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

Pancreatic stellate cells and the interleukin family: Linking fibrosis and immunity to pancreatic ductal adenocarcinoma (Review)

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive form of cancer with a low survival rate. A successful treatment strategy should not be limited to targeting cancer cells alone, but should adopt a more comprehensive approach, taking into account other influential factors. These include the extracellular matrix (ECM) and immune microenvironment, both of which are integral components of the tumor microenvironment. The present review describes the roles of pancreatic stellate cells, differentiated cancer‑associated fibroblasts and the interleukin family, either independently or in combination, in the progression of precursor lesions in pancreatic intraepithelial neoplasia and PDAC. These elements contribute to ECM deposition and immunosuppression in PDAC. Therapeutic strategies that integrate interleukin and/or stromal blockade for PDAC immunomodulation and fibrogenesis have yielded inconsistent results. A deeper comprehension of the intricate interplay between fibrosis, and immune responses could pave the way for more effective treatment targets, by elucidating the mechanisms and causes of ECM fibrosis during PDAC progression.

Immunotoxicity induced by triclocarban exposure in zebrafish triggering the risk of pancreatic cancer

Owing to frequent application as a broad-spectrum bactericide, triclocarban (TCC) exposure has raised great concern for aquatic organisms and human health. Herein, based on transcriptome sequencing data analysis of zebrafish, we confirmed that TCC induced oxidative stress and dysimmunity through transcriptional regulation of the related genes. With aid of the Cancer Genome Atlas (TCGA) assembler database, 52 common differentially expressed genes, whose functions were related to immunity, were screened out by virtue of the meta-analysis of pancreatic cancer sample data and differential transcription profiles from TCC-exposed larvae. Acute TCC exposure affected formation of the innate immune cells, delayed mature thymic T-cell development, reduced immunoglobulin M (IgM) levels and promoted excessive release of the pro-inflammatory factors (IL-6, IL-1β and tnfα). Under TCC exposure, the expressions of the genes associated with immune cell abundance in pancreatic cancer were significantly down-regulated, while the levels of ROS were prominently increased in concomitant with suppressed antioxidant activity. Moreover, a series of marker genes (pi3k, nrf2, keap1, ho-1 and nqo1) in the PI3K/Nrf2 antioxidant-stress pathway were abnormally expressed under TCC exposure. Interestingly, vitamin C decreased the malformation and increased the survival rate of 120-hpf larvae and effectively alleviated TCC-induced oxidative stress and immune responses. Overall, TCC exposure induced immunotoxicity and increased the risk of pancreatic cancer by inhibiting the antioxidant capacity of the PI3K/Nrf2 signal pathway. These observations enrich our in-depth understanding of the effects of TCC on early embryonic-larval development and immune damage in zebrafish.

Construction of a metabolism-related gene prognostic model to predict survival of pancreatic cancer patients

Pancreatic cancer (PC) is one of the most fatal malignant tumors, and is commonly diagnosed at an advanced stage with no effective therapy. Metabolism-related genes (MRGs) and immune-related genes (IRGs) play considerable roles in the tumor microenvironment. Therefore, an effective prediction model based on MRGs and IRGs could aid in the prognosis of PC. In this study, differential expression analysis was performed to gain 25 intersectional genes from 857 differentially expressed MRGs (DEMRGs), and 1353 differentially expressed IRGs, from The Cancer Genome Atlas database of PC. Cox and Lasso regression were applied and a five-DEMRGs prognostic model constructed. Survival analysis, ROC values, risk curve and validation analysis showed that the model could independently predict PC prognosis. In addition, the correlation analysis suggested that the five-DEMRGs prognostic model could reflect the status of the immune microenvironment, including Tregs, M1 macrophages and Mast cell resting. Therefore, our study provides new underlying predictive biomarkers and associated immunotherapy targets.

An immune-related microRNA signature prognostic model for pancreatic carcinoma and association with immune microenvironment

To establish a prognostic model based on immune-related microRNA (miRNA) for pancreatic carcinoma. Weighted correlation network analysis (WGCNA) was performed using the "WGCNA" package to find the key module genes involved in pancreatic carcinoma. Spearman correlation analysis was conducted to screen immune-related miRNAs. Uni- and multi-variate COX regression analyses were carried out to identify miRNAs prognostic for overall survival (OS) of pancreatic carcinoma, which were then combined to generate a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) analysis, distribution plot of survival status in patients and regression analysis were collectively performed to study the accuracy of the model in prognosis. Target genes of the miRNAs in the model were intersected with the key module genes, and a miRNA–mRNA network was generated and visualized by Cytoscape3.8.0. TIMER analysis was conducted to study the abundance of immune infiltrates in tumor microenvironment of pancreatic carcinoma. Expression levels of immune checkpoint genes in subgroups stratified by the model were compared by Wilcoxon test. Gene Set Enrichment Analysis (GSEA) was performed to analyze the enriched signaling pathways between subgroups. Differential analysis revealed 1826 genes differentially up-regulated in pancreatic carcinoma and 1276 genes differentially down-regulated. A total of 700 immune-related miRNAs were obtained, of which 7 miRNAs were significantly associated with OS of patients and used to establish a prognostic model with accurate predictive performance. There were 99 mRNAs overlapped from the 318 target genes of the 7 miRNAs and the key modules genes analyzed by WGCNA. Patient samples were categorized as high or low risk according to the prognostic model, which were significantly associated with dendritic cell infiltration and expression of immune checkpoint genes (TNFSF9, TNFRSF9, KIR3DL1, HAVCR2, CD276 and CD80). GSEA showed remarkably enriched signaling pathways in the two subgroups. This study identified an immune-related 7-miRNA based prognostic model for pancreatic carcinoma, which could be used as a reliable tool for prognosis.

Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.

Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer

Breast cancer (BC) is an inflammatory tumor caused by a variety of pathological factors, and is still the most common malignant tumor in women. Immune-related genes (IRGs) play a prominent role in the oncogenesis and progression of BC, and are of tumor-specific expression patterns that would benefit the prognosis evaluation. However, there were no systematic studies concerning the possibilities of IRGs in BC prognosis. In this study, the Cancer Genome Atlas (TCGA) database was used to integrate the expression profiles of IRG with the overall survival (OS) rate of 1039 breast cancer patients. The Cox regression analysis was used to predict the survival-related IRGs in BC. Then, we successfully screened a total of 6 IRGs, including PSME2, ULBP2, IGHE, SCG2, SDC1, and SSTR1, and accordingly constructed a prognosis prediction model of BC. Based on the IRG-related model, the BC patients were divided into high- and low-risk groups, and the association between the prognostic model and tumor immune microenvironment (TME) was further explored. The prognostic model reflected the infiltration of various immune cells. Moreover, the low-risk group was found to be with higher immunophenoscore and distinct mutation signatures compared with the high-risk group. The histological validation showed that SDC1, as well as M2 macrophage biomarker CD206, were both of higher abundance in BC samples of high-risk patients, compared with those of low-risk patients. Our results identify the clinically significant IRGs and demonstrate the importance of the IRG-based immune prognostic model in BC monitoring, prognosis prediction, and therapy.

Exploring immune-related signatures for predicting immunotherapeutic responsiveness, prognosis, and diagnosis of patients with colon cancer

The present study focused on identifying the immune-related signatures and exploring their performance in predicting the prognosis, immunotherapeutic responsiveness, and diagnosis of patients with colon cancer. Firstly, the immunotherapeutic response-related differential expressed genes (DEGs) were identified by comparing responders and non-responders from an anti-PD-L1 cohort using the edgeR R package. Then, the immunotherapeutic response related DEGs was intersected with immune-related genes (IRGs) to obtain the immunotherapeutic response and immune-related genes (IRIGs). Then, an immunotherapeutic response and immune-related risk score (IRIRScore) model consisting of 6 IRIGs was constructed using the univariable Cox regression analysis and multivariate Cox regression analysis based on the COAD cohort from the cancer genome atlas (TCGA) database, which was further validated in two independent gene expression omnibus database (GEO) datasets (GSE39582 and GSE17536) and anti-PD-L1 cohort. A nomogram with good accuracy was established based on the immune-related signatures and clinical factors (C-index = 0.75). In the training dataset and GSE39582, higher IRIRScore was significantly associated with higher TMN and advanced pathological stages. Based on the anti-PD-L1 cohort, patients who were sensitive to immunotherapy had significantly lower risk score than non-responders. Furthermore, we explored the immunotherapy-related signatures based on the training dataset. Kaplan-Meier curve revealed a high level of T cells regulatory (Tregs) was significantly related to poor overall survival (OS), while a high level of T cells CD4 memory resting was significantly related to better OS. Besides, the TMB value of patients in the high-risk group was significantly higher than those in a low-risk group. Moreover, patients in the high-risk group had significantly higher expression levels of immune checkpoint inhibitors. In addition, the immune-related signatures were applied to establish prediction models using the random forest algorithm. Among them, TDGF1 and NRG1 revealed excellent diagnostic predictive performance (AUC >0.8). In conclusion, the current findings provide new insights into immune-related immunotherapeutic responsiveness, prognosis, and diagnosis of colon cancer.

A Novel Five-Gene Signature Related to Clinical Outcome and Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most frequent cancer in women and the main cause of cancer-related deaths in the globe, according to the World Health Organization. The need for biomarkers that can help predict survival or guide treatment decisions in BC patients is critical in order to provide each patient with an individualized treatment plan due to the wide range of prognoses and therapeutic responses. A reliable prognostic model is essential for determining the best course of treatment for patients. Patients’ clinical and pathological data, as well as their mRNA expression levels at level 3, were gleaned from the TCGA databases. Differentially expressed genes (DEGs) between BC and non-tumor specimens were identified. Tumor immunity analyses have been utilized in order to decipher molecular pathways and their relationship to the immune system. The expressions of KIF4A in BC cells were determined by RT-PCR. To evaluate the involvement of KIF4A in BC cell proliferation, CCK-8 tests were used. In this study, utilizing FC > 4 and p < 0.05, we identified 140 upregulated genes and 513 down-regulated genes. A five-gene signature comprising SFRP1, SAA1, RBP4, KIF4A and COL11A1 was developed for the prediction of overall survivals of BC. Overall survival was distinctly worse for patients in the high-risk group than those in the low-risk group. Cancerous and aggressiveness-related pathways and decreased B cell, T cell CD4⁺, T cell CD8⁺, Neutrophil and Myeloid dendritic cells levels were seen in the high-risk group. In addition, we found that KIF4A was highly expressed in BC and its silence resulted in the suppression of the proliferation of BC cells. Taken together, as a possible prognostic factor for BC, the five-gene profile created and verified in this investigation could guide the immunotherapy selection.

Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma

Background: There is accumulating evidence on the clinical importance of the fibroblast growth factor receptor (FGFR) signal, hypoxia, and glycolysis in the immune microenvironment of head and neck squamous cell carcinoma (HNSCC), yet reliable prognostic signatures based on the combination of the fibrosis signal, hypoxia, and glycolysis have not been systematically investigated. Herein, we are committed to establish a fibrosis–hypoxia–glycolysis–related prediction model for the prognosis and related immune infiltration of HNSCC.

Methods: Fibrotic signal status was estimated with microarray data of a discovery cohort from the TCGA database using the UMAP algorithm. Hypoxia, glycolysis, and immune-cell infiltration scores were imputed using the ssGSEA algorithm. Cox regression with the LASSO method was applied to define prognostic genes and develop a fibrosis–hypoxia–glycolysis–related gene signature. Immunohistochemistry (IHC) was conducted to identify the expression of specific genes in the prognostic model. Protein expression of several signature genes was evaluated in HPA. An independent cohort from the GEO database was used for external validation. Another scRNA-seq data set was used to clarify the related immune infiltration of HNSCC.

Results: Six genes, including AREG, THBS1, SEMA3C, ANO1, IGHG2, and EPHX3, were identified to construct a prognostic model for risk stratification, which was mostly validated in the independent cohort. Multivariate analysis revealed that risk score calculated by our prognostic model was identified as an independent adverse prognostic factor (p < .001). Activated B cells, immature B cells, activated CD4⁺ T cells, activated CD8⁺ T cells, effector memory CD8⁺ T cells, MDSCs, and mast cells were identified as key immune cells between high- and low-risk groups. IHC results showed that the expression of SEMA3C, IGHG2 were slightly higher in HNSCC tissue than normal head and neck squamous cell tissue. THBS1, ANO1, and EPHX3 were verified by IHC in HPA. By using single-cell analysis, FGFR-related genes and highly expressed DEGs in low-survival patients were more active in monocytes than in other immune cells.

Conclusion: A fibrosis–hypoxia–glycolysis–related prediction model provides risk estimation for better prognoses to patients diagnosed with HNSCC.

Construction and verification of a prognostic risk model based on immunogenomic landscape analysis of bladder caner

This study was designed to construct a prognostic risk model to predict prognosis and immunotherapy response of bladder cancer (BCa) patinets. 350 differential expressed immune-related genes (DEIRGs) were obtained according to the transcriptome profiling and immune-related genes from the Cancer Genome Atlas (TCGA) database and ImmPort database, respectively. A prognostic risk model was constructed based on 15 hub genes through univariate, multivariate, and LASSO Cox regression analyses. The area under the receiver operating characteristic (ROC) curve was 0.743, indicating the superiority of the model. The scatter plot showed that as the risk score increased, the overall survival decreased significantly. In addition, all results were internally verified by the TCGA cohort. The model showed that the higher the grade, clinical stage, and TNM stage of BCa, the higher the risk score of patients. The tumor mutation burden of the low-risk group was generally higher than that of the high-risk group. Immune cell infiltration analysis showed that CD8 T cells, naive CD4 T cells, follicular helper T cells and M0 Macrophage were significantly different between the two groups. Several key immune checkpoint genes were found to be significantly different between the two groups, such as CTLA4, PD-L1, CD47, CD276, CXCL8, and HAVCR2/TIM3. Finally, the analysis of immunotherapy revealed that the efficacy of CTLA4 or PD1 blockers alone was better in the low-risk group than in the high-risk group. Taken together, we developed and validated a prognostic risk model based on 15 hub genes, which performed well in predicting prognosis and immunotherapy response of BCa patients.