A Novel Model Based on Necroptosis-Related Genes for Predicting Prognosis of Patients With Prostate Adenocarcinoma

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma

Background

Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC.

Methods

Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion).

Results

Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion.

Conclusion

The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.

Caspase-Linked Programmed Cell Death in Prostate Cancer: From Apoptosis, Necroptosis, and Pyroptosis to PANoptosis

Prostate cancer (PCa) is a complex disease and the cause of one of the highest cancer-related mortalities in men worldwide. Annually, more than 1.2 million new cases are diagnosed globally, accounting for 7% of newly diagnosed cancers in men. Programmed cell death (PCD) plays an essential role in removing infected, functionally dispensable, or potentially neoplastic cells. Apoptosis is the canonical form of PCD with no inflammatory responses elicited, and the close relationship between apoptosis and PCa has been well studied. Necroptosis and pyroptosis are two lytic forms of PCD that result in the release of intracellular contents, which induce inflammatory responses. An increasing number of studies have confirmed that necroptosis and pyroptosis are also closely related to the occurrence and progression of PCa. Recently, a novel form of PCD named PANoptosis, which is a combination of apoptosis, necroptosis, and pyroptosis, revealed the attached connection among them and may be a promising target for PCa. Apoptosis, necroptosis, pyroptosis, and PANoptosis are good examples to better understand the mechanism underlying PCD in PCa. This review aims to summarize the emerging roles and therapeutic potential of apoptosis, necroptosis, pyroptosis, and PANoptosis in PCa.

Revealing the potential of necroptosis-related genes in prognosis, immune characteristics, and treatment strategies for head and neck squamous cell carcinoma

Necroptosis is a recently discovered apoptotic mechanism that has been linked to tumor formation, prognosis, and treatment response. However, the relationship between the TME and NRGs remains unclear. In this study, we analyzed the expression patterns of NRGs in 769 HNSCC cases from two distinct data sets. Our findings revealed distinct genetic groups and a correlation between patient clinical features, prognosis, TME cell infiltration characteristics, and NRG alterations. We then developed an NRG model to predict OS and confirmed its accuracy in predicting OS in HNSCC patients. Moreover, we have devised a precise nomogram that enhances the clinical utility of the NRG model substantially. The low-risk group had a better OS, and they were associated with immune suppression, more mutated genes, and higher TIDE scores. The risk score also had a significant correlation with the CSC index and susceptibility to anti-tumor agents. Our study provides insights into how NRGs affect prognosis, clinically significant features, TME, and immunotherapy response in HNSCC. With a better knowledge of NRGs in HNSCC, we could assess the prognosis and develop immunotherapy regimens that are more successful at opening up new doors.

A novel angiogenesis-related scoring model predicts prognosis risk and treatment responsiveness in diffuse large B-cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous disease with varying therapeutic responses and prognoses. Angiogenesis is a crucial factor in lymphoma growth and progression, but no scoring model based on angiogenesis-related genes (ARGs) has been developed for prognostic evaluation of DLBCL patients. In this study, we used univariate Cox regression to identify prognostic ARGs and found two distinct clusters of DLBCL patients in the GSE10846 dataset based on the expression of these prognostic ARGs. These two clusters had different prognoses and immune cell infiltration. Using LASSO regression analysis, we constructed a novel seven-ARG-based scoring model in GSE10846 dataset, and it was further validated in the GSE87371 dataset. The DLBCL patients were divided into high- and low-score groups based on the median risk score as a cut-off. The high-score group had a worse prognosis and showed higher expression of immune checkpoints, M2 macrophages, myeloid-derived suppressor cells, and regulatory T cells, indicating a stronger immunosuppressive environment. DLBCL patients in high-score group were resistant to doxorubicin and cisplatin, which are components of frequently used chemotherapy regimens, but more sensitive to gemcitabine and temozolomide. Using RT-qPCR, we found that two candidate risk genes, RAPGEF2 and PTGER2, were over-expressed in DLBCL tissues compared with control tissues. Taken together, the ARG-based scoring model provides a promising direction for the prognosis and immune status of DLBCL patients, and benefits the development of personalized treatment for DLBCL patients.

Identification of necroptosis-related long non-coding RNAs prognostic signature and the crucial lncRNA in bladder cancer

BACKGROUND

Research on the relationships between long non-coding RNAs (lncRNAs) and cancer is attractive and has progressed very rapidly. Necroptosis-related biomarkers can potentially be used for predicting the prognosis of cancer patients. This study aimed to establish a necroptosis-related lncRNA (NPlncRNA) signature to predict the prognosis of patients with bladder cancer (BCa).

METHODS

First, NPlncRNAs were identified using Pearson correlation analysis and machine learning algorithms, including SVM-RFE, least absolute shrinkage and selection operator (LASSO) regression, and random forest. The prognostic NPlncRNA signature was constructed using univariate and multivariate Cox regression analyses and the diagnostic efficacy and clinically predictive efficiency were evaluated and validated. The biological functions of the signature were analysed using gene set enrichment analysis (GSEA) and functional enrichment analysis. We further integrated the RNA-seq dataset (GSE133624) with our outcomes to reveal the crucial NPlncRNA that was functionally verified by assessing cell viability, proliferation, and apoptosis in BCa cells.

RESULTS

The prognostic NPlncRNAs signature was composed of PTOV1-AS2, AC083862.2, MAFG-DT, AC074117.1, AL049840.3, and AC078778.1, and a risk score based on this signature was proven to be an independent prognostic factor for the BCa patients, indicated by poor overall survival (OS) of patients in the high-risk group. Additionally, the NPlncRNAs signature had a higher diagnostic validity than that of other clinicopathological variables, with a greater area under the receptor operating characteristic and concordance index curves. A nomogram established by integrating clinical variables and risk score confirmed that the signature can accurately predict the OS of patients and has high clinical practicability. Functional enrichment analysis and GSEA revealed that some cancer-related and necroptosis-related pathways were enriched in high-risk groups. The crucial NPlncRNA MAFG-DT was associated with poor prognosis and was highly expressed in BCa cells. MAFG-DT silencing notably inhibited proliferation and enhanced apoptosis of BCa cells.

CONCLUSIONS

A novel prognostic NPlncRNAs signature was identified in BCa in this study, which provides potential therapeutic targets among which MAFG-DT plays critical roles in the tumorigenesis of BCa.

Integrated single-cell and bulk RNA sequencing analysis identifies a prognostic signature related to ferroptosis dependence in colorectal cancer

Ferroptosis is an iron-dependent form of cell death induced by lipid oxidation with an essential role in diseases, including cancer. Since prognostic value of ferroptosis-dependent related genes (FDRGs) in colorectal cancer (CRC) remains unclear, we explored the significance of FDRGs in CRC through comprehensive single-cell analysis. We downloaded the GSE161277 dataset for single-cell analyses and calculated the ferroptosis-dependent gene score (FerrScore) for each cell type. According to each cell type-specific median FerrScore, we categorized the cells into low- and high-ferroptosis groups. By analyzing differentially-expressed genes across the two groups, we identified FDRGs. We further screened these prognosis-related genes used to develop a prognostic signature and calculated its correlation with immune infiltration. We also compared immune checkpoint gene efficacy among different risk groups, and qRT-PCR was performed in colorectal normal and cancer cell lines to explore whether the signature genes could be used as clinical prognostic indicators. In total, 523 FDRGs were identified. A prognostic signature including five signature genes was constructed, and patients were divided into two risk groups. The high-risk group had poor survival rates and displayed high levels of immune infiltration. Our newly developed ferroptosis-based prognostic signature possessed a high predictive ability for CRC.

High expression of KNL1 in prostate adenocarcinoma is associated with poor prognosis and immune infiltration

Prostate adenocarcinoma (PRAD) is a common malignancy with increasing morbidity and mortality. Kinetochore scaffold 1 (KNL1) has been reported to be involved in tumor progression and prognosis in other tumors, but its role in PRAD has not been reported in detail. KNL1 expression analysis, clinicopathological parameters analysis, prognostic correlation analysis, molecular interaction network and functional abdominal muscle analysis and immune infiltration analysis by using multiple online databases and downloaded expression profile. The results suggest that KNL1 is highly expressed in PRAD, which is associated with worse prognosis in PRAD patients. KnL1-related genes are highly enriched in mitotic function, which is considered to be highly related to the development of cancer. Finally, KNL1 expression is associated with a variety of tumor infiltrating immune cells, especially Treg and Th2 cells. In conclusion, our findings provide preliminary evidence that KNL1 may be an independent prognostic predictor of PRAD and is associated with immune infiltration.

Establishment and validation of a predictive model for bone metastasis in prostate cancer patients based on multiple immune inflammatory parameters

OBJECTIVE

This study aims to establish and validate a predictive model for bone metastasis in prostate cancer patients based on multiple immune inflammatory parameters.

METHODS

In this retrospective study, 162 prostate cancer patients who met the inclusion criteria were selected by Urology Surgery, Shaanxi Provincial People's Hospital. Based on the medical record number of patients and the random number table method, 40 patients were randomly included in a validation group, and the rest were in a modeling group. The patients in the modeling group were divided into a metastatic group (n=67) and a non-metastatic group (n=55) according to the whole-body bone imaging results.

RESULTS

The predictive model was established based on the results of Logistics regression analysis: Logit (P) = -5.341 + 0.930*total Gleason score + 1.426*total prostate specific antigen + 0.836*neutrophil-lymphocyte ratio + 0.896*platelet lymphocyte ratio + 0.641*lymphocyte/monocyte ratio + 0.750*albumin/globulin ratio. ROC analysis showed that the areas under the curve of the predictive model for bone metastasis in the modeling and validation groups were 0.896 and 0.870, respectively. Hosmer-Lemeshow test showed that P=0.253, indicating a high degree of the fitting. External verification results showed that the C-index for predicting prostate cancer bone metastasis in the predictive model established in this study was 0.760 (95% CI: 0.670-0.851).

CONCLUSION

The bone metastasis predictive model based on the multiple immune inflammatory parameters (neutrophil-lymphocyte ratio, platelet lymphocyte ratio, lymphocyte/monocyte ratio and albumin/globulin ratio) in prostate cancer patients can reasonably predict the occurrence of bone metastasis and is well worth clinical application.

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.

Analysis and identification of the necroptosis landscape on therapy and prognosis in bladder cancer

Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system, but the current therapeutic strategy based on chemotherapy and immune checkpoint inhibitor (ICI) therapy cannot meet the treatment needs, mainly owing to the endogenous or acquired apoptotic resistance of cancer cells. Targeting necroptosis provides a novel strategy for chemotherapy and targeted drugs and improves the efficacy of ICIs because of strong immunogenicity of necroptosis. Therefore, we systemically analyzed the necroptosis landscape on therapy and prognosis in BLCA. We first divided BLCA patients from The Cancer Genome Atlas (TCGA) database into two necroptosis-related clusters (C1 and C2). Necroptosis C2 showed a significantly better prognosis than C1, and the differential genes of C2 and C1 were mainly related to the immune response according to GO and KEGG analyses. Next, we constructed a novel necroptosis-related gene (NRG) signature consisting of SIRT6, FASN, GNLY, FNDC4, SRC, ANXA1, AIM2, and IKBKB to predict the survival of TCGA-BLCA cohort, and the accuracy of the NRG score was also verified by external datasets. In addition, a nomogram combining NRG score and several clinicopathological features was established to more accurately and conveniently predict the BLCA patient’s survival. We also found that the NRG score was significantly related to the infiltration levels of CD8 T cells, NK cells, and iDC cells, the gene expression of CTLA4, PD-1, TIGIT, and LAG3 of TME, and the sensitivity to chemotherapy and targeted agents in BLCA patients. In conclusion, the NRG score has an excellent performance in evaluating the prognosis, clinicopathologic features, tumor microenvironment (TME), and therapeutic sensitivity of BLCA patients, which could be utilized as a guide for chemotherapy, ICI therapy, and combination therapy.

Establishment and validation of a clinicopathological prognosis model of gastroenteropancreatic neuroendocrine carcinomas

Background

Gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs) are a rare, highly malignant subset of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). However, how to predict the prognosis of GEP-NECs by clinical features is still under study. This study aims to establish and validate a nomogram model of overall survival (OS) in patients with GEP-NECs for predicting their prognosis.

Methods

We selected patients diagnosed with GEP-NECs from the Surveillance, Epidemiology, and End Results (SEER) database and two Chinese hospitals. After randomization, we divided the data in the SEER database into the train cohort and the test cohort at a ratio of 7:3 and used the Chinese cohort as the validation cohort. The Cox univariate and multivariate analyses were performed to incorporate statistically significant variables into the nomogram model. We then established a nomogram and validated it by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, the area under the curve (AUC), and the decision curve analysis (DCA) curve.

Results

We calculated the nomogram C-index as 0.797 with a 95% confidence interval (95% CI) of 0.783–0.815 in the train cohort, 0.816 (95% CI: 0.794–0.833) in the test cohort and 0.801 (95% CI: 0.784–0.827) in the validation cohort. Then, we plotted the calibration curves and ROC curves, and AUCs were obtained to verify the specificity and sensitivity of the model, with 1-, 3- and 5-year AUCs of 0.776, 0.768, and 0.770, respectively, in the train cohort; 0.794, 0.808, and 0.799 in the test cohort; 0.922, 0.925, and 0.947 in the validation cohort. The calibration curve and DCA curves also indicated that this nomogram model had good clinical benefits.

Conclusions

We established the OS nomogram model of GEP-NEC patients, including variables of age, race, sex, tumor site, tumor grade, and TNM stage. This model has good fitting, high sensitivity and specificity, and good clinical benefits.

A novel necroptosis-related gene signature associated with immune landscape for predicting the prognosis of papillary thyroid cancer

Background: Necroptosis, a type of programmed cell death, has been implicated in a variety of cancer-related biological processes. However, the roles of necroptosis-related genes in thyroid cancer yet remain unknown.

Methods: A necroptosis-related gene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis and Cox regression analysis. The predictive value of the prognostic signature was validated in an internal cohort. Additionally, the single-sample gene set enrichment analysis (ssGSEA) was used to examine the relationships between necroptosis and immune cells, immunological functions, and immune checkpoints. Next, the modeled genes expressions were validated in 96 pairs of clinical tumor and normal tissue samples. Finally, the effects of modeled genes on PTC cells were studied by RNA interference approaches in vitro.

Results: In this study, the risk signature of seven necroptosis-related genes was created to predict the prognosis of papillary thyroid cancer (PTC) patients, and all patients were divided into high- and low-risk groups. Patients in the high-risk group fared worse in terms of overall survival than those in the low-risk group. The area under the curve (AUC) of the receiving operating characteristic (ROC) curves proved the predictive capability of created signature. The risk score was found to be an independent risk factor for prognosis in multivariate Cox analysis. The low-risk group showed increased immune cell infiltration and immunological activity, implying that they might respond better to immune checkpoint inhibitor medication. Next, GEO database and qRT-PCR in 96 pairs of matched tumorous and non-tumorous tissues were used to validate the expression of the seven modeled genes in PTCs, and the results were compatible with TCGA database. Finally, overexpression of IPMK, KLF9, SPATA2 could significantly inhibit the proliferation, invasion and migration of PTC cells.

Conclusion: The created necroptosis associated risk signature has the potential to have prognostic capability in PTC for patient outcome. The findings of this study could pave the way for further research into the link between necroptosis and tumor immunotherapy.

A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma

As one of common malignancies, prostate adenocarcinoma (PRAD) has been a growing health problem and a leading cause of cancer-related death. To obtain expression and functional relevant RNAs, we firstly screened candidate hub mRNAs and characterized their associations with cancer. Eight deregulated genes were identified and used to build a risk model (AUC was 0.972 at 10 years) that may be a specific biomarker for cancer prognosis. Then, relevant miRNAs and lncRNAs were screened, and the constructed primarily interaction networks showed the potential cross-talks among diverse RNAs. IsomiR landscapes were surveyed to understand the detailed isomiRs in relevant homologous miRNA loci, which largely enriched RNA interaction network due to diversities of sequence and expression. We finally characterized TK1, miR-222-3p and SNHG3 as crucial RNAs, and the abnormal expression patterns of them were correlated with poor survival outcomes. TK1 was found synthetic lethal interactions with other genes, implicating potential therapeutic target in precision medicine. LncRNA SNHG3 can sponge miR-222-3p to perturb RNA regulatory network and TK1 expression. These results demonstrate that TK1:miR-222-3p:SNHG3 axis may be a potential prognostic biomarker, which will contribute to further understanding cancer pathophysiology and providing potential therapeutic targets in precision medicine.

Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils the Characteristics of the Immune Microenvironment and Prognosis Signature in Prostate Cancer

The immune microenvironment is a culmination of the collaborative effort of immune cells and is important in cancer development. The underlying mechanisms of the tumor immune microenvironment in regulating prostate cancer (PRAD) are unclear. In the current study, 144 natural killer cell-related genes were identified using differential expression, single-sample gene set enrichment analysis, and weighted gene coexpression network analysis. Furthermore, VCL, ACTA2, MYL9, MYLK, MYH11, TPM1, ACTG2, TAGLN, and FLNC were selected as hub genes via the protein-protein interaction network. Based on the expression patterns of the hub genes, endothelial, epithelial, and tissue stem cells were identified as key cell subpopulations, which could regulate PRAD via immune response, extracellular signaling, and protein formation. Moreover, 27 genes were identified as prognostic signatures and used to construct the risk score model. Receiver operating characteristic curves revealed the good performance of the risk score model in both the training and testing datasets. Different chemotherapeutic responses were observed between the low- and high-risk groups. Additionally, a nomogram based on the risk score and other clinical features was established to predict the 1-, 3-, and 5-year progression-free interval of patients with PRAD. This study provides novel insights into the molecular mechanisms of the immune microenvironment and its role in the pathogenesis of PARD. The identification of key cell subpopulations has a potential therapeutic and prognostic use in PRAD.

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis

Background

Necroptosis, a form of programmed cell death, is increasingly being investigated for its controversial role in tumorigenesis and progression. Necroptosis suppresses tumor formation and tumor development by killing tumor cells; however, the necrotic cells also promote tumor formation and tumor development via the immunosuppressive effect of necroptosis and inflammatory response caused by cytokine release. Thus, the exact mechanism of necroptosis in pan-cancer remains unknown.

Methods

The data of 11,057 cancer samples were downloaded from the TCGA database, along with clinical information, tumor mutation burden, and microsatellite instability information of the corresponding patients. We used the TCGA data in a pan-cancer analysis to identify differences in mRNA level as well as single nucleotide variants, copy number variants, methylation profiles, and genomic signatures of miRNA-mRNA interactions. Two drug datasets (from GDSC, CTRP) were used to evaluate drug sensitivity and resistance against necroptosis genes.

Results

Necroptosis genes were aberrantly expressed in various cancers. The frequency of necroptosis gene mutations was highest in lung squamous cell carcinoma. Furthermore, the correlation between necroptosis gene expression in the tumor microenvironment and immune cell infiltration varied for different cancers. High necroptosis gene expression was found to correlate with NK, Tfh, Th1, CD8_T, and DC cells. These can therefore be used as biomarkers to predict prognosis. By matching gene targets with drugs, we identified potential candidate drugs.

Conclusion

Our study showed the genomic alterations and clinical features of necroptosis genes in 33 cancers. This may help clarify the link between necroptosis and tumorigenesis. Our findings may also provide new approaches for the clinical treatment of cancer.

Identification and Validation of Prognostic Model for Pancreatic Ductal Adenocarcinoma Based on Necroptosis-Related Genes

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors with a poor prognosis. Recently, necroptosis has been reported to participate in the progression of multiple tumors. However, few studies have revealed the relationship between necroptosis and PDAC, and the role of necroptosis in PDAC has not yet been clarified.

Methods: The mRNA expression data and corresponding clinical information of PDAC patients were downloaded from the TCGA and GEO databases. The necroptosis-related genes (NRGs) were obtained from the CUSABIO website. Consensus clustering was performed to divide PDAC patients into two clusters. Univariate and LASSO Cox regression analyses were applied to screen the NRGs related to prognosis to construct the prognostic model. The predictive value of the prognostic model was evaluated by Kaplan-Meier survival analysis and ROC curve. Univariate and multivariate Cox regression analyses were used to evaluate whether the risk score could be used as an independent predictor of PDAC prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA) were used for functional enrichment analysis. Finally, using qRT-PCR examined NRGs mRNA expression in vitro.

Results: Based on the TCGA database, a total of 22 differential expressed NRGs were identified, among which eight NRGs (CAPN2, CHMP4C, PLA2G4F, PYGB, BCL2, JAK3, PLA2G4C and STAT4) that may be related to prognosis were screened by univariate Cox regression analysis. And CAPN2, CHMP4C, PLA2G4C and STAT4 were further selected to construct the prognostic model. Kaplan-Meier survival analysis and ROC curve showed that there was a significant correlation between the risk model and prognosis. Univariate and multivariate Cox regression analyses showed that the risk score of the prognostic model could be used as an independent predictor. The model efficacy was further demonstrated in the GEO cohort. Functional analysis revealed that there were significant differences in immune status between high and low-risk groups. Finally, the qRT-PCR results revealed a similar dysregulation of NRGs in PDAC cell lines.

Conclusion: This study successfully constructed and verified a prognostic model based on NRGs, which has a good predictive value for the prognosis of PDAC patients.

Development of a novel five-gene immune-related risk model for the prognosis evaluation of prostate adenocarcinoma patients

The interaction between the immune cells and the host immune system with the tumor cells is significantly associated with the initiation and progression of prostate adenocarcinoma (PRAD), whereas the application of immune-related genes (IRGs) for the prognosis evaluation of PRAD patients is still lacking. In this study, we aimed to identify IRGs with prognostic values and to develop a clinically effective risk model. Wilcoxon rank-sum test and univariate Cox analysis were applied to identify the differentially expressed immune-related genes (DEIRGs) related to the survival of PRAD patients. The Least absolute shrinkage and selection operator (LASSO) analysis was performed to identify the independent prognostic DEIRGs and to establish an immune risk score prognostic model. The reliability and veracity of the prognostic model were validated in PRAD patients from the internal cohort (The Cancer Genome Atlas, TCGA dataset) and the external cohort (International Cancer Genome Consortium, ICGC dataset), respectively. Six of the 193 identified DEIRGs were survival-associated in PRAD patients. Five prognostic DEIRGs (SLPI, NOX1, DES, BIRC5 and AMH) were selected to construct the immune-related prognostic model with optimal robustness. In the 2 independent cohorts we chose, PRAD patients could be effectively stratified according to our risk model. Patients with high risk scores had worse survival. Clinical correlation analysis proved that the risk score was associated with advanced clinicopathologic features. Multivariate analysis indicated that the risk model was an independent prognostic indicator. We also established a nomogram based on the risk score model for clinical application. Additionally, the risk score model was correlated with immune cell infiltration and reflected the status of the immune microenvironment. The prognostic value of the five immune-related genes used in the prognostic model was also validated. Our immune-related prognostic model was an effective tool that could not only serve as a predictor for prognosis, but also provide potential prognostic and therapeutic molecular biomarkers for optimizing personalized therapies in clinical practice.