Pre-therapeutic molecular biomarkers of pathological response to neoadjuvant chemotherapy in gastric and esophago-gastric junction adenocarcinoma: A systematic review and meta-analysis

HIST1H2BK predicts neoadjuvant-chemotherapy response and mediates 5-fluorouracil resistance of gastric cancer cells

BACKGROUND

Neoadjuvant chemotherapy (NACT) is routinely used to treat patients with advanced gastric cancer (AGC). However, the identification of reliable markers to determine which AGC patients would benefit from NACT remains challenging.

METHODS

A systematic screening of plasma proteins between NACT-sensitive and NACT-resistant AGC patients was performed by a mass spectrometer (n = 6). The effect of the most differential plasma protein was validated in two independent cohorts with AGC patients undergoing NACT (ELISA cohort: n = 155; Validated cohort: n = 203). The expression of this candidate was examined in a cohort of AGC tissues using immunohistochemistry (n = 34). The mechanism of this candidate on 5-Fluorouracil (5-FU) resistance was explored by cell-biology experiments in vitro and vivo.

RESULTS

A series of differential plasma proteins between NACT-sensitive and NACT-resistant AGC patients was identified. Among them, plasma HIST1H2BK was validated as a significant biomarker for predicting NACT response and prognosis. Moreover, HIST1H2BK was over-expression in NACT-resistant tissues compared to NACT-sensitive tissues in AGC. Mechanistically, HIST1H2BK inhibited 5-FU-induced apoptosis by upregulating A2M transcription and then activating LRP/PI3K/Akt pathway, thereby promoting 5-FU resistance in GC cells. Intriguingly, HIST1H2BK-overexpressing 5-FU-resistant GC cells propagated resistance to 5-FU-sensitive GC cells through the secretion of HIST1H2BK.

CONCLUSION

This study highlights significant differences in plasma protein profiles between NACT-resistant and NACT-sensitive AGC patients. Plasma HIST1H2BK emerged as an effective biomarker for achieving more accurate NACT in AGC. The mechanism of intracellular and secreted HIST1H2BK on 5-FU resistance provided a novel insight into chemoresistance in AGC.

Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study

Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.