BACKGROUND:Predicting survival is uniquely difficult in patients with pancreatic cancer who receive chemotherapy. The authors developed a systemic inflammation response index (SIRI) based on peripheral neutrophil, monocyte, and lymphocyte counts and evaluated the ability of the SIRI to predict the survival of patients with pancreatic cancer who received chemotherapy. METHODS: The SIRI was developed in a training set of 177 patients who had advanced pancreatic cancer and received palliative chemotherapy. The ability of the SIRI to predict a patient's survival after chemotherapy was validated in 2 independent cohorts (n 5 397). RESULTS: Compared with patients who had an SIRI <1.8, patients in the training cohort who had an SIRI 1.8 had a shorter time to progression (TTP) (hazard ratio [HR], 2.348; 95% confidence interval, 1.559-3.535; P 5 .003) and shorter overall survival (OS) (HR, 2.789; 95% confidence interval, 1.897-4.121; P < .001). Comparable TTP and OS findings were observed in 2 independent validation cohorts. Multivariate analysis confirmed that the SIRI was an independent prognostic factor for both TTP and OS. In addition, compared with no change, an increase in the SIRI at week 8 was associated with poor TTP and OS, whereas a decrease in the SIRI was associated with improved outcomes. In addition, high SIRI scores were correlated with higher serum levels of interleukin 10, C-C motif chemokine ligand 17 (CCL17), CCL18, and CCL22 and with a shortened TTP. CONCLUSIONS: The SIRI can be used to predict the survival of patients with pancreatic adenocarcinomas who receive chemotherapy, potentially allowing clinicians to improve treatment outcomes by identifying candidates for aggressive therapy. Cancer 2016;122:2158-67.

As genomics advances reveal the cancer gene landscape, a daunting task is to understand how these genes contribute to dysregulated oncogenic pathways. Integration of cancer genes into networks offers opportunities to reveal protein–protein interactions (PPIs) with functional and therapeutic significance. Here, we report the generation of a cancer-focused PPI network, termed OncoPPi, and identification of >260 cancer-associated PPIs not in other large-scale interactomes. PPI hubs reveal new regulatory mechanisms for cancer genes like MYC, STK11, RASSF1 and CDK4. As example, the NSD3 (WHSC1L1)–MYC interaction suggests a new mechanism for NSD3/BRD4 chromatin complex regulation of MYC-driven tumours. Association of undruggable tumour suppressors with drug targets informs therapeutic options. Based on OncoPPi-derived STK11-CDK4 connectivity, we observe enhanced sensitivity of STK11-silenced lung cancer cells to the FDA-approved CDK4 inhibitor palbociclib. OncoPPi is a focused PPI resource that links cancer genes into a signalling network for discovery of PPI targets and network-implicated tumour vulnerabilities for therapeutic interrogation.

Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets. Both traditional medicine-related approaches and modern drug discovery platforms have been used in an attempt to develop m6A-targeted drugs. Here, we provide an update of the latest findings on m6A modification and the critical roles of m6A modification in cancer progression, and we summarize rational sources for the discovery of m6A-targeted anticancer agents from traditional medicines and computer-based chemosynthetic compounds. This review highlights the potential agents targeting m6A modification for cancer treatment and proposes the advantage of artificial intelligence (AI) in the discovery of m6A-targeting anticancer drugs. Graphical abstract Three stages of m6A-targeting anticancer drug discovery: traditional medicine-based natural products, modern chemical modification or synthesis, and artificial intelligence (AI)-assisted approaches for the future.

Diffuse large B cell lymphoma (DLBCL) is the commonest disorder derived from the B-lymphocytes. Inhibiting the immune checkpoint through naturalizing programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1) is proved to be a successful therapeutic regime for lymphoma. Long non-coding RNAs (lncRNAs) are unceasingly reported to be promising biological targets for the cancer therapies. This study planned to explore the regulation of small nucleolar RNA host gene 14 (SNHG14) on DLBCL. SNHG14 level in DLBCL samples and cell lines was analyzed by GEPIA bioinformatics tool and RT-qPCR. Biological functions of SNHG14 in DLBCL were detected by CCK-8, colony formation, and transwell invasion assays. Molecular interaction was determined by RNA immunoprecipitation (RIP) and luciferase reporter assays. MiR-5590-3p-related pathway was identified through KEGG pathway analysis applying DAVID6.8 online bioinformatics tool. Effect of SNHG14 on CD8+ T cells was detected by flow cytometry. Results depicted that SNHG14 was upregulated in DLBCL and its depletion retarded proliferation, migration and epithelial-to-mesenchymal transition (EMT). Mechanistically, SNHG14 sponged miR-5590-3p to upregulate Zinc finger E-box binding homeobox 1 (ZEB1), and ZEB1 transcriptionally activated SNHG14 and PD-L1 to promote the immune evasion of DLBCL cells. In conclusion, we firstly showed that SNHG14/miR-5590-3p/ZEB1 positive feedback loop promoted diffuse large B cell lymphoma progression and immune evasion through regulating PD-1/PD-L1 checkpoint, indicating that targeting SNHG14 was a potential approach to improve the efficacy of immunotherapy in DLBCL.