A new approach: Evaluation of necroptosis and immune status enables prediction of the tumor microenvironment and treatment targets in pancreatic cancer.

Growing evidence indicates a potential correlation between necroptosis and pancreatic cancer, and the relationship between necroptosis, immune infiltration and the microenvironment in pancreatic cancer has drawn increasing attention. However, two-dimensional phenotype and prognostic assessment systems based on a combination of necroptosis and immunity have not been explored. In our present study, we explored the pancancer genomics signature of necroptosis-related molecules, identifying necroptosis-related molecule mutation profiles, expression profiles, and correlations between expression levels and methylation/CNV levels. We identified distinct necroptotic as well as immune statuses in pancreatic cancer, and a high necroptosis phenotype and high immunity phenotype both indicated better prognosis than a low necroptosis phenotype and low immunity phenotype. The two-dimensional phenotype we constructed has ideal discriminative effects on pancreatic cancer prognosis, inflammation, and the immune microenvironment. The "high-necroptosis and high-immunity (HNHI)" group exhibited the best prognosis and the highest proportion of infiltrating immune cells. The NI score can be used to predict patient prognosis and is correlated with the immune microenvironment score, chemotherapeutic drug IC50, and tumor mutational burden. In addition, it may be useful for predicting the effect of individualized chemotherapy and immunotherapy. Our study also revealed that SLC2A1 is associated with both necroptosis and immunity and acts as a potential oncogene in pancreatic cancer. In conclusion, the two-dimensional phenotype and NI score we developed are promising tools for clinical multiomics applications and prediction of chemotherapy and immunotherapy response and present benefits in terms of precision medicine and individualized treatment decision-making for pancreatic cancer patients.

Turning up the heat on non-immunoreactive tumors: autophagy influences the immune microenvironment in pancreatic cancer.

BACKGROUND: Autophagy regulators play important roles in the occurrence and development of a variety of tumors and are involved in immune regulation and drug resistance. However, the modulatory roles and prognostic value of autophagy regulators in pancreatic cancer have not been identified. METHODS: Transcriptomic data and survival information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to construct a risk score model. Important clinical features were analyzed to generate a nomogram. In addition, we used various algorithms, including ssGSEA, CIBERSORT, XCELL, EPIC, TIMER, and QUANTISEQ, to evaluate the roles of autophagy regulators in the pancreatic cancer immune microenvironment. Furthermore, the mutation landscape was compared between different risk groups. RESULTS: Pan cancer analysis indicated that most of the autophagy regulators were upregulated in pancreatic cancer and were correlated with methylation and CNV level. MET, TSC1, and ITGA6 were identified as the prognostic autophagy regulators and used to construct a risk score model. Some critical clinical indicators, such as age, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, alcohol and sex, were combined with the risk model to establish the nomogram, which may offer clinical guidance. In addition, our study demonstrated that the low score groups exhibited high immune activity and high abundances of various immune cells, including T cells, B cells, and NK cells. Patients with high risk scores exhibited lower half inhibitory concentration (IC50) values for paclitaxel and had downregulated expression profiles of PD1, CTLA4, and LAG3. Mutation investigation indicated that the high risk groups exhibited a higher mutation burden and higher mutation number compared to the low risk groups. additionally, we verified our risk stratification method using cytology and histology data from our center, and the results are satisfactory. CONCLUSION: We speculated that autophagy regulators have large effects on the prognosis, immune landscape and drug sensitivity of pancreatic cancer. Our model, which combines critical autophagy regulators and clinical indicators, will provide guidance for clinical treatment.

Pyroptosis-related lncRNA pairs to estimate the molecular features and prognostic outcomes of pancreatic ductal adenocarcinoma.

Pyroptosis is a form of programmed cell death associated with inflammatory alterations. However, the intrinsic mechanisms and underlying correlation of pyroptosis-related lncRNAs (PRLs) in pancreatic ductal adenocarcinoma (PDAC) remain unclear. The objective of the current research was to identify pyroptosis-related lncRNAs and a prognostic model to predict the prognosis of patients. We extracted pyroptosis-related lncRNAs to construct a risk model and validated them at Fudan University Shanghai Cancer Center. Crosstalk between lncRNA SNHG10 and GSDMD was found to regulate pyroptosis levels. A new algorithm was used to establish a 0 or 1 PRL pair matrix and prognostic model. Six pyroptosis-related lncRNA pairs were identified and utilized to construct a risk model. The low-risk groups exhibited better prognoses than the high-risk groups. The area under the curve (AUC) indicated extremely high accuracy, reaching 0.810 at 1 year, 0.850 at 2 years, and 0.850 at 3 years in the training set. Patients with different risk scores exhibited distinct metabolic, inflammatory, and immune microenvironments as well as tumor mutation landscapes. Additionally, 9 commonly used chemotherapeutic drugs exhibited different sensitivities between the high- and low-risk groups. To conclude, we propose that pyroptosis exhibits a close correlation with PDAC. Our risk model based on PRL pairs may be beneficial for the accurate estimation of prognostic outcomes, the immune microenvironment, and drug sensitivity, bringing therapeutic hope for patients with PDAC.

Construction of a paclitaxel-related competitive endogenous RNA network and identification of a potential regulatory axis in pancreatic cancer.

BACKGROUND: Increasing numbers of studies have elucidated the role of competitive endogenous RNA (ceRNA) networks in carcinogenesis. However, the potential role of the paclitaxel-related ceRNA network in the innate mechanism and prognosis of pancreatic cancer has not been identified. METHODS: Comprehensive bioinformatics analyses were performed to identify drug-related miRNAs (DRmiRNAs), drug-related mRNAs (DRmRNAs) and drug-related lncRNAs (DRlncRNAs) and construct a ceRNA network. The ssGSEA and CIBERSORT algorithms were utilized for immune cell infiltration analysis. Additionally, we validated our paclitaxel-related ceRNA regulatory axis at the gene expression level; functional experiments were conducted to explore the biological functions of the key genes. RESULTS: A total of 182 mRNAs, 13 miRNAs, and 53 lncRNAs were confirmed in the paclitaxel-related ceRNA network. In total, 6 mRNAs, 4 miRNAs, and 6 lncRNAs were identified to establish a risk signature and exhibited optimal prognostic effects. The mRNA signature can predict the abundance of immune cell infiltration and the sensitivity of different chemotherapeutic drugs and may also have a guiding effect in immune checkpoint therapy. A potential PART1/hsa-mir-21/SCRN1 axis was confirmed according to the ceRNA theory and was verified by qPCR. The results indicated that PART1 knockdown markedly increased hsa-mir-21 expression but inhibited SCRN1 expression, weakening the proliferation and migration abilities. CONCLUSIONS: We hypothesized that the paclitaxel-related ceRNA network strongly influences the innate mechanism, prognosis, and immune infiltration of pancreatic cancer. Our risk signatures can accurately predict survival outcomes and provide a clinical basis.

Microorganisms in chemotherapy for pancreatic cancer: An overview of current research and future directions.

Pancreatic cancer is a malignant tumor of the digestive system with a very high mortality rate. While gemcitabine-based chemotherapy is the predominant treatment for terminal pancreatic cancer, its therapeutic effect is not satisfactory. Recently, many studies have found that microorganisms not only play a consequential role in the occurrence and progression of pancreatic cancer but also modulate the effect of chemotherapy to some extent. Moreover, microorganisms may become an important biomarker for predicting pancreatic carcinogenesis and detecting the prognosis of pancreatic cancer. However, the existing experimental literature is not sufficient or convincing. Therefore, further exploration and experiments are imperative to understanding the mechanism underlying the interaction between microorganisms and pancreatic cancer. In this review, we primarily summarize and discuss the influences of oncolytic viruses and bacteria on pancreatic cancer chemotherapy because these are the two types of microorganisms that are most often studied. We focus on some potential methods specific to these two types of microorganisms that can be used to improve the efficacy of chemotherapy in pancreatic cancer therapy.

Validation and head-to-head comparison of four models for predicting malignancy of intraductal papillary mucinous neoplasm of the pancreas: A study based on endoscopic ultrasound findings.

BACKGROUND: Several models are currently available for predicting the malignancy of pancreatic intraductal papillary mucinous neoplasm (IPMN), namely, the Pancreatic Surgery Consortium (PSC), the Japan Pancreas Society (JPS), the Johns Hopkins Hospital (JHH), and the Japan-Korea (JPN-KOR) models. However, a head-to-head comparison that shows which model is more accurate for this individualized prediction is lacking. AIM: To perform a head-to-head comparison of the four models for predicting the malignancy of pancreatic IPMN. METHODS: A total of 181 patients with IPMN who had undergone surgical resection were identified from a prospectively maintained database. The characteristics of IPMN in patients were recorded from endoscopic ultrasound imaging data and report archives. The performance of all four models was examined using Harrell's concordance index (C-index), calibration plots, decision curve analyses, and diagnostic tests. RESULTS: Of the 181 included patients, 94 were categorized as having benign disease, and the remaining 87 were categorized as having malignant disease. The C-indexes were 0.842 [95% confidence interval (CI): 0.782-0.901], 0.704 (95%CI: 0.626-0.782), 0.754 (95%CI: 0.684-0.824), and 0.650 (95%CI: 0.483-0.817) for the PSC, JPS, JHH, and JPN-KOR models, respectively. Calibration plots showed that the PSC model had the least pronounced departure from ideal predictions. Of the remaining three models, the JPS and JHH models underestimated the probability of malignancy, while the JPN-KOR model overestimated the malignant potential of branch duct-IPMN. Decision curve analysis revealed that the PSC model resulted in a better clinical net benefit than the three other models. Diagnostic tests also showed a higher accuracy (0.801) for the PSC model. CONCLUSION: The PSC model exhibited the best performance characteristics. Therefore, the PSC model should be considered the best tool for the individualized prediction of malignancy in patients with pancreatic IPMN.

The microbiota and microbiome in pancreatic cancer: more influential than expected.

Microbiota is just beginning to be recognized as an important player in carcinogenesis and the interplay among microbes is greater than expected. Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease for which mortality closely parallels incidence. Early detection would provide the best opportunity to increase survival rates. Specific well-studied oral, gastrointestinal, and intrapancreatic microbes and some kinds of hepatotropic viruses and bactibilia may have potential etiological roles in pancreatic carcinogenesis, or modulating individual responses to oncotherapy. Concrete mechanisms mainly involve perpetuating inflammation, regulating the immune system-microbe-tumor axis, affecting metabolism, and altering the tumor microenvironment. The revolutionary technology of omics has generated insight into cancer microbiomes. A better understanding of the microbiota in PDAC might lead to the establishment of screening or early-stage diagnosis methods, implementation of cancer bacteriotherapy, adjustment of therapeutic efficacy even alleviating the adverse effects, creating new opportunities and fostering hope for desperate PDAC patients.