Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

VSIG2 promotes malignant progression of pancreatic ductal adenocarcinoma by enhancing LAMTOR2-mediated mTOR activation

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most intractable malignancies to overcome clinically due to its insidious onset as well as rapid progression. It is urgent to seek new diagnostic markers and therapeutic targets in order to furthest ameliorate the prognosis of patients with PDAC. V-set and immunoglobulin domain containing 2 (VSIG2) belongs to immunoglobulin superfamily (IgSF), which function as coinhibitory molecule to mediate immune evasion of tumors. Nevertheless, the role of VSIG2 in PDAC and related mechanism still keep unclear.

METHODS

Different expression of VSIG2 in PDAC tissues and cells were detected by bioinformatic analysis, immunohistochemistry, real-time quantitative PCR as well as western blotting. CCK-8, colony formation, Transwell assay, and scratch experiment were utilized to assess proliferation, invasion and migration properties of PDAC cells. The relationship of VSIG2 with late endosomal/lysosomal adaptor, MAPK and MTOR activator 2 (LAMTOR2) and mechanistic target of rapamycin (mTOR) was identified using mass spectrometry, co-immunoprecipitation and immunofluorescence. GO and KEGG enrichment analysis were performed for further pathway verification using western blotting. Additionally, subcutaneous xenograft tumor model and clinical samples analysis were implemented to further elucidate the oncogenic effect of VSIG2 on PDAC in vivo and clinically.

RESULTS

VSIG2 was highly expressed in PDAC tissues and cells. Overexpression of VSIG2 facilitated the proliferation, invasion and migration abilities of PDAC cells, while VSIG2-inhibition exerted opposite effects. Mechanistically, VSIG2 could simultaneously bind to LAMTOR2 and mTOR, thereby enhancing interaction between two molecules, which resulted in elevated phosphorylation-modificatory activation of mTOR and downstream key molecules. Clinically, up-regulation of VSIG2 was positively associated with advanced stage, overall survival and disease-free survival of PDAC patients.

CONCLUSIONS

Our study disclosed that VSIG2 was overexpressed in PDAC, which promoted the proliferation, invasion and metastasis. Mechanically, VSIG2 acted as a scaffold to recruit LAMTOR2 and mTOR simultaneously, stabilize the interaction between them, thus enhancing LAMTOR2-mediated mTOR phosphorylated activation. Collectively, VSIG2 could be exploited as a biomarker for diagnosis and prognosis monitor of PDAC in the future, meanwhile, targeting VSIG2 in PDAC management is expected to be a novel strategy. Video Abstract. Video Abstract.

A population-based nomogram to individualize treatment modality for pancreatic cancer patients underlying surgery

As the most aggressive tumor, TNM staging does not accurately identify patients with pancreatic cancer who are sensitive to therapy. This study aimed to identify associated risk factors and develop a nomogram to predict survival in pancreatic cancer surgery patients and to select the most appropriate comprehensive treatment regimen. First, the survival difference between radiotherapy and no radiotherapy was calculated based on propensity score matching (PSM). Cox regression was conducted to select the predictors of overall survival (OS). The model was constructed using seven variables: histologic type, grade, T stage, N stage, stage, chemotherapy and radiotherapy. All patients were classified into high- or low-risk groups based on the nomogram. The nomogram model for OS was established and showed good calibration and acceptable discrimination (C-index 0.721). Receiver operating characteristic curve (ROC) and DCA curves showed that nomograms had better predictive performance than TNM stage. Patients were divided into low-risk and high-risk groups according to nomogram scores. Radiotherapy is recommended for high-risk patients but not for low-risk patients. We have established a well-performing nomogram to effectively predict the prognosis of pancreatic cancer patients underlying surgery. The web version of the nomogram https://rockeric.shinyapps.io/DynNomapp/ may contribute to treatment optimization in clinical practice.

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study

BACKGROUND

A prognostic assessment method with good sensitivity and specificity plays an important role in the treatment of pancreatic cancer patients. Finding a way to evaluate the prognosis of pancreatic cancer is of great significance for the treatment of pancreatic cancer.

METHODS

In this study, GTEx dataset and TCGA dataset were merged together for differential gene expression analysis. Univariate Cox regression and Lasso regression were used to screen variables in the TCGA dataset. Screening the optimal prognostic assessment model is then performed by gaussian finite mixture model. Receiver operating characteristic (ROC) curves were used as an indicator to assess the predictive ability of the prognostic model, the validation process was performed on the GEO datasets.

RESULTS

Gaussian finite mixture model was then used to build 5-gene signature (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3). Receiver operating characteristic (ROC) curves suggested the 5-gene signature performed well on both the training and validation datasets.

CONCLUSIONS

This 5-gene signature performed well on both our chosen training dataset and validation dataset and provided a new way to predict the prognosis of pancreatic cancer patients.

V-Set and immunoglobulin domain containing (VSIG) proteins as emerging immune checkpoint targets for cancer immunotherapy

The development of immune checkpoint inhibitors is becoming a promising approach to fight cancers. Antibodies targeting immune checkpoint proteins such as CTLA-4 and PD-1 can reinvigorate endogenous antitumor T-cell responses and bring durable advantages to several malignancies. However, only a small subset of patients benefit from these checkpoint inhibitors. Identification of new immune checkpoints with the aim of combination blockade of multiple immune inhibitory pathways is becoming necessary to improve efficiency. Recently, several B7 family-related proteins, TIGIT, VSIG4, and VSIG3, which belong to the VSIG family, have attracted substantial attention as coinhibitory receptors during T-cell activation. By interacting with their corresponding ligands, these VSIG proteins inhibit T-cell responses and maintain an immune suppressive microenvironment in tumors. These results indicated that VSIG family members are becoming putative immune checkpoints in cancer immunotherapy. In this review, we summarized the function of each VSIG protein in regulating immune responses and in tumor progression, thus providing an overview of our current understanding of VSIG family members.

A Ductal-Cell-Related Risk Model Integrating Single-Cell and Bulk Sequencing Data Predicts the Prognosis of Patients With Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy. Single-cell sequencing (scRNA-seq) technology enables quantitative gene expression measurements that underlie the phenotypic diversity of cells within a tumor. By integrating PDAC scRNA-seq and bulk sequencing data, we aim to extract relevant biological insights into the ductal cell features that lead to different prognoses. Firstly, differentially expressed genes (DEGs) of ductal cells between normal and tumor tissues were identified through scRNA-seq data analysis. The effect of DEGs on PDAC survival was then assessed in the bulk sequencing data. Based on these DEGs (LY6D, EPS8, DDIT4, TNFSF10, RBP4, NPY1R, MYADM, SLC12A2, SPCS3, NBPF15) affecting PDAC survival, a risk score model was developed to classify patients into high-risk and low-risk groups. The results showed that the overall survival was significantly longer in the low-risk group (p < 0.05). The model also revealed reliable predictive power in different subgroups of patients. The high-risk group had a higher tumor mutational burden (TMB) (p < 0.05), with significantly higher mutation frequencies in KRAS and ADAMTS12 (p < 0.05). Meanwhile, the high-risk group had a higher tumor stemness score (p < 0.05). However, there was no significant difference in the immune cell infiltration scores between the two groups. Lastly, drug candidates targeting risk model genes were identified, and seven compounds might act against PDAC through different mechanisms. In conclusion, we have developed a validated survival assessment model, which acted as an independent risk factor for PDAC.