[Development and validation of predictive models for esophageal squamous cell carcinoma and its precancerous lesions using terminal motif analysis in circulating cell-free DNA].

Objectives: To develop and validate predictive models for esophageal squamous cell carcinoma (ESCC) using circulating cell-free DNA (cfDNA) terminal motif analysis. The goal was to improve the non-invasive detection of early-stage ESCC and its precancerous lesions. Methods: Between August 2021 and November 2022, we prospectively collected plasma samples from 448 individuals at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences for cfDNA extraction, library construction, and sequencing. We analyzed 201 cases of ESCC, 46 high-grade intraepithelial neoplasia (HGIN), 46 low-grade intraepithelial neoplasia (LGIN), 176 benign esophageal lesions, and 29 healthy controls. Participants, including ESCC patients and control subjects, were randomly assigned to a training set (n=284) and a validation set (n=122). The training cohort underwent z-score normalization of cfDNA terminal motif matrices and a selection of distinctive features differentiated ESCC cases from controls. The random forest classifier, Motif-1 (M1), was then developed through principal component analysis, ten-fold cross-validation, and recursive feature elimination. M1's efficacy was then validated in the validation and precancerous lesion sets. Subsequently, individuals with precancerous lesions were included in the dataset and participants were randomly allocated to newly formed training (n=243), validation (n=105), and test (n=150) cohorts. Using the same procedure as M1, we trained the Motif-2 (M2) random forest model with the training cohort. The M2 model's accuracy was then confirmed in the validation cohort to establish the optimal threshold and further tested by performing validation in the test cohort. Results: We developed two cfDNA terminal motif-based predictive models for ESCC and associated precancerous conditions. The first model, M1, achieved a sensitivity of 90.0%, a specificity of 77.4%, and an area under the curve (AUC) of 0.884 in the validation cohort. For LGIN, HGIN, and T1aN0 stage ESCC, M1's sensitivities were 76.1%, 80.4%, and 91.2% respectively. Notably, the sensitivity for jointly predicting HGIN and T1aN0 ESCC reached 85.0%. Both the predictive accuracy and sensitivity increased in line with the cancer's progression (P＜0.001). The second model, M2, exhibited a sensitivity of 87.5%, a specificity of 77.4%, and an AUC of 0.857 in the test cohort. M2's sensitivities for detecting precancerous lesions and ESCC were 80.0% and 89.7%, respectively, and it showed a combined sensitivity of 89.4% for HGIN and T1aN0 stage ESCC. Conclusions: Two predictive models based on cfDNA terminal motif analysis for ESCC and its precancerous lesions are developed. They both show high sensitivity and specificity in identifying ESCC and its precancerous stages, indicating its potential for early ESCC detection.

Dexamethasone suppresses apoptosis in a human gastric cancer cell line through modulation of bcl-x gene expression.

Treatment of human gastric cancer TMK-1 cells with transcription and translation inhibitors rapidly triggered cell apoptosis. Along with cell apoptosis, the Bcl-xS level was markedly upregulated suggesting a crucial role of this protein in promoting the apoptotic process. In the presence of dexamethasone, however, cell apoptosis was greatly attenuated as demonstrated by DNA histogram shift and DNA fragmentation. Studies using the glucocorticoid receptor antagonist RU486 indicated that attenuation of apoptosis was mediated through glucocorticoid receptors. Dexamethasone not only suppressed the apoptosis-associated upregulation of Bcl-xS but also enhanced the basal level of Bcl-xL in the cells. In addition, bcl-x mRNA stability was significantly extended in the presence of dexamethasone. These results indicate that dexamethasone exerted a protective effect and delayed apoptosis of TMK-1 cells by modulating bcl-x gene expression.

Effects of transcription and translation inhibitors on a human gastric carcinoma cell line. Potential role of Bcl-X(S) in apoptosis triggered by these inhibitors.

The effects of the macromolecular synthesis inhibitors 5,6-dichloro-1-beta-D-ribofuranosyl benzimidazole (DRB), actinomycin D, and cycloheximide on the human gastric cancer TMK-1 cell line were studied. These agents inhibited DNA, RNA, or protein synthesis efficiently and induced cell death rapidly in a wide range of concentrations. After 8 hr of exposure to these agents, the cells exhibited morphological features of apoptosis, including cell shrinkage, nuclear condensation, DNA fragmentation, and formation of apoptotic bodies. Western blot analysis revealed that these inhibitors altered the protein levels of apoptosis-related gene products such as c-Myc, Bcl-X(S), and the mutant p53 (mp53) in TMK-1 cells markedly. The c-myc mRNA and protein levels were decreased initially and were then induced markedly to a new level after 4 hr of exposure to DRB, a RNA polymerase II inhibitor. The Bcl-X(S) levels were increased rapidly after treatment with all of these agents, whereas the levels of Bcl-X(L) and Bax remained largely unchanged. Northern blot analysis indicated that the c-myc overexpression is concomitant to DRB-induced DNA fragmentation and that the increased mp53 protein level was mainly a posttranscriptional event. Our observations suggest that the up-regulation of Bcl-X(S) may serve as an important mechanism for the apoptosis triggered by these inhibitors. This study also provides evidence for the notion that interference with the cellular survival pathway may lead to apoptosis.

Approximations of bayes and minimax risks and the least favorable distribution.

In this paper we present a method for approximating the risks and Bayes risk associated with a Bayes decision procedure. Additionally, our method leads to approximating the least favorable distribution and the risk associated with the minimax decision procedure. We assume two states of nature (or classes of patterns) and multivariate probability density functions. Taylor series expansions are used, and an nth-order polynomial equation derived from such expansions provides an approximation to one of the least favorable probabilities. An application to a normally distributed random vector of observables is presented with numerical comparisons. The method can be generalized to cases having more than two states of nature by using Taylor series expansions in several variables.

The relationship of the bayes risk to certain separability measures in normal classification.

For the problem of classifying an element (e.g., an unknown pattern) into one of two given categories where the associated observables are distributed according to one of two known multivariate normal populations having a common covariance matrix, it is shown that the minimum Bayes risk is a strict monotonic function of certain separability or statistical distance measures regardless of the a priori probabilities and the assigned loss function. However, for the associated conditional expected losses, strict monotonicity holds, if and only if a certain condition dependent on these probabilities and the given loss function is satisfied. These results remain valid for classification problems in which the observable can be transformed by a one-to-one differentiable mapping to normality.