Wei Wang

Objectives The aim of this analysis was to investigate the worldwide distribution and trends in the burden of female infertility (FI). Design and setting Analysis of cross-sectional data from 1990 to 2019 from the Global Burden of Disease (GBD) Study 2019. Participants Adults aged 20–49 years in various geographical regions, Sociodemographic Index (SDI) levels and populations across 204 countries worldwide. Outcome measures Disability-adjusted life years (DALYs), age-standardised DALY rates and estimated annual percentage changes (EAPCs) for FI over the research period. Results The global burden of FI, as measured by DALYs and age-standardised DALY rates among adults aged 20–49 years, exhibited an upward trend from 1990 to 2019. Regions such as Tropical Latin America (EAPC: 3.76, 95% CI 2.76 to 4.77), South Asia (EAPC: 1.74, 95% CI 1.01 to 2.48) and Andean Latin America (EAPC: 8.54, 95% CI 6.79 to 10.32) had higher DALYs. The age-standardised DALY rates notably increased in low-middle-SDI and low-SDI countries, with EAPCs of 1.42 (95% CI 0.76 to 2.10) and 1.25 (95% CI 0.30 to 2.21), respectively. Despite lower overall DALYs in high-SDI countries, they experienced the highest EAPC in DALYs (1.57, 95% CI 1.30 to 1.83) during the period 1990–2019. China and India consistently had the highest DALYs for FI globally in both 1990 and 2019, while Africa and Latin America bore a significantly greater burden of the disease compared with other regions. Conclusion The GBD Study data on FI among adults aged 20–49 years revealed a rising global trend in FI from 1990 to 2019, with notable variations across different regions.

Background Immune checkpoint inhibitors (ICIs) have dramatically changed the landscape of cancer treatment. However, only a few patients respond to ICI treatment. Thus, uncovering clinically accessible ICI biomarkers would help identify which patients will respond well to ICI treatment. A comprehensive objective response rate (ORR) data of anti-PD-1/PD-L1 monotherapy in pan-cancer would offer the original data to explore the new biomarkers for ICIs. Methods We systematically searched PubMed, Cochrane, and Embase for clinical trials on July 1, 2021, limited to the years 2017–2021, from which we obtained studies centering around anti–PD-1/PD-L1 monotherapy. Finally, 121 out of 3099 publications and 143 ORR data were included. All of the 31 tumor types/subtypes can be found in the TCGA database. The gene expression profiles and mutation data were downloaded from TCGA. A comprehensive genome-wide screening of ORR highly correlated mutations among 31 cancers was conducted by Pearson correlation analysis based on the TCGA database. Results According to the ORR, we classified 31 types of cancer into high, medium, and low response types. Further analysis uncovered that “high response” cancers had more T cell infiltration, more neoantigens, and less M2 macrophage infiltration. A panel of 28 biomarkers reviewed from recent articles were investigated with ORR. We also found the TMB as a traditional biomarker had a high correlation coefficient with ORR in pan-cancer, however, the correlation between ITH and ORR was low across pan-cancer. Moreover, we primarily identified 1044 ORR highly correlated mutations through a comprehensive screening of TCGA data, among which USH2A, ZFHX4 and PLCO mutations were found to be highly correlated to strengthened tumor immunogenicity and inflamed antitumor immunity, as well as improved outcomes for ICIs treatment among multiple immunotherapy cohorts. Conclusion Our study provides comprehensive data on ORR of anti-PD-1/PD-L1 monotherapy across 31 tumor types/subtypes and an essential reference of ORR to explore new biomarkers. We also screened out a list of 1044 immune response related genes and we showed that USH2A, ZFHX4 and PLCO mutations may act as good biomarkers for predicting patient response to anti-PD-1/PD-L1 ICIs.

Background: Colorectal cancer (CRC) is a heterogeneous disease, and current classification systems are insufficient for stratifying patients with different risks. This study aims to develop a generalized, individualized prognostic consensus molecular subtype (CMS)-transcription factors (TFs)-based signature that can predict the prognosis of CRC.Methods: We obtained differentially expressed TF signature and target genes between the CMS4 and other CMS subtypes of CRC from The Cancer Genome Atlas (TCGA) database. A multi-dimensional network inference integrative analysis was conducted to identify the master genes and establish a CMS4-TFs-based signature. For validation, an in-house clinical cohort (n = 351) and another independent public CRC cohort (n = 565) were applied. Gene set enrichment analysis (GSEA) and prediction of immune cell infiltration were performed to interpret the biological significance of the model.Results: A CMS4-TFs-based signature termed TF-9 that includes nine TF master genes was developed. Patients in the TF-9 high-risk group have significantly worse survival, regardless of clinical characteristics. The TF-9 achieved the highest mean C-index (0.65) compared to all other signatures reported (0.51 to 0.57). Immune infiltration revealed that the microenvironment in the high-risk group was highly immune suppressed, as evidenced by the overexpression of TIM3, CD39, and CD40, suggesting that high-risk patients may not directly benefit from the immune checkpoint inhibitors.Conclusions: The TF-9 signature allows a more precise categorization of patients with relevant clinical and biological implications, which may be a valuable tool for improving the tailoring of therapeutic interventions in CRC patients.

Background: Ampullary adenocarcinoma (AAC) arises from the ampulla of Vater where the pancreatic duct and bile duct join and empty into the duodenum. It can be classified into intestinal and pancreatobiliary types based on histopathology or immunohistochemistry. However, there are no biomarkers for further classification of pancreatobiliary-type AAC which has important implications for its treatment. We aimed to identify the tumor origin of pancreatobiliary-type AAC by systematically analyzing whole-slide images (WSIs), survival data, and genome sequencing data collected from multiple centers.Methods: This study involved three experiments. First, we extracted quantitative and highly interpretable features from the tumor region in WSIs and constructed a histologic classifier to differentiate between pancreatic adenocarcinoma (PAC) and cholangiocarcinoma. The histologic classifier was then applied to patients with pancreatobiliary-type AAC to infer the tumor origin. Secondly, we compared the overall survival of patients with pancreatobiliary-type AAC stratified by the adjuvant chemotherapy regimens designed for PAC or cholangiocarcinoma. Finally, we compared the mutation landscape of pancreatobiliary-type AAC with those of PAC and cholangiocarcinoma.Results: The histologic classifier accurately classified PAC and cholangiocarcinoma in both the internal and external validation sets (AUC > 0.99). All pancreatobiliary-type AACs (n = 45) were classified as PAC. The patients with pancreatobiliary-type AAC receiving regimens designed for PAC showed more favorable overall survival than those receiving regimens designed for cholangiocarcinoma in a multivariable Cox regression (hazard ratio = 7.24, 95% confidence interval: 1.28–40.78,P = 0.025). The results of mutation analysis showed that the mutation landscape of AAC was very similar to that of PAC but distinct from that of cholangiocarcinoma.Conclusions: This multi-center study provides compelling evidence that pancreatobiliary-type AAC resembles PAC instead of cholangiocarcinoma in different aspects, which can guide the treatment selection and clinical trials planning for pancreatobiliary-type AAC.

Exosomes are critical intercellular messengers released upon the fusion of multivesicular bodies with the cellular plasma membrane that deliver their cargo in the form of extracellular vesicles. Containing numerous nonrandomly packed functional proteins, lipids, and RNAs, exosomes are vital intercellular messengers that contribute to the physiologic processes of the healthy organism. During the post‐genome era, exosome‐oriented proteomics have garnered great interest. Since its establishment, mass spectrometry (MS) has been indispensable for the field of proteomics research and has advanced rapidly to interrogate biological samples at a higher resolution and sensitivity. Driven by new methodologies and more advanced instrumentation, MS‐based approaches have revolutionized our understanding of protein biology. As the access to online proteomics database platforms has blossomed, experimental data processing occurs with more speed and accuracy. Here, we review recent advances in the technological progress of MS‐based proteomics and several new detection strategies for MS‐based proteomics research. We also summarize the use of integrated online databases for proteomics research in the era of big data. © 2020 John Wiley & Sons Ltd. Mass Spec Rev