ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection. METHODS: The study encompassed three phases: discovery, verification, and validation, comprising a total of 1309 individuals. Serum autoantibodies were profiled using the HuProtTM human proteome microarray, and autoantibody levels were measured using the enzyme-linked immunosorbent assay (ELISA). Twelve machine learning algorithms were employed to construct diagnostic models, and evaluated using the area under the receiver operating characteristic curve (AUC). The model application was facilitated through R Shiny, providing a graphical interface. RESULTS: Thirteen autoantibodies targeting TAAs (CAST, FAM131A, GABPA, HDAC1, HDGFL1, HSF1, ISM2, PTMS, RNF219, SMARCE1, SNAP25, SRPK2, and ZPR1) were identified in the discovery phase. Subsequent verification and validation phases identified five TAAbs (anti-CAST, anti-HDAC1, anti-HSF1, anti-PTMS, and anti-ZPR1) that exhibited significant differences between ESCC and control subjects (P < 0.05). The support vector machine (SVM) model demonstrated robust performance, with AUCs of 0.86 (95% CI: 0.82-0.89) in the training set and 0.83 (95% CI: 0.78-0.88) in the test set. For early-stage ESCC, the SVM model achieved AUCs of 0.83 (95% CI: 0.79-0.88) in the training set and 0.83 (95% CI: 0.77-0.90) in the test set. Notably, promising results were observed for high-grade intraepithelial neoplasia, with an AUC of 0.87 (95% CI: 0.77-0.98). The web-based implementation of the early ESCC diagnostic tool is publicly accessible at https://litdong.shinyapps.io/ESCCPred/ . CONCLUSION: This study provides a promising and easy-to-use diagnostic prediction model for early ESCC detection. It holds promise for improving early detection strategies and has potential implications for public health.

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

AIMS: This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). METHODS: A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. RESULTS: A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). CONCLUSIONS: A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Paeoniflorin prevents hypoxia-induced epithelial-mesenchymal transition in human breast cancer cells.

Paeoniflorin (PF) is a monoterpene glycoside extracted from the root of Paeonia lactiflora Pall. Previous studies have demonstrated that PF inhibits the growth, invasion, and metastasis of tumors in vivo and in vitro. However, the effect of PF on hypoxia-induced epithelial-mesenchymal transition (EMT) in breast cancer cells remains unknown. Therefore, the objective of this study was to investigate the effect of PF on hypoxia-induced EMT in breast cancer cells, as well as characterize the underlying mechanism. The results presented in this study demonstrate that PF blocks the migration and invasion of breast cancer cells by repressing EMT under hypoxic conditions. PF also significantly attenuated the hypoxia-induced increase in HIF-1α level. Furthermore, PF prevented hypoxia-induced expression of phosphorylated PI3K and Akt in MDA-MB-231 cells. In conclusion, PF prevented hypoxia-induced EMT in breast cancer cells by inhibiting HIF-1α expression via modulation of PI3K/Akt signaling pathway. This finding provides evidence that PF can serve as a therapeutic agent for the treatment of breast cancer.

Angelica sinensis polysaccharides promotes apoptosis in human breast cancer cells via CREB-regulated caspase-3 activation.

Angelica sinensis polysaccharide (ASP) is purified from the fresh roots of Angelica sinensis (AS). This traditional Chinese medicine has been used for thousands of years for treating gynecological diseases and used in functional foods for the prevention and treatment of various diseases, such as inflammation and cancer. The antitumor activity of ASP is related to its biological activities, because it suppresses a variety of pro-proliferative or anti-apoptotic factors that are dramatically expressed in cancer cells of given types. In this study, we show that angelica sinensis polysaccharide induced apoptosis in breast cancer cells of T47D over-expressing the Cyclic AMP response element binding protein (CREB), inducing apoptosis-related signaling pathway activity. The result also found that ASP caused cell death was linked to caspase activity, accompanied by the loss of mitochondrial membrane potential, cytochrome c release, and Bax translocation from the cytosol to the mitochondria. We found that ASP significantly affected the poly-ADP-ribose polymerase (PARP), Bcl-2 Associated X Protein (Bax), Bcl-2, Bcl-xL and apoptotic protease activating facter-1 (Apaf1) protein expression in a dose- and time-dependent manner. DAPI staining and Flow cytometry were used to analyze apoptosis. The nude mice xenograft model was used to evaluate the antitumor effect of ASP in vivo. ASP has profound antitumor effect on T47D cells, probably by inducing apoptosis through CREB signaling pathway. Thus, these results suggest that ASP would be a promising therapeutic agent for breast cancer.