Identification of Prognostic Immune-Related Genes in Pancreatic Adenocarcinoma and Establishment of a Prognostic Nomogram: A Bioinformatic Study

A prospective prognostic signature for pancreatic adenocarcinoma based on ubiquitination-related mRNA-lncRNA with experimental validation in vitro and vivo

Ubiquitination-related genes (URGs) exerted a crucial part in a variety of human disease disorders; however, their association with pancreatic adenocarcinoma (PAAD) had yet to be clearly described. We aimed to comprehensively characterize the contributions of URGs in PAAD through in silico analysis and experimental validation, and then identified a robust mRNA-lncRNA-based molecular prognostic panel for patients with PAAD using bulk RNA-sequencing and single-cell RNA-sequencing data. Initially, we collected the multi-omics data from TCGA platform to depict a comprehensive landscape of URGs in pan-cancer. Furthermore, we were accurate to PAAD for in-depth analysis. Significant differences of the activation of ubiquitination pathways and the expression of URGs were detected between normal and malignant cells. Unsupervised hierarchical clustering determined two PAAD subtypes with distinct clinical outcomes, ubiquitination pathway activities, immune microenvironment, and functional annotation characteristics. The expression profiles of ubiquitination-associated mRNAs and lncRNAs in the training and validation datasets were utilized to develop and verify a novel ubiquitination-related mRNA-lncRNA prognostic panel, which had a satisfied prediction efficiency. Our ubiquitination-associated model could function as an effective prognostic index and outperformed four other recognized panels in evaluating PAAD patients' survival status. Tumor immune microenvironment, mutation burden, and chemotherapy response were intensively explored to demonstrate the underlying mechanism of prognostic difference according to our panel. Our findings also revealed that FTI-277, a farnesyltransferase inhibitor, had a better curative effect in high-risk patients, while MK-2206, an Akt allosteric inhibitor, had a superior therapeutic effect in low-risk patients. The real-time PCR results uncovered the RNA expression of AC005062.1 in all the three PAAD cell lines was elevated several thousandfold. In conclusion, our URGs-based classification panel could be triumphantly served as a prediction tool for survival evaluation in patients with PAAD, and the genes in this panel could be developed as a potential target in PAAD therapy.

A novel immune checkpoint score system for prognostic evaluation in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) remains a lethal malignancy making the detection of novel prognostic biomarkers urgent. Limited studies have investigated the predictive capability of immune checkpoints in PAAD.

METHOD

Gene expression data and correlative clinical information of PAAD cohort were obtained from public databases, including TCGA, ICGC, GTEX and GEO databases. Risk factors were screened and used to establish a risk score model through LASSO and Cox regression analyses. The prognostic ability of the risk score model was demonstrated. The association between risk score with immune cells infiltration, immune checkpoint genes expression, immunogenic cell death, somatic mutations and signaling pathways enrichment were analysed. scRNA-seq data were collected to confirmed the immune checkpoints expression in PAAD samples. The prognosis prediction ability of OX40/TNFRSF4 was identified. The mRNA and protein expression of OX40 in our clinical specimens were examined by RT-PCR and IHC method and its prognosis ability was verified.

RESULTS

First of all, the difference of immune microenvironment between pancreatic cancer and adjacent tissues was shown. A risk score system based on three immune checkpoints (OX40, TNFSF14 and KIR3DL1) was established. The risk score model was an independent prognostic factor and performed well regarding overall survival (OS) predictions among PAAD patients. A nomogram was established to facilitate the risk model application in clinical prognosis. Immune cells including naive B cells, CD8⁺ T cells and Tregs were negatively correlated with the risk score. The risk score was associated with expression of immune checkpoint genes, immunogenic cell death related genes and somatic mutations. Glycolysis processes, IL-2-STAT5, IL-6-STAT3, and mTORC1 signaling pathways were enriched in the high-risk score group. Furthermore, scRNA-seq data confirmed that TNFRSF4, TNFSF14 and KIR3DL1 were expressed on immune cells in PAAD samples. We then identified OX40 as an independent prognosis-related gene, and a higher OX40 expression was associated with increased survival rate and immune environment change. In 84 PAAD clinical specimens collected from our center, we confirmed that higher OX40 mRNA expression levels were related to a good prognosis. The protein expression of OX40 on tumor-infiltrating immune cells (TIICs), endothelial cells and tumor cells was verified in PAAD tissues by immunohistochemistry (IHC) method.

CONCLUSIONS

Overall, our findings strongly suggested that the three-immune checkpoints score system might be useful in the prognosis and design of personalized treatments for PAAD patients. Finally, we identified OX40 as an independent potential biomarker for PAAD prognosis prediction.

Semaphorins and their receptors in pancreatic cancer: Mechanisms and therapeutic opportunities

Pancreatic cancer (PC) is a malignant tumor with high malignancy that is difficult to diagnose and treat. PC is a major medical problem because of its low early diagnosis rate, high surgical mortality rate, low cure rate, and expensive related testing cost. Therefore, the significance of finding new markers for PC is self-evident. Semaphorins (Semas) have been shown to affect angiogenesis and lymphangiogenesis and can also directly affect the behavior of tumor cells. The expression and related action targets of its family members on PC are summarized in this review.

A Novel Immune-Related Prognostic Signature Predicting Survival in Patients with Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) carries the lowest survival rate of all major organ cancers, which is of dismal prognosis and high mortality rate. Thus, the present study attempted to identify a few novel prognostic biomarkers and establish an immune-related prognostic signature which could predict the prognosis of PAAD. Four prognostic immune-related genes (IRGs) including S100A6, S100A10, S100A16, and SDC1 were screened by differentially expressed gene (DEG) identification and weighted gene coexpression network analysis (WGCNA). Subsequent analysis proved the high expression of these IRGs in PAAD tissues, suggested by TCGA-PAAD data, merged microarray-acquired dataset (MMD), GEPIA, and Oncomine webtool. By using MMD and TCGA-PAAD data, S100A6 (MMD: AUC = 0.897; TCGA: AUC = 0.843), S100A10 (MMD: AUC = 0.880; TCGA: AUC = 0.780), S100A16 (MMD: AUC = 0.878; TCGA: AUC = 0.838), and SDC1 (MMD: AUC = 0.885; TCGA: AUC = 0.812) exhibited excellent diagnostic efficiency for PAAD. By conducting connectivity map (CMap) analysis, we concluded that three molecule drugs (sulpiride, famotidine, and nalidixic acid) might have worked in the treatment of PAAD. Then, an immune-related prognostic index was constructed, which was validated as an independent prognostic factor for PAAD patients (P=0.004). We further constructed a nomogram by using this immune-related signature and age, the prognostic value of which was validated by using concordance index (C-index = 0.780) and area under curve (AUC = 0.909). Moreover, the immune-related prognostic signature was associated with response to anti-PD-1/L1 immunotherapy. To sum up, four IRGs were screened out and verified to be novel immune-related prognostic biomarkers in PAAD. Besides, sulpiride, famotidine, and nalidixic acid might be potential choices in the treatment of PAAD. An immune-related signature was established to show great potential for prognosis prediction for PAAD, independently, which might guide more effective immunotherapy strategies. A nomogram is further established by using this immune-related prognostic index, which might contribute to more effective prognosis prediction in PAAD patients.

Construction of a Tumor Immune Microenvironment-Related Prognostic Model in BRAF-Mutated Papillary Thyroid Cancer

BRAF mutation is a representative oncogenic mutation, with a frequency of 60% in papillary thyroid carcinoma (PTC), but the reasons for the poor prognosis and more aggressive course of BRAF-mutated PTC are controversial. Tumor immune microenvironment (TIME) is an essential factor permitting the development and progression of malignancy, but whether TIME participates in the prognosis of BRAF-mutated PTC has not yet been reported. The primary goal of the present study was to provide a comprehensive TIME-related prognostic model to increase the predictive accuracy of progression-free survival (PFS) in patients with BRAF-mutated PTC. In this study, we analyzed the mRNA-seq data and corresponding clinical data of PTC patients obtained from the TCGA database. By calculating the TIME scores (immune score, stromal score and ESTIMATE score), the BRAF mutation group (n=237) was dichotomized into the high- and low-score groups. By functional analysis of differentially expressed genes (DEGs) in different high/low score groups, we identified 2 key TIME-related genes, HTR3A and NIPAL4, which affected PFS in BRAF-mutated PTC. A risk scoring system was developed by multivariate Cox analysis based on the abovementioned 2 TIME-related genes. Then, the BRAF-mutated cohort was divided into the high- and low-risk groups using the median risk score as a cutoff. A high risk score correlated positively with a higher HTR3A/NIPAL4 expression level but negatively with PFS in BRAF-mutated PTC. Ultimately, a nomogram was constructed by combining risk score with clinical parameter (Tumor stage), and the areas under the ROC curve (AUCs) of the nomogram for predicting 1-, 3- and 5-year PFS were then calculated and found to be 0.694, 0.707 and 0.738, respectively, indicating the improved accuracy and clinical utility of the nomogram versus the risk score model in the BRAF-mutated PTC cohort. Moreover, we determined the associations between prognostic genes or risk score and immune cell infiltration by two-way ANOVA. In the high-risk score, high HTR3A expression, and high NIPAL4 expression groups, higher infiltration of immune cells was found. Collectively, these findings confirm that the nomogram is effective in predicting the outcome of BRAF-mutated PTC and will add a spatial dimension to the developing risk stratification system.

Construction of a ceRNA Network and Analysis of Tumor Immune Infiltration in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is characterized by high malignancy, frequent metastasis, and recurrence with an unfavorable prognosis. This study is aimed at constructing a prognostic model for tumor-infiltrating immune cells and a competing endogenous RNA (ceRNA) network in PAAD and analyzing susceptibilities of chemotherapy and immunotherapy of PAAD. Gene expression profiles and clinical information of PAAD were downloaded from The Cancer Genome Atlas (TCGA) database and divided into the tumor group and the normal group. A total of five PAAD survival-related key genes in the ceRNA network and three survival-related immune infiltrating cells were uncovered, and two survival risk models and nomograms were constructed. The efficiency and performance of the two models were verified using multi-index area under the curve analysis at different time points, decision curve analysis, and calibration curves. Co-expression analysis showed that LRRC1, MIR600HG, and RNF166 in the ceRNA network and tumor-infiltrating immune cells including CD8 T cells and M1 macrophages were likely related to the PAAD prognosis, and the expression of key ceRNA-related genes was experimently validated in tissues and cell lines by RT-qPCR. Patients with low risk scores for key genes in the ceRNA network displayed a positive response to anti-programmed death-1 (PD-1) treatment and greater sensitivity to chemotherapeutic drugs such as docetaxel, lapatinib, and paclitaxel. More importantly, our results suggested that the IC50 values of gemcitabine in PAAD were not significantly different between the high and low risk groups. The expression levels of immune checkpoints were significantly different in the high-risk and low-risk groups. The prognostic model, nomogram, and drug analysis may provide an essential reference for PAAD patient management in the clinic.

Up-regulation of S100P predicts the poor long-term survival and construction of prognostic signature for survival and immunotherapy in patients with pancreatic cancer

Pancreatic cancer is associated with a high mortality rate, and the prognosis is positively related to immune status. In this study, we constructed a prognostic signature from survival- and immune-related genes (IRGs) to guide treatment and assess prognosis of patients with pancreatic cancer. The transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) database, and IRGs were extracted from the ImmPort database. Univariate and LASSO regression analysis were used to obtain survival-related IRGs. Finally, the prognostic signature was constructed using multivariate regression analysis. The laboratory experiments were conducted to verify the key IRG expression. Immune cells infiltration was analyzed using the CIBERSORT algorithm and TIMER database. Prognostic signature containing four IRGs (ADA2, TLR1, PTPN6, S100P) was constructed with good predictive performance; in particular, S100P played a significant role in the immune microenvironment, and tumorigenesis of pancreatic cancer. Moreover, we found that CD8⁺ T cell and activated CD4⁺ memory T cell tumor infiltration was lower in the high-risk group, while high-risk score correlated positively with higher tumor mutational burden, and the higher half inhibitory centration 50 of chemotherapeutic agents Docetaxel and Sunitinib. In summary, this study identified and constructed an immune-related prognostic signature that can predict overall survival, besides suggests that S100P was a novel immune-related biomarker. We hope that this signature will aid the identification of new biomarkers for the individualized immunotherapy of pancreatic cancer.

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

Objective: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Although progress has been made in the treatment of PDAC, its prognosis remains unsatisfactory. This study aimed to develop novel prognostic genes related to glycolysis in PDAC and to apply these genes to new risk stratification.

Methods: In this study, based on the Cancer Genome Atlas (TCGA) PAAD cohort, the expression level of glycolysis-related gene at mRNA level in PAAD and its relationship with prognosis were analyzed. Non-negative matrix decomposition (NMF) clustering was used to cluster PDAC patients according to glycolytic genes. Prognostic glycolytic genes, screened by univariate Cox analysis and LASSO regression analysis were established to calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group and the low-risk group were analyzed, and the signal pathway was further enriched to analyze the correlation between glycolysis genes. In addition, based on RNA-seq data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC samples, and ESTIMATE was used to calculate the immune score of the samples.

Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples were divided into two clusters by NMF cluster analysis. Survival analysis showed that PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with cluster 2 samples (P < 0.001). A risk prediction model based on 11 glycolysis genes was constructed, according to which patients were divided into two groups, with significantly poorer prognosis in high-risk group than in low-risk group (P < 0.001). Both internal validation and external dataset validation demonstrate good predictive ability of the model (AUC = 0.805, P < 0.001; AUC = 0.763, P < 0.001). Gene aggregation analysis showed that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk group showed immunosuppressed status and infiltrated by relatively more macrophages and less CD8+T cell.

Conclusions: These findings suggested that the gene signature based on glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value for PDAC.

Three Immune-Related Prognostic mRNAs as Therapeutic Targets for Pancreatic Cancer

Objective: Pancreatic cancer is a highly lethal malignancy globally. This study aimed to probe and validate immune-related prognostic mRNAs as therapeutic targets for pancreatic cancer.

Methods: Gene transcriptome data of pancreatic cancer and normal pancreas were retrieved from TCGA-GTEx projects. Two thousand four hundred and ninety-eight immune-related genes were obtained from the IMMUPORT database. Abnormally expressed immune-related genes were then identified. Under univariate and multivariate cox models, a gene signature was constructed. Its predictive efficacy was assessed via ROCs. The interactions between the 21 genes were analyzed by Spearson analysis and PPI network. Using the GEPIA and The Human Protein Atlas databases, their expression and prognostic value were evaluated. The TIMER database was utilized to determine the relationships between MET, OAS1, and OASL mRNAs and immune infiltrates. Finally, their mRNA expression was externally verified in the GSE15471 and GSE62452 datasets.

Results: An immune-related 21-gene signature was developed for predicting patients' prognosis. Following verification, this signature exhibited the well predictive performance. There were physical and functional interactions between them. MET, OAS1, and OASL mRNAs were all up-regulated in pancreatic cancer and associated with unfavorable prognosis. They showed strong correlations with tumor progression. Furthermore, the three mRNAs were distinctly associated with immune infiltrates. Their up-regulation was confirmed in the two external datasets.

Conclusion: These findings identified three immune-related prognostic mRNAs MET, OAS1, and OASL, which may assist clinicians to choose targets for immunotherapy and make personalized treatment strategy for pancreatic cancer patients.

A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.

Integrate analysis of the promote function of Cell division cycle-associated protein family to pancreatic adenocarcinoma

Background: The cell division cycle-associated (CDCA) protein family plays a pivotal role in the regulation of the cell cycle during tumorigenesis and predicts the prognosis of tumors, but an analysis of these proteins in pancreatic adenocarcinoma (PAAD) is still lacking. Methods: Oncomine and GEPIA were used to observe the expression and prognostic value of eight CDCAs in pan-cancer. Univariate Cox analysis of single CDCAs and multivariate Cox analysis of all eight CDCAs were performed to evaluate the integrated prognostic value of CDCAs, and the results are displayed as hazard ratios (HRs) and 95% confidence intervals (95% CIs). K-M plots and receiver operating characteristics curves were used to display the predicted function and accuracy of CDCAs to determine the risk score. Annotation of CDCA-related genes, gene sets enrichment analysis (GSEA) and gene sets variation analysis (GSVA) were performed to reveal the CDCAs that impact biological processes. Results: CDCAs expression in most tumors is higher than that in normal tissues and is associated with a poor prognosis. Regarding PAAD, increased CDCA expression along with advanced PAAD tumor stage, NUF2, CDCA2, CDCA3, CDCA4 and CDCA5 expression are risk factors for poor prognosis, while CBX2 expression is a protective factor (P < 0.05). The integrated prognostic value of CDCAs in PAAD patients was validated by SurvExpress in the TCGA-PAAD cohort (P < 0.001, HR = 2.16, 95% CI = 1.41-3.3) and the ICGC-PACA cohort (P < 0.001, HR = 2.56, 95% CI = 1.73-3.79). Genetic alteration and DNA methylation of CDCAs might not affect the prognosis of PAAD patients. After comparing high- and low-risk groups separated by CDCA risk scores, the activated pathways were revealed and included the cell cycle, DNA repair, P53, MYC-targets, E2F-targets and PI3K pathways. Conclusion: CDCAs can predict the OS prognosis of PAAD patients. The cell cycle, DNA repair, E2F, P53 and PI3K signaling pathways, in which CDCAs are involved, impact the tumorigenesis of PAAD.

Modulating barriers of tumor microenvironment through nanocarrier systems for improved cancer immunotherapy: a review of current status and future perspective

Cancer immunotherapy suppresses and destroys tumors by re-activating and sustaining the tumor-immune process, and thus improving the immune response of the body to the tumor. Immunotherapeutic strategies are showing promising results in pre-clinical and clinical trials, however, tumor microenvironment (TME) is extremely immunosuppressive. Thus, their translation from labs to clinics still faces issues. Recently, nanomaterial-based strategies have been developed to modulate the TME for robust immunotherapeutic responses. The combination of nanotechnology with immunotherapy potentiates the effectiveness of immunotherapy by increasing delivery and retention, and by reducing immunomodulation toxicity. This review aims to highlight the barriers offered by TME for hindering the efficiency of immunotherapy for cancer treatment. Next, we highlight various nano-carriers based strategies for modulating those barriers for achieving better therapeutic efficacy of cancer immunotherapy with higher safety. This review will add to the body of scientific knowledge and will be a good reference material for academia and industries.

A signature of 18 immune-related gene pairs to predict the prognosis of pancreatic cancer patients

Pancreatic cancer is one of the most lethal malignancies. With the promising prospects conveyed by immunotherapy in cancers, we aimed to construct an immune‐related gene pairs (IRGPs) signature to predict the prognosis of pancreatic cancer patients. We downloaded clinical and transcriptional data of pancreatic cancer patients from The Cancer Genome Atlas data set as the training group and GSE57495 data set as the verification group. We filtered immune‐related transcriptional data by IMMPORT. With the assistance of lasso penalized Cox regression, we constructed our prognostic IRGPs signature and divided all samples into high‐/low‐risk groups by receiver operating characteristic curve for further comparisons. The comparisons between high‐ and low‐risk groups including survival rate, multivariate, and univariate Cox proportional‐hazards analysis, infiltration of immune cells, and Gene Set Enrichment Analysis (GSEA). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) are facilitated to analyze the proceedings in which our IRGPs signature may involve in. The results revealed that 18 IRGPs were defined as our prognostic signature. The prognostic value of this IRGPs signature was verified from the GSE57495 data set. We further demonstrated the independent prognostic value of this IRGPs signature. The contents of six immune cells between high‐/low‐risk groups were different, which was associated with the progression of diverse cancers. Results from GO, KEGG, and GSEA revealed that this IRGPs signature was involved in extracellular space, immune response, cancer pathways, cation channel, and gated channel activities. Evidently, this IRGPs signature will provide remarkable value for the therapy of pancreatic cancer patients.