Apoptosis resistance is to a large extent a major obstacle leading to chemotherapy failure during cancer treatment. Bypassing the apoptotic pathway to induce cancer cell death is considered to be a promising approach to overcoming this problem. Necroptosis is a regulated necrotic cell death modality in a caspase-independent fashion and is mainly mediated by Receptor-Interacting Protein 1 (RIP1), RIP3, and Mixed Lineage Kinase Domain-Like (MLKL). Necroptosis serves as an alternative mode of programmed cell death overcoming apoptosis resistance and may trigger and amplify antitumor immunity in cancer therapy. The role of necroptosis in cancer is complicated. The expression of key regulators of the necroptotic pathway is generally downregulated in cancer cells, suggesting that cancer cells may also evade necroptosis to survive; however, in certain types of cancer, the expression level of key mediators is elevated. Necroptosis can elicit strong adaptive immune responses that may defend against tumor progression; however, the recruited inflammatory response may also promote tumorigenesis and cancer metastasis, and necroptosis may generate an immunosuppressive tumor microenvironment. Necroptosis also reportedly promotes oncogenesis and cancer metastasis despite evidence demonstrating its antimetastatic role in cancer. In addition, necroptotic microenvironments can direct lineage commitment to determine cancer subtype development in liver cancer. A plethora of compounds and drugs targeting necroptosis exhibit potential antitumor efficacy, but their clinical feasibility must be validated. Better knowledge of the necroptotic pathway mechanism and its physiological and pathological functions is urgently required to solve the remaining mysteries surrounding the role of necroptosis in cancer. In this review, we briefly introduce the molecular mechanism and characteristics of necroptosis, the interplay between necroptosis and other cell death mechanisms, crosstalk of necroptosis and metabolic signaling and detection methods. We also summarize the intricate role of necroptosis in tumor progression, cancer metastasis, prognosis of cancer patients, cancer immunity regulation, cancer subtype determination and cancer therapeutics.

This article is the series of methodology of clinical prediction model construction (total 16 sections of this methodology series). The first section mainly introduces the concept, current application status, construction methods and processes, classification of clinical prediction models, and the necessary conditions for conducting such researches and the problems currently faced. The second episode of these series mainly concentrates on the screening method in multivariate regression analysis. The third section mainly introduces the construction method of prediction models based on Logistic regression and Nomogram drawing. The fourth episode mainly concentrates on Cox proportional hazards regression model and Nomogram drawing. The fifth Section of the series mainly introduces the calculation method of C-Statistics in the logistic regression model. The sixth section mainly introduces two common calculation methods for C-Index in Cox regression based on R. The seventh section focuses on the principle and calculation methods of Net Reclassification Index (NRI) using R. The eighth section focuses on the principle and calculation methods of IDI (Integrated Discrimination Index) using R. The ninth section continues to explore the evaluation method of clinical utility after predictive model construction: Decision Curve Analysis. The tenth section is a supplement to the previous section and mainly introduces the Decision Curve Analysis of survival outcome data. The eleventh section mainly discusses the external validation method of Logistic regression model. The twelfth mainly discusses the in-depth evaluation of Cox regression model based on R, including calculating the concordance index of discrimination (C-index) in the validation data set and drawing the calibration curve. The thirteenth section mainly introduces how to deal with the survival data outcome using competitive risk model with R. The fourteenth section mainly introduces how to draw the nomogram of the competitive risk model with R. The fifteenth section of the series mainly discusses the identification of outliers and the interpolation of missing values. The sixteenth section of the series mainly introduced the Zhou et al. Clinical prediction models with R

CEA and CA125 have the potential to be applied as biomarkers in Lewis negative patients with pancreatic cancer. CEA and CA125 should be routinely measured for all patients with pancreatic cancer.

The current study demonstrated the CRP/Alb ratio may serve as a significant and promising inflammatory prognostic score in pancreatic cancer. An elevated CRP/Alb ratio is an independent factor for poor prognosis with the cutoff value of 0.180.

Baseline NLR and postchemotherapy NLR change may serve as potential biomarkers for overall survival in patients with advanced pancreatic cancer undergoing chemotherapy.

Purpose The European Neuroendocrine Tumor Society (ENETS) and the American Joint Committee on Cancer (AJCC) staging classifications are two widely used systems in managing pancreatic neuroendocrine tumors. However, there is no universally accepted system. Methods An analysis was performed to evaluate the application of the ENETS and AJCC staging classifications using the SEER registry (N = 2,529 patients) and a multicentric series (N = 1,143 patients). A modified system was proposed based on analysis of the two existing classifications. The modified system was then validated. Results The proportion of patients with AJCC stage III disease was extremely low for both the SEER series (2.2%) and the multicentric series (2.1%). For the ENETS staging system, patients with stage I disease had a similar prognosis to patients with stage IIA disease, and patients with stage IIIB disease had a lower hazard ratio for death than did patients with stage IIIA disease. We modified the ENETS staging classification by maintaining the ENETS T, N, and M definitions and adopting the AJCC staging definitions. The proportion of patients with stage III disease using the modified ENETS (mENETS) system was higher than that of the AJCC system in both the SEER series (8.9% v 2.2%) and the multicentric series (11.6% v 2.1%). In addition, the hazard ratio of death for patients with stage III disease was higher than that for patients with stage IIB disease. Moreover, statistical significance and proportional distribution were observed in the mENETS staging classification. Conclusion An mENETS staging classification is more suitable for pancreatic neuroendocrine tumors than either the AJCC or ENETS systems and can be adopted in clinical practice.

Cell-free circulating tumor DNA (ctDNA) in plasma has been used as a potential noninvasive biomarker for various tumors. Our study was performed to evaluate the clinical implications of ctDNA detection in patients with metastatic pancreatic cancer. First, we attempted to prospectively screen a panel of 60 genes in cell-free DNA (cfDNA) from ten metastatic pancreatic cancer patients via exome sequencing. Second, droplet digital PCR (ddPCR) was used to identify potential mutations in a cohort of 188 patients with metastatic pancreatic cancer. Finally, to preliminary evaluate the potential role of ctDNA in monitoring tumor responses following chemotherapy, we detected the presence of ctDNA in serial plasma samples from 13 metastatic pancreatic cancer patients (Clinical trial: NCT02017015). The analysis revealed five somatic mutations at BRCA2, EGFR, KDR and ERBB2 gene loci. The frequencies of ctDNA mutation at BRCA2, KDR, EGFR, ERBB2 exon17 and ERBB2 exon27 were 11.7%, 13.8%, 13.3%, 13.3% and 6.4% respectively. Univariate and multivariate analyses identified the ERBB2 exon17 mutation (p = 0.035, HR = 1.61) as an independent factor associated with overall survival among metastatic pancreatic cancer patients. Furthermore, the rate of coincident detection of ctDNA and response to treatment as assessed by CT imaging was 76.9% (10 of 13 cases), and the presence of ctDNA provided the earliest measure of treatment in 6 of 10 patients (60%). ctDNA sequencing may have clinical value for determining metastatic pancreatic cancer treatment and monitoring the tumor response.

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