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Although m6A modifications are associated with tumor progression, and anti-tumor immune responses, the role of m6A regulators in HPV-related carcinogenesis has not been well resolved. To provide evidence for the role of m6A regulators in HPV-related carcinogenesis and identify potential therapeutic targets for HPV-related cancers, integrative analyses of m6A regulators in 1,485 head and neck squamous cell carcinoma (HNSC) patients and 507 cervical squamous cell carcinoma (CESC) patients was performed and identified that an m6A regulator, METTL3, was highly expressed in tumors and was related to the poor prognosis in HNSC and CESC. In HPV-positive tumors, METTL3 was positively associated with tumor HPV status, such as HPV integration status, E6 and unspliced-E6 expression, and p16 expression. Further analysis demonstrated that METTL3

Although endometrioid carcinoma (EC) and endometrioid ovarian carcinoma (EnOC) display similar pathological features, their molecular characteristics remain to be determined. Somatic mutation data from 2777 EC, 423 EnOC, and 57 endometriosis patients from the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset were analyzed and showed similar profiles with different mutation frequencies among them. By using 275 overlapping mutated genes, EC was clustered into two groups with different disease outcomes and different clinical characteristics. Although BRCA-associated mutation characteristics were identified in both EC and EnOC, the mutation frequencies of BRCA1 (P=0.0146), BRCA2 (P=0.0321), ATR (P=3.25E-11), RAD51 (P=3.95E-08), RAD1 (P=0.0003), TP53 (P=6.11E-33), and BRIP1 (P=2.90E-09) were higher in EnOC. Further analysis showed that EnOC cell lines with BRCA-associated mutation characteristics were more sensitive to poly ADP-ribose polymerase (PARP) inhibitors than EC cell lines, including olaparib, talazoparib, rucaparib, and veliparib. Moreover, based on BRCA-associated mutational and transcriptomic profiles, EC with BRCA-associated mutational burdens shows lower levels of immune cell infiltration, higher expression of immunosuppressive checkpoint molecules and worse prognosis than EC without BRCA mutation. Our study comprehensively analyzed the genome mutation features of EC and EnOC and provide insights into the molecular characteristics of EC and EnOC.

Increased ovarian fibrosis and an expanded stromal cell compartment are the main characteristics of aging ovaries. However, the molecular mechanisms and the key factor of stromal cells underlying ovarian aging remain unclear. Here, we explored single-cell transcriptomic data of ovaries from the adult mouse (4,363 cells), young (1,122 cells), and aged (1,479 cells) non-human primates (NHPs) to identify expression patterns of stromal cells between young and old ovaries. An increased number of stromal cells (p = 0.0386) was observed in aged ovaries of NHPs, with enrichment processes related to the collagen-containing extracellular matrix. In addition, differentially expressed genes of stromal cells between young and old ovaries were regulated by ESR1 (p = 7.94E-08) and AR (p = 1.99E-05). Among them

Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. Results Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979–1.000) in internal validation cohort and 0.999 (95% CI 0.998–1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. Conclusions The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR2000032161). Graphical abstracthelper lymphocytve vv

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients., Methods to stratify patients according to mortality risk are essential to allocate limited heath resources during the COVID-19 crisis. Here, using machine learning methods, the authors present a mortality risk prediction model for COVID-19 that uses patients’ clinical data on admission to stratify patients by mortality risk.

Integration of human papillomavirus (HPV) DNA into the human genome is a reputed key driver of cervical cancer. However, the effects of HPV integration on chromatin structural organization and gene expression are largely unknown. We studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome 19. We then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome, RNA, chromatin immunoprecipitation and high-throughput chromosome conformation capture (Hi-C) sequencing to identify the mechanisms of HPV integration in cervical carcinogenesis. Molecular analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities, with correlation found between three-dimensional (3D) structural change and gene expression. Importantly, HPV integration divided one topologically associated domain (TAD) into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106, with a decrease in PEG3 expression and an increase in CCDC106 expression. This expression dysregulation was further confirmed using 10 samples from our cohort, which exhibited the same HPV-CCDC106 integration. In summary, we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure remodeling. Thus, this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis.

IntroductionNucleic acid amplification is the main method used to detect infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the false-negative rate of nucleic acid tests cannot be ignored.Material and methodsHerein, we demonstrated genomic variations at the target sequences for the tests and the geographical distribution of the variations across countries by analyzing the whole-genome sequencing data of SARS-CoV-2 strains from the 2019 Novel Coronavirus Resource (2019nCoVR) database.ResultsAmong the 21 pairs of primer sequences in regions ORF1ab, S, E, and N, the total length of primer and probe target sequences was 938 bp, with 131 (13.97%) variant loci in 2415 (38.96%) isolates. Primer targets in the N region contained the most variations that were distributed among the most isolates, and the E region contained the fewest. Single nucleotide polymorphisms were the most frequent variation, with C to T transitions being detected in the most variant loci. G to A transitions and G to C transversions were the most common and had the highest isolate density. Genomic variations at the three mutation sites N: 28881, N: 28882, and N: 28883 were the most commonly detected, including in 608 SARS-CoV-2 strains from 33 countries, especially in the United Kingdom, Portugal, and Belgium.ConclusionsOur work comprehensively analyzed genomic variations at the target sequences of the nucleic acid amplification tests, offering evidence to optimize primer and probe target sequence selection, thereby improving the performance of the SARS-CoV-2 diagnostic test.

Patients with malignancy were reportedly more susceptible and vulnerable to Coronavirus Disease 2019 (COVID-19), and witnessed a greater mortality risk in COVID-19 infection than noncancerous patients. But the role of immune dysregulation of malignant patients on poor prognosis of COVID-19 has remained insufficiently investigated. Here we conducted a retrospective cohort study that included 2,052 patients hospitalized with COVID-19 (Cancer, n = 93; Non-cancer, n = 1,959), and compared the immunological characteristics of both cohorts. We used stratification analysis, multivariate regressions, and propensity-score matching to evaluate the effect of immunological indices. In result, COVID-19 patients with cancer had ongoing and significantly elevated inflammatory factors and cytokines (high-sensitivity C-reactive protein, procalcitonin, interleukin (IL)-2 receptor, IL-6, IL-8), as well as decreased immune cells (CD8 + T cells, CD4 + T cells, B cells, NK cells, Th and Ts cells) than those without cancer. The mortality rate was significantly higher in cancer cohort (24.7%) than non-cancer cohort (10.8%). By stratification analysis, COVID-19 patients with immune dysregulation had poorer prognosis than those with the relatively normal immune system both in cancer and non-cancer cohort. By logistic regression, Cox regression, and propensity-score matching, we found that prior to adjustment for immunological indices, cancer history was associated with an increased mortality risk of COVID-19 (p .30). In conclusion, COVID-19 patients with cancer had more severely dysregulated immune responses than noncancerous patients, which might account for their poorer prognosis. Clinical Trial: This study has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2000032161).

Additional file 4 Relative feature importance of SVM, GBDT, NN and SPMCIIP model. Abbreviations: SVM, supported vector machine. GBDT, Gradient Boosted Decision Tree. NN, neural network. SPMCIIP, Severity prediction model for COVID-19 by immune-inflammatory parameters. CRP, C reactive protein. IL-2R, interleukin 2 receptor. IL-6, interleukin 6. NK, Natural killer cells. PCT, procalcitonin. Th, T-helper lymphocyte. Ts, T-suppressor lymphocyte.

Additional file 5 Calibration curves of SPMCIIP model in cohorts. Calibration curves of SPMCIIP model in a internal validation cohort and b external validation cohort, respectively. The triangle represents the observation group. Each group contained an average of 20 observations. The dashed line is the ideal calibration curve. The bottom vertical lines refer to the predicted probability distribution. Red curve is the fitted nonparametric calibration curve. Abbreviations: AUC, Area under the curve.

Additional file 1 Differential variables between critical ill and non-critical ill patients. The significant test is Asymptotic Two-Sample Brown-Mood Median Test. Abbreviations: Th/Ts, T-helper/T-suppressor lymphocyte. IL-2R, interleukin 2 receptor. CRP, C reactive protein. IQR, interquartile ranges.

Additional file 2. Visualization of the denosing and filtering process. a, Heatmap of raw lab test data. b, Heatmap of lab test data after removing patients with more than and equal to 30% missing entries across the SF and OV hospitals. c, Heatmap of lab test data after removing lab test features with more than and equal to 30% missing entries across the SF and OV hospitals. Black tiles refer to missing entries. Abbreviations: NK, Natural killer cells, Th, T-helper lymphocyte. Ts, T-suppressor lymphocyte. C3, complement 3. C4, complement 4. CRP, C reactive protein. PCT, procalcitonin IFN-γ, interferon-γ. TNF-α, tumor necrosis factor α. IL-1β, interleukin 1β. IL-2R, interleukin 2 receptor. IL-4, interleukin 4. IL-6, interleukin 6. IL-8, interleukin 8. IL-10, interleukin 10. IGA, immunoglobulin A. IGG, immunoglobulin G. IGM, immunoglobulin M. C-IGM, SARS-COV-2 specific antibody IgM. C-IGG, SARS-COV-2 specific antibody IgG. SF, Sino-French New City Campus of Tongji Hospital. OV, Optical Valley Campus of Tongji Hospital.

Additional file 3 Visualization of the imputation process. a, c Heatmap of SF and OV lab test data before imputation. b, d Heatmap of SF and OV lab test data after imputation. Black tiles refer to missing entries. Abbreviations: NK, Natural killer cells, Th, T-helper lymphocyte. Ts, T-suppressor lymphocyte. CRP, C reactive protein. PCT, procalcitonin. IFN-γ, interferon-γ. TNF-α, tumor necrosis factor α. IL-1β, interleukin 1β. IL-2R, interleukin 2 receptor. IL-4, interleukin 4. IL-6, interleukin 6. IL-8, interleukin 8. IL-10, interleukin 10. C-IGM, SARS-COV-2 specific antibody IgM. C-IGG, SARS-COV-2 specific antibody IgG. SF, Sino-French New City Campus of Tongji Hospital. OV, Optical Valley Campus of Tongji Hospital.

Background The ferocious global assault of COVID-19 continues. Critically ill patients witnessed significantly higher mortality than severe and moderate ones. Herein, we aim to comprehensively delineate clinical features of COVID-19 and explore risk factors of developing critical disease. Methods This is a Mini-national multicenter, retrospective, cohort study involving 2,387 consecutive COVID-19 inpatients that underwent discharge or death between January 27 and March 21, 2020. After quality control, 2,044 COVID-19 inpatients were enrolled. Electronic medical records were collected to identify the risk factors of developing critical COVID-19. Findings The severity of COVID-19 climbed up straightly with age. Critical group was characterized by higher proportion of dyspnea, systemic organ damage, and long-lasting inflammatory storm. All-cause mortality of critical group was 85•45%, by contrast with 0•58% for severe group and 0•18% for moderate group. Logistic regression revealed that sex was an effect modifier for hypertension and coronary heart disease (CHD), where hypertension and CHD were risk factors solely in males. Multivariable regression showed increasing odds of critical illness associated with hypertension, CHD, tumor, and age ≥ 60 years for male, and chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), tumor, and age ≥ 60 years for female. Interpretation We provide comprehensive front-line information about different severity of COVID-19 and insights into different risk factors associated with critical COVID-19 between sexes. These results highlight the significance of dividing risk factors between sexes in clinical and epidemiologic works of COVID-19, and perhaps other coronavirus appearing in future. Funding National Science Foundation of China .

Supplemental Digital Content is available in the text, Background: In this study, we investigate the incidence of venous thrombosis (VT), and evaluate the effectiveness and safety of 3 major thromboprophylaxes and the potential risk factors for VT in women undergoing surgery for a gynecological malignancy. Methods: We performed a randomized controlled trial of 307 patients undergoing laparoscopic surgery for gynecological malignancies at a single institution from January 2016 to October 2017. Patients were divided into 3 groups: one receiving a half dose of low-molecular-weight heparin sodium injection (FLUXUM, Alfa Wassermann, Italy) delivered by injection, one receiving a full dose of FLUXUM, and a third group receiving an Argatroban injection. Results: None of the patients in our study developed a pulmonary embolism, bleeding, or infectious complications. There were no statistical differences in the rate of deep venous thrombosis (DVT) (0%, 0%, and 2.38%) and the superficial venous thromboembolism (SVT) (15.66%, 8.97%, and 18.6%) among the 3 groups. None of the patients developed symptomatic VT. The effect of treatment on alanine aminotransferase and aspartate aminotransferase differed between the groups, with a minimal effect in the Argatroban group, and all 3 methods resulted in minimal impairment of renal function. Decreased hemoglobin, elevated levels of D-dimer, and prothrombin time were closely related to thrombogenesis. Conclusion: In conclusion, the incidence of postoperative thrombosis in gynecological malignancy among these Chinese people is not as low as we had originally presumed. Argatroban is not more effective than Parnaparin as a direct thrombin inhibitor, but it has less influence on liver function, which is beneficial for patients undergoing chemotherapy. Hemoglobin, D-dimer, and prothrombin time may be used to predict or detect thrombogenesis.

Background: Coronavirus disease 2019 (COVID­­­-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), broke out in Wuhan, China, sinc

Background: Integration of human papillomavirus (HPV) DNA into human genome is the reputed key driver of cervical cancer. However, the effects of HPV integration on chromatin structural organization and gene expression are largely unknown in such cancer. Methods: We investigated a cohort of 61 samples for HPV integration analysis. And selected a fresh cancer tissue from the set of clinical samples whose tissue only contained unique integration loci at CCDC106 and contained no HPV episomal DNA. A combination of WGS, RNA-seq, ChIP-seq and Hi-C data analysis were applied to identify the mechanisms of HPV integration in cervical carcinogenesis. IHC staining was used to validate the expression change after HPV-CCDC106 integration. Results: Based on the cohort of 61 samples, we identified a hot integrated site in the CCDC106 gene on chromosome 19. Additional analysis showed that this chromosome was enriched with genome variation and differential expression densities in the same HPV-CCDC106 integrated carcinoma sample. More importantly, we identified that HPV divided one topologically associated domain (TAD) into two TADs and hijacked an enhancer from PEG3 to CCDC106, thus leading to a decrease in PEG3 expression and high CCDC106 expression. This expression dysregulation was further confirmed by 10 samples exhibiting the same HPV-CCDC106 integration from our cohort. Interpretation: We found that HPV-CCDC106 integration tended to alter the architecture of the local chromosome and hijacked an enhancer via 3D genome structure remodeling, thus providing insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis. Funding Statement: This work was supported by Natural Science Foundation of China (81630060 to P. W., 81830074 and 81772786 to H. W., 81572569 to G. C., 31771402 to G. L. and 81772775 to J. W.), National Science and Technology Major Project (2018ZX10301402-002 to Q. G.), and the research-oriented clinician funding program of Tongji Medical College, Huazhong University of Science and Technology for P.W. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: All tumor specimens, ThinPrep cytologic test (TCT) samples, and normal cervical tissues were obtained from the Department of Gynecological Oncology at Tongji Hospital under ethics board approval (TJ-IRB20180611) and with documented informed consent from all patients.