immunotherapy and is considered a form of cell death that overcomes apoptosis resistance and enhances tumor immunogenicity. Herein, we aimed to determine necroptosis subtypes and investigate the roles of necroptosis in pancreatic cancer therapy. Methods: Based on the expression of prognostic necroptosis genes in pancreatic cancer samples from TCGA and ICGC cohorts, a consensus clustering approach was implemented for robustly identifying necroptosis subtypes. Immunogenic features were evaluated according to immune cell infiltrations, immune checkpoints, HLA molecules, and cancer-immunity cycle. The sensitivity to chemotherapy agents was estimated using the pRRophetic package. A necroptosis-relevant risk model was developed with a multivariate Cox regression analysis. Results: Five necroptosis subtypes were determined for pancreatic cancer (C1∼C5) with diverse prognosis, immunogenic features, and chemosensitivity. In particular, C4 and C5 presented favorable prognosis and weakened immunogenicity; C2 had high immunogenicity; C1 had undesirable prognosis and high genetic mutations. C5 was the most sensitive to known chemotherapy agents (cisplatin, gemcitabine, docetaxel, and paclitaxel), while C4 displayed resistance to aforementioned agents. The necroptosis-relevant risk model could accurately predict prognosis, immunogenicity, and chemosensitivity. Conclusion: Our findings provided a conceptual framework for comprehending necroptosis in pancreatic cancer biology. Future work is required for evaluating its relevance in the design of combined therapeutic regimens and guiding the best choice for immuno- and chemotherapy.