BackgroundIt is well-established that patients with glioma have a poor prognosis. Although the past few decades have witnessed unprecedented medical advances, the 5-year survival remains dismally low.ObjectiveThis study aims to investigate the role of transmembrane protein-related genes in the development and prognosis of glioma and provide new insights into the pathogenesis of the diseaseMethodsThe datasets of glioma patients, including RNA sequencing data and relative clinical information, were obtained from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) databases. Prognostic transmembrane protein-related genes were identified by univariate Cox analysis. New disease subtypes were recognized based on the consensus clustering method, and their biological uniqueness was verified via various algorithms. The prognosis signature was constructed using the LASSO-Cox regression model, and its predictive power was validated in external datasets by receiver operating characteristic (ROC) curve analysis. An independent prognostic analysis was conducted to evaluate whether the signature could be considered a prognostic factor independent of other variables. A nomogram was constructed in conjunction with traditional clinical variables. The concordance index (C-index) and Decision Curve Analysis (DCA) were used to assess the net clinical benefit of the signature over traditional clinical variables. Seven different softwares were used to compare the differences in immune infiltration between the high- and low-risk groups to explore potential mechanisms of glioma development and prognosis. Hub genes were found using the random forest method, and their expression was based on multiple single-cell datasets.ResultsFour molecular subtypes were identified, among which the C1 group had the worst prognosis. Principal Component Analysis (PCA) results and heatmaps indicated that prognosis-related transmembrane protein genes exhibited differential expression in all four groups. Besides, the microenvironment of the four groups exhibited significant heterogeneity. The 6 gene-based signatures could predict the 1-, 2-, and 3-year overall survival (OS) of glioma patients. The signature could be used as an independent prognosis factor of glioma OS and was superior to traditional clinical variables. More immune cells were infiltrated in the high-risk group, suggesting immune escape. According to our signature, many genes were associated with the content of immune cells, which revealed that transmembrane protein-related genes might influence the development and prognosis of glioma by regulating the immune microenvironment. TMEM158 was identified as the most important gene using the random forest method. The single-cell datasets consistently showed that TMEM158 was expressed in multiple malignant cells.ConclusionThe expression of transmembrane protein-related genes is closely related to the immune status and prognosis of glioma patients by regulating tumor progression in various ways. The interaction between transmembrane protein-related genes and immunity during glioma development lays the groundwork for future studies on the molecular mechanism and targeted therapy of glioma.

Background and purposeTo provide a comprehensive overview of epidemiological features and temporal trends of pancreatic cancer in urban Shanghai from 1973 to 2017.MethodsData on pancreatic cancer in urban Shanghai were obtained through the Shanghai Cancer Registry and the Vital Statistics System. Joinpoint analysis was used to describe the temporal trends and annual percent changes (APCs) and age-period-cohort analysis were used to estimate the effects of age, period, and birth cohort on pancreatic cancer.ResultsThere were a total of 29,253 cases and 27,105 deaths of pancreatic cancer in urban Shanghai over the 45-year study period. The overall average annual age-standardized incidence and mortality rates were 5.45/100,000 and 5.02/100,000, respectively. Both the incidence and mortality rates demonstrated fluctuating upward trends, with an average annual increase rate of 1.51% (APC = 1.51, P < 0.001) and 1.04% (APC = 1.04, P < 0.001), respectively. The upward trend in incidence was greater for females than for males, while the trend in mortality was seen in both sexes equally and continuously. In recent years (2013-2017), the age-specific incidence rates increased further than before, with statistically significant changes in the 35-year, 45- to 55-year and 70- to 85-year age groups (P < 0.05). The age-specific mortality rates also showed obvious upward trends, which in the 50- to 55-year, and 75- to 85-year age groups increased significantly. The results of the age-period-cohort analysis suggested significant effects of age, period, and cohort on the prevalence of pancreatic cancer.ConclusionThe prevalence of pancreatic cancer, dramatically influenced by socioeconomic development and lifestyles, demonstrated a significant upward trend from 1973 to 2017 in urban Shanghai and underscored the necessity and urgency for additional efforts in primary and secondary prevention measures.

ObjectiveTo investigate the predicting prognosis and guiding postoperative chemoradiotherapy (POCRT) value of preoperative mean platelet volume (MPV) in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC).MethodsWe proposed a blood biomarker, MPV, for predicting disease-free survival (DFS) and overall survival (OS) in LA-ESCC patients who underwent surgery (S) alone or S+POCRT. The median cut-off value of MPV was 11.4 fl. We further evaluated whether MPV could guide POCRT in the study and external validation groups. We used multivariable Cox proportional hazard regression analysis, Kaplan–Meier curves, and log-rank tests to ensure the robustness of our findings.ResultsIn the developed group, a total of 879 patients were included. MVP was associated with OS and DFS defined by clinicopathological variables and remained an independent prognostic factor in the multivariate analysis (P = 0.001 and P = 0.002, respectively). For patients with high MVP, 5-year OS and 0DFS were significantly improved compared to those with low MPV (P = 0.0011 and P = 0.0018, respectively). Subgroup analysis revealed that POCRT was associated with improved 5-year OS and DFS compared with S alone in the low-MVP group (P < 0.0001 and P = 0.0002, respectively). External validation group analysis (n = 118) showed that POCRT significantly increased 5-year OS and DFS (P = 0.0035 and P = 0.0062, respectively) in patients with low MPV. For patients with high MPV, POCRT group showed similar survival rates compared with S alone in the developed and validation groups.ConclusionsMPV as a novel biomarker may serve as an independent prognosis factor and contribute to identifying patients most likely to benefit from POCRT for LA-ESCC.

Purpose/Objective(s)The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy.Materials/MethodsComputed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05).ResultsOur proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively.ConclusionIn conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers.

PurposeClear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients’ prognosis.Materials and methodsUrine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data.Results133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FC<0.5). Top 10 proteins including S100A14, PKHD1L1, FABP4, ITIH2, C3, C8G, C2, ATF6, ANGPTL6, F13B were performed ELISA to verify. The results showed that PKHD1L1, ANGPTL6, FABP4 and C3 were statistically significant (P<0.05). We performed multivariate logistic regression analysis and plotted a nomogram. Receiver operating characteristic (ROC) curve indicted that the diagnostic efficiency of combined indicators is satisfactory (Aare under curve, AUC=0.835). Furthermore, the prognostic value of the urine proteomics was explored through the intersection between urine proteomics and TCGA RNA-seq data. Thus, COX regression analysis showed that VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 were statistically significant (P<0.05).ConclusionOur study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.