Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study

IntroductionPostoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients.MethodsPatients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort.ResultsA total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application.ConclusionWe developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients

Data_Sheet_1_Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study.docx

IntroductionPostoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients.MethodsPatients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort.ResultsA total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application.ConclusionWe developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients.</p

A Novel Ferroptosis Related Gene Signature for Prognosis Prediction in Patients With Colon Cancer

PurposeColon cancer (CC) is a serious disease burden. The prognosis of patients with CC is different, so looking for effective biomarkers to predict prognosis is vitally important. Ferroptosis is a promising therapeutic and diagnosis strategy in CC. However, the role of ferroptosis in prognosis of CC has not been studied. The aim of the study is to build a prognosis model related ferroptosis, and provide clues for further therapy of CC.MethodsThe RNA-seq data were from TCGA (training group) and GEO (testing group). The R language and Perl language were used to process and analyze data. LASSO regression analysis was used to build the prognosis model. ssGSEA was used to compare the immune status between two groups. Immunohistochemistry was used to detect expression of AKR1C1 and CARS1 in colon cancer tissues and adjacent tissues.ResultsThe prognosis model consisted of five ferroptosis related genes (AKR1C1, ALOX12, FDFT1, ATP5MC3, and CARS1). The area under curve (AUC) at 1-, 2-, and 3-year were 0.668, 0.678, and 0.686, respectively. The high- and low-risk patients had significant survival probability and could be clearly distinguished by the PCA and t-SNE analysis. The multivariate cox regression analysis also showed the riskscore is an independent prognosis factor. Importantly, we found that the immune status between high- and low-risk patients were different obviously, such as CD8+T cells. And STING, a new promising immune target, was also correlated to our signature genes statistically significantly.ConclusionOur ferroptosis prognosis signature could predict survival of CC patients to a certain degree. And the crosstalk between ferroptosis and immune, especially STING need further studies

Blocking IL-17A enhances tumor response to anti-PD-1 immunotherapy in microsatellite stable colorectal cancer

Background Immune checkpoint inhibitors (ICIs), including anti-PD-1 therapy, have limited efficacy in patients with microsatellite stable (MSS) colorectal cancer (CRC). Interleukin 17A (IL-17A) activity leads to a protumor microenvironment, dependent on its ability to induce the production of inflammatory mediators, mobilize myeloid cells and reshape the tumor environment. In the present study, we aimed to investigate the role of IL-17A in resistance to antitumor immunity and to explore the feasibility of anti-IL-17A combined with anti-PD-1 therapy in MSS CRC murine models.Methods The expression of programmed cell death-ligand 1 (PD-L1) and its regulation by miR-15b-5p were investigated in MSS CRC cell lines and tissues. The effects of miR-15b-5p on tumorigenesis and anti-PD-1 treatment sensitivity were verified both in vitro and in colitis-associated cancer (CAC) and APCmin/+ murine models. In vivo efficacy and mechanistic studies were conducted using antibodies targeting IL-17A and PD-1 in mice bearing subcutaneous CT26 and MC38 tumors.Results Evaluation of clinical pathological specimens confirmed that PD-L1 mRNA levels are associated with CD8+ T cell infiltration and better prognosis. miR-15b-5p was found to downregulate the expression of PD-L1 at the protein level, inhibit tumorigenesis and enhance anti-PD-1 sensitivity in CAC and APCmin/+ CRC models. IL-17A led to high PD-L1 expression in CRC cells through regulating the P65/NRF1/miR-15b-5p axis. Combined IL-17A and PD-1 blockade had efficacy in CT26 and MC38 tumors, with more cytotoxic T lymphocytes cells and fewer myeloid-derived suppressor cells in tumors.Conclusions IL-17A increases PD-L1 expression through the p65/NRF1/miR-15b-5p axis and promotes resistance to anti-PD-1 therapy. Blocking IL-17A improved the efficacy of anti-PD-1 therapy in MSS CRC murine models. IL-17A might serve as a therapeutic target to sensitize patients with MSS CRC to ICI therapy