We performed a genome-wide association study of esophageal squamous cell carcinoma (ESCC) by genotyping 1,077 individuals with ESCC and 1,733 control subjects of Chinese Han descent. We selected 18 promising SNPs for replication in an additional 7,673 cases of ESCC and 11,013 control subjects of Chinese Han descent and 303 cases of ESCC and 537 control subjects of Chinese Uygur-Kazakh descent. We identified two previously unknown susceptibility loci for ESCC: PLCE1 at 10q23 (P(Han combined for ESCC) = 7.46 x 10(-56), odds ratio (OR) = 1.43; P(Uygur-Kazakh for ESCC) = 5.70 x 10(-4), OR = 1.53) and C20orf54 at 20p13 (P(Han combined for ESCC) = 1.21 x 10(-11), OR = 0.86; P(Uygur-Kazakh for ESCC) = 7.88 x 10(-3), OR = 0.66). We also confirmed association in 2,766 cases of gastric cardia adenocarcinoma cases and the same 11,013 control subjects (PLCE1, P(Han for GCA) = 1.74 x 10(-39), OR = 1.55 and C20orf54, P(Han for GCA) = 3.02 x 10(-3), OR = 0.91). PLCE1 and C20orf54 have important biological implications for both ESCC and GCA. PLCE1 might regulate cell growth, differentiation, apoptosis and angiogenesis. C20orf54 is responsible for transporting riboflavin, and deficiency of riboflavin has been documented as a risk factor for ESCC and GCA.

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Genome-wide association studies have identified susceptibility loci for esophageal squamous cell carcinoma (ESCC). We conducted a meta-analysis of all single-nucleotide polymorphisms (SNPs) that showed nominally significant P-values in two previously published genome-wide scans that included a total of 2961 ESCC cases and 3400 controls. The meta-analysis revealed five SNPs at 2q33 with P< 5 × 10(-8), and the strongest signal was rs13016963, with a combined odds ratio (95% confidence interval) of 1.29 (1.19-1.40) and P= 7.63 × 10(-10). An imputation analysis of 4304 SNPs at 2q33 suggested a single association signal, and the strongest imputed SNP associations were similar to those from the genotyped SNPs. We conducted an ancestral recombination graph analysis with 53 SNPs to identify one or more haplotypes that harbor the variants directly responsible for the detected association signal. This showed that the five SNPs exist in a single haplotype along with 45 imputed SNPs in strong linkage disequilibrium, and the strongest candidate was rs10201587, one of the genotyped SNPs. Our meta-analysis found genome-wide significant SNPs at 2q33 that map to the CASP8/ALS2CR12/TRAK2 gene region. Variants in CASP8 have been extensively studied across a spectrum of cancers with mixed results. The locus we identified appears to be distinct from the widely studied rs3834129 and rs1045485 SNPs in CASP8. Future studies of esophageal and other cancers should focus on comprehensive sequencing of this 2q33 locus and functional analysis of rs13016963 and rs10201587 and other strongly correlated variants.

To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.

IMPORTANCE Lung cancer screening has been widely implemented in Europe and the US. However, there is little evidence on participation and diagnostic yields in population-based lung cancer screening in China. OBJECTIVE To assess the participation rate and detection rate of lung cancer in a population-based screening program and the factors associated with participation. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used data from the Cancer Screening Program in Urban China from October 2013 to October 2019, with follow-up until March 10, 2020. The program is conducted at centers in 8 cities in Henan Province, China. Eligible participants were aged 40 to 74 and were evaluated for a high risk for lung cancer using an established risk score system. MAIN OUTCOMES AND MEASURES Overall and group-specific participation rates by common factors, such as age, sex, and educational level, were calculated. Differences in participation rates between those groups were compared. The diagnostic yield of both screening and nonscreening groups was calculated. RESULTS The study recruited 282 377 eligible participants and included 55 428 with high risk for lung cancer; the mean (SD) age was 55.3 (8.1) years, and 34 966 participants (63.1%) were men. A total of 22 260 participants underwent LDCT (participation rate, 40.16%; 95% CI, 39.82%-40.50%). The multivariable logistic regression model showed that female sex (odds ratio [OR], 1.64; 95% CI,