Construction of a novel model based on cell-in-cell-related genes and validation of KRT7 as a biomarker for predicting survival and immune microenvironment in pancreatic cancer.

Pancreatic cancer (PC) is a highly malignant tumor featured with high intra-tumoral heterogeneity and poor prognosis. Cell-in-cell (CIC) structures have been reported in multiple cancers, and their presence is associated with disease progression. Nonetheless, the prognostic values and biological functions of CIC-related genes in PC remain poorly understood. The sequencing data, as well as corresponding clinicopathological information of PC were collected from public databases. Random forest screening, least absolute shrinkage, and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to construct a prognostic model. The effectiveness and robustness of the model were evaluated using receiver operating characteristic (ROC) curves, survival analysis and establishing the nomogram model. Functional enrichment analyses were conducted to annotate the biological functions. The immune infiltration levels were evaluated by ESTIMATE and CIBERSORT algorithms. The expression of KRT7 (Keratin 7) was validated by quantitative real-time PCR (qRT-PCR), western blotting and immunohistochemistry (IHC) staining. The CIC formation, cell clusters, cell proliferation, migration and invasion assays were applied to investigate the effects of silencing the expression of KRT7. A prognostic model based on four CIC-related genes was constructed to stratify the patients into the low- and high-risk subgroups. The high-risk group had a poorer prognosis, higher tumor mutation burden and lower immune cell infiltration than the low-risk group. Functional enrichment analyses showed that numerous terms and pathways associated with invasion and metastasis were enriched in the high-risk group. KRT7, as the most paramount risk gene in the prognostic model, was significantly associated with a worse prognosis of PC in TCGA dataset and our own cohort. High expression of KRT7 might be responsible for the immunosuppression in the PC microenvironment. KRT7 knockdown was significantly suppressed the abilities of CIC formation, cell cluster, cell proliferation, migration, and invasion in PC cell lines. Our prognostic model based on four CIC-related genes has a significant potential in predicting the prognosis and immune microenvironment of PC, which indicates that targeting CIC processes could be a therapeutic option with great interests. Further studies are needed to reveal the underlying molecular mechanisms and biological implications of CIC phenomenon and related genes in PC progression.

Song J ，Ruze R ，Chen Y ，Xu R ，Yin X ，Wang C ，Xu Q ，Zhao Y ... -  《BMC CANCER》

Construction of a novel model based on PVT1-MYC duet-related genes for predicting survival and characterization of the tumor microenvironment in pancreatic cancer.

Pancreatic cancer is an extremely malignant tumor. PVT1 and MYC signaling has been considered as a therapeutic target recently. Nonetheless, the prognostic values and critical regulatory networks of PVT1-MYC duet in pancreatic cancer remain unclear. Firstly, we identified PVT1-MYC duet-related genes using public databases. Then we analyzed our Hi-C and ChIP-seq data to confirm PVT1-MYC duet. We performed LASSO regression and multivariate Cox regression analysis to build a prognostic model whose effectiveness and robustness were validated by Cox regression, ROC analysis, calibration curve, and nomogram. Besides, we conducted functional enrichment analyses, mutation profiles analyses and the immune features analyses to compare low- and high-risk group. Functional enrichment analyses revealed that several terms associated with cancer progression were enriched in the high-risk group. Mutation profile analysis showed that high-risk group had higher tumor mutation burden, and immune analysis demonstrated high-risk group had more immunosuppressive tumor microenvironment. Finally, we detected PVT1 expression in pancreatic cancer and paracancer tissues from the PUMCH cohort, which showed that PVT1 was significantly upregulated in pancreatic cancer and associated with invasion, metastasis, and poor prognosis. We further performed transwell and proliferation assays and found that PVT1, CDC6, and COL17A1 could promote migration or proliferation of PDAC cells. This study constructed a prognostic model based on three PVT1-MYC duet-related genes, which had a significant potential in predicting the prognosis and tumor microenvironment of pancreatic cancer. These results suggested that targeting PVT1-MYC duet or its regulatory processes could be a therapeutic option with great interests.

Ren B ，Ren J ，Gu M ，Liu X ，You L ，Zhao Y ... -  《Frontiers in Immunology》

Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer.

To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC). Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and Immunology Database and Analysis Portal (ImmPort) were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452. In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic (ROC) curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group. The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.

Liu B ，Fu T ，He P ，Du C ，Xu K ... -  《-》

Development and Validation of an Inflammatory Response-Related Gene Signature for Predicting the Prognosis of Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a highly dangerous malignant tumor of the digestive tract, and difficult to diagnose, treat, and predict the prognosis. As we all know, tumor and inflammation can affect each other, and thus the inflammatory response in the microenvironment can be used to affect the prognosis. So far, the prognostic value of inflammatory response-related genes in PAAD is still unclear. Therefore, this study aimed to explore the inflammatory response-related genes for predicting the prognosis of PAAD. In this study, the mRNA expression profiles of PAAD patients and the corresponding clinical characteristics data of PAAD patients were downloaded from the public database. The least absolute shrinkage and selection operator (LASSO) Cox analysis model was used to identify and construct the prognostic gene signature in The Cancer Genome Atlas (TCGA) cohort. The PAAD patients used for verification are from the International Cancer Genome Consortium (ICGC) cohort. The Kaplan-Meier method was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were performed to identify the independent predictors of OS. Gene set enrichment analysis (GSEA) was performed to obtain gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the correlation between gene expression and immune infiltrates was investigated via single sample gene set enrichment analysis (ssGSEA). The GEPIA database was performed to examine prognostic genes in PAAD. LASSO Cox regression analysis was used to construct a model of inflammatory response-related gene signature. Compared with the low-risk group, patients in the high-risk group had significantly lower OS. The receiver operating characteristic curve (ROC) analysis confirmed the signature's predictive capacity. Multivariate Cox analysis showed that risk score is an independent predictor of OS. Functional analysis shows that the immune status between the two risk groups is significantly different, and the cancer-related pathways were abundant in the high-risk group. Moreover, the risk score is significantly related to tumor grade, stage, and immune infiltration types. It was also obtained that the expression level of prognostic genes was significantly correlated with the sensitivity of cancer cells to anti-tumor drugs. In addition, there are significant differences in the expression of PAAD tissues and adjacent non-tumor tissues. The novel signature constructed from five inflammatory response-related genes can be used to predict prognosis and affect the immune status of PAAD. In addition, suppressing these genes may be a treatment option.

Deng ZL ，Zhou DZ ，Cao SJ ，Li Q ，Zhang JF ，Xie H ... -  《-》

The role of KRT7 in metastasis and prognosis of pancreatic cancer.

Xu C ，Wang S ，Sun Y 《Cancer Cell International》