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Background: As gastric cancer patients aged ≥ 85 years have a short life expectancy and often die from other diseases such as pneumonia, indications for surgery are controversial. In this study, we retrospectively analyzed the prognostic factors of elderly patients with gastric cancer who are candidates for curative gastrectomy. Methods: Among 114 patients aged ≥ 85 years with gastric cancer at our hospital between 2010 and 2019, prognostic factors were examined using the Cox proportional hazards model in 76 patients excluding those with cStage IVB or endoscopic submucosal dissection. We also analyzed the factors of pneumonia death. Results: cStage was I/IIA/IIB/III/IVA in 37/6/14/14/5 patients, respectively. Treatment included distal gastrectomy in 28 patients, total gastrectomy in 6, local resection in 9, others in 3, and no surgery in 30. In univariate analyses of overall survival, Eastern Cooperative Oncology Group Performance Status, physiological score of Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM), Onodera's prognostic nutritional index, cStage, and treatment were prognostic factors. In a multivariate analysis, POSSUM physiological score, cStage, treatment method {no surgery vs. distal gastrectomy: hazard ratio (HR) 5.78, 95% confidence interval (CI) 2.33–14.3}, (total gastrectomy vs. distal gastrectomy: HR 4.26, 95% CI 1.22–14.9) were independent prognostic factors. In univariate analyses of pneumonia-specific survival, treatment (total gastrectomy vs. distal gastrectomy: HR 6.98, 95% CI 1.18–41.3) was the only prognostic factor. Conclusions: The prognosis of distal gastrectomy was better than that of non-surgery even in patients aged ≥ 85 years. However, total gastrectomy was considered to be avoidable due to the high rate of postoperative pneumonia death. [ABSTRACT FROM AUTHOR]

Background: Some biomarkers collected from routine laboratory tests have shown important value in cancer prognosis. The study aimed to evaluate the prognostic significance of routine laboratory biomarkers in patients with endometrial cancer (EC) and to develop credible prognostic nomogram models for clinical application. Methods: A total of 727 patients were randomly divided into a training set and a validation set. Cox proportional hazards models were used to evaluate each biomarker's prognostic value, and independent prognostic factors were used to generate overall survival (OS) and progression-free survival (PFS) nomgrams. The efficacy of the nomograms were evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, X-tile analysis and Kaplan‒Meier curves. Results: Ten significant biomarkers in multivariate Cox analysis were integrated to develop OS and PFS nomograms. The C-indices of the OS- nomogram in the training and validation sets were 0.885 (95% confidence interval (CI), 0.810–0.960) and 0.850 (95% CI, 0.761–0.939), respectively; those of the PFS- nomogram in the training and validation sets were 0.903 (95% CI, 0.866–0.940) and 0.825 (95% CI, 0.711–0.939), respectively. ROC, DCA and calibration curves showed better clinical application value for the nomograms incorporating routine laboratory biomarkers. X-tile analysis and Kaplan‒Meier curves showed that the nomograms were stable and credible in evaluating patients at different risks. Conclusions: Nomogram models incorporating routine laboratory biomarkers, including NLR, MLR, fibrinogen, albumin and AB blood type, were demonstrated to be simple, reliable and favourable in predicting the outcomes of patients with EC. [ABSTRACT FROM AUTHOR]

Background: Overall survival (OS) is a key endpoint measure in the management of patients with cancer. Immunotherapy has become a dominant strategy in cancer therapy. To investigate the relationship between OS and the immune system, we assessed the role of immune genes in OS in 8648 patients across 22 cancer types.Methods: Gene expression data and clinical information were collected from The Cancer Genome Atlas (TCGA) and cBioPortal. Survival analysis was performed with a Cox proportional hazards regression model.Results: (1) The number of prognostic genes, prognostic immune genes (PIGs) and the hazard ratio (HR) of PIGs in different cancer types all varied greatly; (2) KEGG pathway enrichment analyses indicated that the prognostic genes of 6 cancer types were significantly enriched in multiple (≥5) immune system-related pathways. Of the PIGs in these 6 cancer types, we screened 48 common PIGs in at least 5 cancer types. Eleven out of the 48 PIGs were found to participate in the T cell receptor (TCR) signaling pathway according to the STRING database. Among these genes, ZAP70, CD3E, CD3G, CD3D, and CD247 were part of the TCR 'signal-triggering module'; (3) High expression of the PIGs involved in the TCR signaling pathway was associated with improved OS in 5 cancer types (breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC), lung adenocarcinoma (LUAD), and sarcoma (SARC)), but was associated with decreased OS in brain lower-grade glioma (LGG).Conclusions: The TCR signaling pathway played a distinct role in the OS of these 6 cancer types. [ABSTRACT FROM AUTHOR]

Background: Lymphatic vessel density and lymphovascular invasion are commonly assessed to identify the clinicopathological outcomes in breast cancer. However, the prognostic values of them on patients' survival are still uncertain.Methods: Databases of PubMed, Embase, and Web of Science were searched from inception up to 30 June 2016. The hazard ratio with its 95% confidence interval was used to determine the prognostic effects of lymphatic vessel density and lymphovascular invasion on disease-free survival and overall survival in breast cancer.Results: Nineteen studies, involving 4215 participants, were included in this study. With the combination of the results of lymphatic vessel density, the pooled hazard ratios and 95% confidence intervals were 2.02 (1.69-2.40) for disease-free survival and 2.88 (2.07-4.01) for overall survival, respectively. For lymphovascular invasion study, the pooled hazard ratios and 95% confidence intervals were 1.81 (1.57-2.08) for disease-free survival and 1.64 (1.43-1.87) for overall survival, respectively. In addition, 29.56% (827/2798) of participants presented with lymphovascular invasion in total.Conclusions: Our study demonstrates that lymphatic vessel density and lymphovascular invasion can predict poor prognosis in breast cancer. Standardized assessments of lymphatic vessel density and lymphovascular invasion are needed. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to evaluate the survival outcome for middle ear cancer and to construct prognostic models to provide patients and clinicians with more accurate estimates of individual survival probability. Methods: Patients diagnosed with middle ear cancer between 1983 and 2011 were selected for the study from the Surveillance Epidemiology and End Results Program. We used the Kaplan-Meier product limit method to describe overall survival and cause-specific survival. Cox proportional hazards models were fitted to model the relationships between patient characteristics and prognosis. Nomograms for predicting overall survival and cause-specific survival were built using the Cox models established. Results: The entire cohort comprised 247 patients with malignant middle ear cancer. Median duration of follow-up until censoring or death was 25 months (range, 1-319 months). Five-year overall survival and cause-specific survival were 47.4% (95% Confidence Interval (CI), 41.2% to 54.6%) and 58.0% (95% CI, 51.6% to 65.3%), respectively. In multivariable analysis, age, histological subtype, stage, surgery and radiotherapy were predictive of survival. The bootstrap corrected c-index for model predicting overall and cause-specific survival was 0.73 and 0.74, respectively. Calibration plots showed that the predicted survival reasonably approximated observed outcomes. Conclusion: The models represent an objective analysis of all currently available data. The resulting models demonstrated good accuracy in predicting overall survival and cause-specific survival. Nomograms should thus be considered as a useful tool for predicting clinical prognosis. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer has long been a common malignance troubling women. However, there are few studies developing nomogram with comprehensive factors for the prognosis of cervical cancer. Hence, we aimed to build a nomogram to calculate the overall survival (OS) probability in patients with cervical cancer.Methods: Data of 9876 female patients in SEER database and diagnosed as cervical cancer during 2010-2015, was retrospectively analyzed. Univariate and multivariate Cox proportional hazard regression model were applied to select predicted factors and a nomogram was developed to visualize the prediction model. The nomogram was compared with the FIGO stage prediction model. Harrell's C-index, receiver operating curve, calibration plot and decision curve analysis were used to assess the discrimination, accuracy, calibration and clinical utility of the prediction models.Result: Eleven independent prognostic variables, including age at diagnosis, race, marital status at diagnosis, grade, histology, tumor size, FIGO stage, primary site surgery, regional lymph node surgery, radiotherapy and chemotherapy, were used to build the nomogram. The C-index of the nomogram was 0.826 (95% CI: 0.818 to 0.834), which was better than that of the FIGO stage prediction model (C-index: 0.785, 95% CI: 0.776 to 0.793). Calibration plot of the nomogram was well fitted in 3-year overall OS prediction, but overfitting in 5-year OS prediction. The net benefit of the nomogram was higher than the FIGO prediction model.Conclusion: A clinical useful nomogram for calculating the overall survival probability in cervical cancer patients was developed. It performed better than the FIGO stage prediction model and could help clinicians to choose optimal treatments and precisely predict prognosis in clinical care and research. [ABSTRACT FROM AUTHOR]