APOBEC3-related mutations in the spike protein-encoding region facilitate SARS-CoV-2 evolution.

SARS-CoV-2 evolves gradually to cause COVID-19 epidemic. One of driving forces of SARS-CoV-2 evolution might be activation of apolipoprotein B mRNA editing catalytic subunit-like protein 3 (APOBEC3) by inflammatory factors. Here, we aimed to elucidate the effect of the APOBEC3-related viral mutations on the infectivity and immune evasion of SARS-CoV-2. The APOBEC3-related C > U mutations ranked as the second most common mutation types in the SARS-CoV-2 genome. mRNA expression of APOBEC3A (A3A), APOBEC3B (A3B), and APOBEC3G (A3G) in peripheral blood cells increased with disease severity. A3B, a critical member of the APOBEC3 family, was significantly upregulated in both severe and moderate COVID-19 patients and positively associated with neutrophil proportion and COVID-19 severity. We identified USP18 protein, a key molecule centralizing the protein-protein interaction network of key APOBEC3 proteins. Furthermore, mRNA expression of USP18 was significantly correlated to ACE2 and TMPRSS2 expression in the tissue of upper airways. Knockdown of USP18 mRNA significantly decreased A3B expression. Ectopic expression of A3B gene increased SARS-CoV-2 infectivity. C > U mutations at S371F, S373L, and S375F significantly conferred with the immune escape of SARS-CoV-2. Thus, APOBEC3, whose expression are upregulated by inflammatory factors, might promote SARS-CoV-2 evolution and spread via upregulating USP18 level and facilitating the immune escape. A3B and USP18 might be therapeutic targets for interfering with SARS-CoV-2 evolution.

Tumor Microenvironment Composition and Related Therapy in Hepatocellular Carcinoma.

Globally, primary liver cancer is the third leading cause of cancer death, and hepatocellular carcinoma (HCC) accounts for 75%-95%. The tumor microenvironment (TME), composed of the extracellular matrix, helper cells, immune cells, cytokines, chemokines, and growth factors, promotes the immune escape, invasion, and metastasis of HCC. Tumor metastasis and postoperative recurrence are the main threats to the long-term prognosis of HCC. TME-related therapies are increasingly recognized as effective treatments. Molecular-targeted therapy, immunotherapy, and their combined therapy are the main approaches. Immunotherapy, represented by immune checkpoint inhibitors (ICIs), and targeted therapy, highlighted by tyrosine kinase inhibitors (TKIs), have greatly improved the prognosis of HCC. This review focuses on the TME compositions and emerging therapeutic approaches to TME in HCC.

APOBEC3A suppresses cervical cancer via apoptosis.

Background: Family members of Apolipoprotein B mRNA-editing enzyme catalytic 3 (APOBEC3) play critical roles in cancer evolution and development. However, the role of APOBEC3A in cervical cancer remains to be clarified. Methods: We used bioinformatics to investigate APOBEC3A expression and outcomes using The Cancer Genome Atlas (TCGA)-cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) dataset, GTEx, and GSE7803. Immunohistochemistry was then used to identify APOBEC3A's expression pattern. We performed Cell Counting Kit-8, wound-healing, Transwell, and flow cytometry assays to measure proliferation, migration, invasion, and apoptosis, respectively, using the SiHa and HeLa cell lines transfected with APOBEC3A. BALB/c nude mice were used to investigate the effects of APOBEC3A in vivo. The phosphorylated gamma-H2AX staining assay was applied to measure DNA damage. RNA sequencing (RNA-Seq) was applied to explore APOBEC3A-related signaling pathways. Results: APOBEC3A was more significantly expressed in cancer tissues than in adjacent normal tissues. Higher expression of APOBEC3A was associated with better outcomes in TCGA-CESC and GTEx. Immunohistochemistry showed that the expression of APOBEC3A was significantly higher in cancer tissues than in normal tissues. Transfection experiments showed that APOBEC3A inhibited proliferation, upregulated S-phase cells, inhibited migration and invasion, induced DNA damage, and promoted apoptosis. Overexpression of APOBEC3A inhibited tumor formation in the mouse model. RNA-seq analysis showed that ectopic expression of APOBEC3A inhibited several cancer-associated signaling pathways. Conclusions: APOBEC3A is significantly upregulated in cervical cancer, and higher expression of APOBEC3A is associated with better outcomes. APOBEC3A is a tumor suppressor whose overexpression induces apoptosis in cervical cancer.

Innate and adaptive immunity to SARS-CoV-2 and predisposing factors.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2), has affected all countries worldwide. Although some symptoms are relatively mild, others are still associated with severe and even fatal clinical outcomes. Innate and adaptive immunity are important for the control of SARS-CoV-2 infections, whereas a comprehensive characterization of the innate and adaptive immune response to COVID-19 is still lacking and the mechanisms underlying immune pathogenesis and host predisposing factors are still a matter of scientific debate. Here, the specific functions and kinetics of innate and adaptive immunity involved in SARS-CoV-2 recognition and resultant pathogenesis are discussed, as well as their immune memory for vaccinations, viral-mediated immune evasion, and the current and future immunotherapeutic agents. We also highlight host factors that contribute to infection, which may deepen the understanding of viral pathogenesis and help identify targeted therapies that attenuate severe disease and infection.

Potential Role of the Fragile Histidine Triad in Cancer Evo-Dev.

Cancer development follows an evolutionary pattern of "mutation-selection-adaptation" detailed by Cancer Evolution and Development (Cancer Evo-Dev), a theory that represents a process of accumulating somatic mutations due to the imbalance between the mutation-promoting force and the mutation-repairing force and retro-differentiation of the mutant cells to cancer initiation cells in a chronic inflammatory microenvironment. The fragile histidine triad (FHIT) gene is a tumor suppressor gene whose expression is often reduced or inactivated in precancerous lesions during chronic inflammation or virus-induced replicative stress. Here, we summarize evidence regarding the mechanisms by which the FHIT is inactivated in cancer, including the loss of heterozygosity and the promoter methylation, and characterizes the role of the FHIT in bridging macroevolution and microevolution and in facilitating retro-differentiation during cancer evolution and development. It is suggested that decreased FHIT expression is involved in several critical steps of Cancer Evo-Dev. Future research needs to focus on the role and mechanisms of the FHIT in promoting the transformation of pre-cancerous lesions into cancer.

The mortalities of female-specific cancers in China and other countries with distinct socioeconomic statuses: A longitudinal study.

INTRODUCTION: Female-specific cancers seriously affect physical and psychological health of women worldwide. OBJECTIVES: We aimed to elucidate trends in the age-standardized mortality rates (ASMRs) of breast cancer, cervical cancer, uterine cancer, and ovarian cancer in female populations with different socioeconomic statuses in China and in countries with different Human Development Index (HDI). METHODS: A longitudinal study was performed using the data of cancer death in China and other 39 countries. The mortality rates were standardized with the Segi's world population. Trends in the mortalities were exhibited by estimated annual percentage change (EAPC). Pearson correlation was used to assess the association between EAPC and HDI. RESULTS: In mainland China, female breast cancer, cervical cancer, uterine cancer, and ovarian cancer accounted for 6.60 %, 4.21 %, 2.50 %, and 2.02 % of cancer death (n = 1,314,040) in women with 1,220,251,032 person-years, respectively. The ASMRs of cervical cancer (EAPC = 3.87 %, P < 0.001) and ovarian cancer (EAPC = 1.81 %, P < 0.001) increased, that of female breast cancer unchanged, whereas that of uterine cancer was extremely higher and rapidly decreased (EAPC =  - 7.65 %, P < 0.001), during 2004-2019. The ASMRs of female breast and ovarian cancers were higher in urban and developed regions than in rural and undeveloped regions, in contrast to cervical and uterine cancers. The ASMRs of female breast and ovarian cancers were lower in China than in other countries, in contrast to uterine cancer. The ASMR of cervical cancer decreased, that of uterine cancer increased, in other countries during 2004-2017. EAPCs for the ASMRs of breast and ovarian cancers were inversely correlated to HDI. CONCLUSION: The ASMRs of cervical and ovarian cancers increased, in contrast to uterine cancer, in China during socioeconomic transition. Trends in the ASMRs of breast and ovarian cancers were inversely associated with HDI. These data help control female-specific cancers.

Study on Risk Factors of Peripheral Neuropathy in Type 2 Diabetes Mellitus and Establishment of Prediction Model.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability. METHODS: Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, -2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model. RESULTS: Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power. CONCLUSION: Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN.

Nomogram Based on Risk Factors for Type 2 Diabetes Mellitus Patients with Coronary Heart Disease.

INTRODUCTION: This study aimed to study risk factors for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) patients and establish a clinical prediction model. RESEARCH DESIGN AND METHODS: A total of 3402 T2DM patients were diagnosed by clinical doctors and recorded in the electronic medical record system (EMRS) of six Community Health Center Hospitals from 2015 to 2017, including the communities of Huamu, Jinyang, Yinhang, Siping, Sanlin and Daqiao. From September 2018 to September 2019, 3361 patients (41 patients were missing) were investigated using a questionnaire, physical examination, and biochemical index test. After excluding the uncompleted data, 3214 participants were included in the study and randomly divided into a training set (n = 2252) and a validation set (n = 962) at a ratio of 3:1. Through lead absolute shrinkage and selection operator (LASSO) regression analysis and logistic regression analysis of the training set, risk factors were determined and included in a nomogram. The C-index, receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were used to validate the distinction, calibration and clinical practicality of the model. RESULTS: Age, T2DM duration, hypertension (HTN), hyperuricaemia (HUA), body mass index (BMI), glycosylated haemoglobin A1c (HbA1c), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C) were significant factors in this study. The C-index was 0.750 (0.724-0.776) based on the training set and 0.767 (0.726-0.808) based on the validation set. Through ROC analysis, the set area was 0.750 for the training set and 0.755 for the validation set. The calibration test indicated that the S:P of the prediction model was 0.982 in the training set and 0.499 in the validation set. The decision curve analysis showed that the threshold probability of the model was 16-69% in the training set and 16-73% in the validation set. CONCLUSION: Based on community surveys and data analysis, a prediction model of CHD in T2DM patients was established.

Study on the Risk Factors for Hyperuricaemia and Related Vascular Complications in Patients with Type 2 Diabetes Mellitus.

PURPOSE: The study aimed to identify diseases that exhibit significant differences between hyperuricaemia (HUA) and non-hyperuricaemia (NHUA) groups and analyse the risk factors for HUA based on the related diseases in type 2 diabetes mellitus (T2DM). METHODS: A total of 3264 T2DM patients were investigated from 2013 to 2017 in the Jinyang and Sanlin communities by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 3000 patients (264 patients were missing during follow-up) were investigated with questionnaires, physical examinations and biochemical index tests. After removing missing values, 2899 patients were divided into HUA and NHUA groups. The chi-square test was used to identify diseases with differences. Using Lasso analysis and logistic regression analysis, risk factors for HUA based on the related diseases were obtained. The C-index, receiver operating characteristic (ROC) curve and calibration plot were used to validate the discrimination and accuracy of the factors. RESULTS: The chi-square test showed that there were significant differences in coronary heart disease (CHD) and diabetic nephropathy (DN) between the HUA group and the NHUA group. Through Lasso regression, glycosylated haemoglobin A1c (HbA1c), triglyceride (TG), blood urea nitrogen (BUN) and serum creatinine (SCR) were screened in the CHD group. Body mass index (BMI), HbA1c, total cholesterol (TC), TG, BUN, SCR and urine microalbumin (UMA) were screened in the DN group. The P-value of all the variables was less than 0.05. Through the C-index, calibration, and ROC curve analyses, these risk factors had medium accuracy. CONCLUSION: HUA was significantly related to CHD and DN. The level of UA was correlated with HbA1c, TG, BUN, and SCR based on CHD. The level of UA was associated with BMI, HbA1c, TC, TG, BUN, SCR, and UMA based on DN.

Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study.

PURPOSE: This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample. METHODS: We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model. RESULTS: The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice. CONCLUSION: This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.