Objective:To explore the distribution and hematological characteristics of rare thalassemia-associated mutations in Chenzhou region of Hunan Province with an aim to provide a basis for genetic counseling and effective prevention.Methods:A total of 37 370 individuals enrolled from January 2015 to December 2021 were screened by routine blood test and hemoglobin electrophoresis. The genotypes were determined with high-throughput sequencing.Results:A total of 8 455 thalassemia mutations (including 185 rare ones) were detected, which had involved 27 mutational types. Rare type α-Thalassemia --THAI and CD31 (AGG>AAG) have the typical microcytic hypochromic hematological features, whilst SEA-HPFH, CD14 (CTG>-TG), CD37 (TGG>TAG), -90(C>T), Codon 15 (G>A), IVS-Ⅰ-128 (T>G), CD86 (GCC>GC-) and Chinese Gγ+ (Aγδβ)0 had typical microcytic hypochromic and β-thalassemia-associated hematological features of elevated HbA2 or HbF. In addition, the -50(G>A)heterozygotes of β-thalassemia had normal or slightly decreased MCV and MCH without an increase in HbA2.Conclusion:Various forms of thalassemia-associated mutations have been identified in the Chenzhou region of Hunan Province. Above finding has facilitated development of preventive and control strategies for thalassemia as well as birth health programs.

OBJECTIVE: To assess the diagnostic value of whole exome sequencing (WES) for patients with intellectual disability (ID) or global developmental delay (GDD). METHODS: 134 individuals with ID or GDD who presented at Chenzhou First People's Hospital between May 2018 and December 2021 were selected as the study subjects. WES was carried out on peripheral blood samples of the patients and their parents, and candidate variants were verified by Sanger sequencing, copy number variation sequencing (CNV-seq) and co-segregation analysis. The pathogenicity of the variants was predicted based on the guidelines from the American College of Medical Genetics and Genomics (ACMG). RESULTS: A total of 46 pathogenic single nucleotide variants (SNVs) and small insertion/deletion (InDel) variants, 11 pathogenic genomic copy number variants (CNVs), and 1 uniparental diploidy (UPD) were detected, which yielded an overall detection rate of 43.28% (58/134). The 46 pathogenic SNV/InDel have involved 62 mutation sites in 40 genes, among which MECP2 was the most frequent (n = 4). The 11 pathogenic CNVs have included 10 deletions and 1 duplication, which have ranged from 0.76 to 15.02 Mb. A loss of heterozygosity (LOH) region of approximately 15.62 Mb was detected in 15q11.2q12 region in a patient, which was validated as paternal UPD based on the result of trio-WES. The patient was ultimately diagnosed as Angelman syndrome. CONCLUSION WES can detect not only SNV/InDel, but also CNV and LOH. By integrating family data, WES can accurately determine the origin of the variants and provide a useful tool for uncovering the genetic etiology of patients with ID or GDD.
Humans ; Exome Sequencing ; Intellectual Disability/genetics* ; DNA Copy Number Variations ; Mutation ; Loss of Heterozygosity

Background::Non-smokers account for a large proportion of lung cancer patients, especially in Asia, but the attention paid to them is limited compared with smokers. In non-smokers, males display a risk for lung cancer incidence distinct from the females—even after excluding the influence of smoking; but the knowledge regarding the factors causing the difference is sparse. Based on a large multicenter prospective cancer screening cohort in China, we aimed to elucidate the interpretable sex differences caused by known factors and provide clues for primary and secondary prevention.Methods::Risk factors including demographic characteristics, lifestyle factors, family history of cancer, and baseline comorbidity were obtained from 796,283 Chinese non-smoking participants by the baseline risk assessment completed in 2013 to 2018. Cox regression analysis was performed to assess the sex difference in the risk of lung cancer, and the hazard ratios (HRs) that were adjusted for different known factors were calculated and compared to determine the proportion of excess risk and to explain the existing risk factors.Results::With a median follow-up of 4.80 years, 3351 subjects who were diagnosed with lung cancer were selected in the analysis. The lung cancer risk of males was significantly higher than that of females; the HRs in all male non-smokers were 1.29 (95% confidence interval [CI]: 1.20-1.38) after adjusting for the age and 1.38 (95% CI: 1.28-1.50) after adjusting for all factors, which suggested that known factors could not explain the sex difference in the risk of lung cancer in non-smokers. Known factors were 7% (|1.29-1.38|/1.29) more harmful in women than in men. For adenocarcinoma, women showed excess risk higher than men, contrary to squamous cell carcinoma; after adjusting for all factors, 47% ([1.30-1.16]/[1.30-1]) and 4% ([7.02-6.75]/[7.02-1])) of the excess risk was explainable in adenocarcinoma and squamous cell carcinoma. The main causes of gender differences in lung cancer risk were lifestyle factors, baseline comorbidity, and family history.Conclusions::Significant gender differences in the risk of lung cancer were discovered in China non-smokers. Existing risk factors did not explain the excess lung cancer risk of all non-smoking men, and the internal causes for the excess risk still need to be explored; most known risk factors were more harmful to non-smoking women; further exploring the causes of the sex difference would help to improve the prevention and screening programs and protect the non-smoking males from lung cancers.

Objective To evaluate the value of chromosomal microarray analysis (CMA) for genetic evaluation of fetal ultrasound abnormality. Methods A total of 180 pregnant women with fetal abnormality detected by prenatal ultrasound diagnosis in the first trimester during the period from January 2020 through May 2022 were enrolled as the study subjects. All prenatal fetal screening samples were subjected to G-band karyotyping and CMA. Results G-band karyotyping detected normal karyotypes in 168 samples (93.85%) and abnormal karyotypes in 11 samples (6.15%), and CMA detected 17 positive samples (9.44%) and 163 negative samples (90.56%). The seventeen positive samples included 11 pathogenic copy number variations (CNVs) and 6 variants of unknown significance (VOUS), and there were 11 CMA-positive results consistent with G-band karyotyping, and 6 additional pathogenic CNVs mainly included microdeletion and microduplication syndromes. The detection rates of pathogenic CNVs were 11.11%, 2.63%, 2.78%, 4.00%, 0, 0, 11.11% and 0 among the fetuses with abnormal structure of the cardiovascular system, the lymphatic system, the nervous system, the digestive system, the cranial and face system, the skeletal system, the urinary system, and other system (χ² =8.188, P = 0.316). All eleven fetuses with pathogenic CNVs detected by CMA were all induced for abortion. Conclusion CMA improves the detection of genetic abnormality among fetuses with ultrasound abnormality in relative to G-band karyotyping, which is feasible for prenatal cytogenetic diagnosis among fetuses with ultrasound abnormality

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.

Objective:To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models.Methods:"lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared.Results:Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90.Conclusion:Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.

Objective:To develop a lung cancer risk prediction model for female non-smokers.Methods:Based on the Kailuan prospective dynamic cohort (2006.05-2015.12), a nested case-control study was conducted. Participants diagnosed with primary pathologically confirmed lung cancer during follow-up were identified as the case group, and others were identified as the control group. A total of 24 701 subjects were included in the study, including 86 lung cancer cases and 24 615 control population, respectively. Questionnaires, physical examinations, and laboratory tests were conducted to collect relevant information. Multivariable-adjusted logistic regressions were conducted to develop a lung cancer risk prediction model. Area Under the Curve (AUC) and Hosmer-Lemeshow tests were used to evaluate discrimination and calibration, respectively. Ten-fold cross-validation was used for internal validation.Results:Two sets of models were developed: the simple model (including age and monthly income) and the metabolic index model [including age, monthly income, fasting blood glucose (FBG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C)].The AUC (95%CI) [0.745 (0.719-0.771)] of the metabolic index model was higher than that of the simple prediction model [0.688 (0.660-0.716)] ( P=0.004). Both the simple model ( PHL=0.287) and the metabolic index model ( PHL=0.134) were well-calibrated. The results of ten-fold cross-validation indicated sufficient stability, with an average AUC of 0.699 and a standard error (SD) of 0.010. Conclusion:By incorporating metabolic markers, accurate and reliable lung cancer risk prediction model for female non smokers could be developed.

Objective: To investigate whether elevated levels of C-reactive protein (CRP) and neutrophil (NE) in the blood is associated with an increased risk of lung cancer incidence. Methods: From 2006 to 2007, all employees and retirees from Kailuan (Group) Limited liability Corporation were included in this Kailuan Cohort study. The last follow-up date was December 2015. Data on new cases of lung cancer were collected, and multivariable Cox proportional hazards regression models were used to the relationship between baseline CRP and NE at baseline and risk of lung cancer. Results: A total of 92 735 participants were enrolled in this study. During the follow-up, 850 new cases of lung cancer were identified. All subjects were divided into four groups according to the combination level of CRP and NE at baseline: CRP≤3 mg/L and NE≤4×10⁹/L(Group A), CRP≤3 mg/L and NE>4×10⁹/L(Group B), CRP>3 mg/L and NE≤4×10⁹/L(Group C), CRP>3 mg/L and NE>4×10⁹/L(Group D). The cumulative incidence of lung cancer were 950/100 000, 1 030/100 000, 1 081/100 000 and 1 596/100 000 in these four groups, respectively (P<0.001). Multivariate Cox proportional risk model showed that participants from Group D had an significantly increased 72% risks of lung cancer when compared to Group A (95% CI: 1.40~2.12, P<0.001). Stratified analyses gender showed that males in Group D had higher risk of lung cancer when compared with participants in Group A (HR=1.73, 95% CI: 1.40~2.15, P<0.001). Conclusion Elevated levels of CRP and NE might increase the risk of lung cancer.

Objective To investigate whether elevated levels of C?reactive protein ( CRP ) and neutrophil (NE) in the blood is associated with an increased risk of lung cancer incidence. Methods From 2006 to 2007, all employees and retirees from Kailuan (Group) Limited liability Corporation were included in this Kailuan Cohort study. The last follow?up date was December 2015. Data on new cases of lung cancer were collected, and multivariable Cox proportional hazards regression models were used to the relationship between baseline CRP and NE at baseline and risk of lung cancer. Results A total of 92 735 participants were enrolled in this study. During the follow?up, 850 new cases of lung cancer were identified. All subjects were divided into four groups according to the combination level of CRP and NE at baseline: CRP≤3 mg／L and NE≤4×109／L(Group A), CRP≤3 mg／L and NE＞4×109／L( Group B), CRP＞3 mg／L and NE≤4× 109／L(Group C), CRP＞3 mg／L and NE＞4×109／L(Group D). The cumulative incidence of lung cancer were 950／100 000, 1 030／100 000, 1 081／100 000 and 1 596／100 000 in these four groups, respectively (P＜0.001 ). Multivariate Cox proportional risk model showed that participants from Group D had an significantly increased 72% risks of lung cancer when compared to Group A ( 95% CI: 1.40～2.12, P＜0.001). Stratified analyses gender showed that males in Group D had higher risk of lung cancer when compared with participants in Group A (HR＝1.73, 95% CI: 1.40～2.15,P＜0.001).Conclusion Elevated levels of CRP and NE might increase the risk of lung cancer.