Lactylation modification is a new type of protein post-translational modification,which medi-ates the covalent binding of lactic acid groups to lysine residues through amide bonds,thus changing pro-tein function and intracellular signal transduction process.Lactylation modifications can be broadly cate-gorized into two types:histone lactylation and non-histone lactylation,both of which are dynamically and precisely regulated by the"Writer-Eraser"enzyme system.Among them,non-histone lactylation,mainly regulated by enzymes such as AARS1 and SIRT3,plays an important role in the progression of many dis-eases,including tumor metabolic reprogramming,ROS stress and signal pathway regulation.Especially in tumors,non-histone lactylation is closely related to tumor proliferation,immune escape and drug re-sistance.Therefore,an in-depth study of the role of non-histone lactylation in the progression of tumors is expected to provide new targets and strategies for the accurate diagnosis and treatment of tumors.It is noteworthy that in the context of non-histone lactylation modification,the interference effect of acetylation modification cannot be ignored.Lactylation and acetylation share similar"writer"and"eraser"enzymes and exhibit overlapping modification sites,suggesting the possibility of functional crosstalk between the two.Due to the current lack of specific editing tools targeting lysine lactylation,it remains challenging to definitively determine whether lactylation plays a predominant regulatory role.This article reviews the re-search progress of non-histone lactylation in tumors in recent years.

Ferroptosis is a new form of programmed cell death with iron-dependent lipid peroxidation as its core.A variety of metabolites are involved in the regulation of ferroptosis,among which lipid metabo-lism plays an important role.The most classic lipid metabolism-mediated ferroptosis mechanism is that phospholipids containing polyunsaturated fatty acyl chain(PUFA-PLs)located on biological membranes undergo super-threshold peroxidation,which leads to the destruction of membrane structure and function.In addition,special lipids containing polyunsaturated fatty acid(PUFA),such as phospholipid with dia-cyl-PUFA tails(PL-PUFA2),polyunsaturated ether phospholipid(PUFA-ePL),cholesterol ester con-taining polyunsaturated fatty acyl chain(PUFA-CE)have also been found to be involved in the ferropto-sis process by providing PUFA for peroxidation.Lipid droplets was also found to regulate the sensitivity of ferroptosis through storing and releasing PUFA.Intermediates and derivatives of cholesterol metabolism are mainly involved in the negative regulation of ferroptosis,whereas different classes of sphingolipids were reported to have inconsistent regulatory directions for ferroptosis.A large number of previous studies have confirmed that ferroptosis is closely related to the metastasis and drug resistance of gastrointestinal tumors,therefore,we further summarized the lipid metabolism mechanisms related to ferroptosis resist-ance in gastrointestinal tumor cells,such as weakening the anabolism and peroxidation processes of PU-FA-PLs,enhancing the ferroptosis defense system and so on.At the same time,we elaborated on the re-lationship between cholesterol metabolism,lipid drop metabolism,sphingolipid metabolism,and ferropto-sis resistance in gastrointestinal tumors.Targeting these specific lipids,metabolic enzymes,and pathways to regulate ferroptosis has important clinical potential value.It is expected to provide new ideas for finding new diagnostic and prognostic markers,therapeutic drugs,and reversing chemotherapy resistance for gas-trointestinal tumors.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Lactylation modification is a new type of protein post-translational modification,which medi-ates the covalent binding of lactic acid groups to lysine residues through amide bonds,thus changing pro-tein function and intracellular signal transduction process.Lactylation modifications can be broadly cate-gorized into two types:histone lactylation and non-histone lactylation,both of which are dynamically and precisely regulated by the"Writer-Eraser"enzyme system.Among them,non-histone lactylation,mainly regulated by enzymes such as AARS1 and SIRT3,plays an important role in the progression of many dis-eases,including tumor metabolic reprogramming,ROS stress and signal pathway regulation.Especially in tumors,non-histone lactylation is closely related to tumor proliferation,immune escape and drug re-sistance.Therefore,an in-depth study of the role of non-histone lactylation in the progression of tumors is expected to provide new targets and strategies for the accurate diagnosis and treatment of tumors.It is noteworthy that in the context of non-histone lactylation modification,the interference effect of acetylation modification cannot be ignored.Lactylation and acetylation share similar"writer"and"eraser"enzymes and exhibit overlapping modification sites,suggesting the possibility of functional crosstalk between the two.Due to the current lack of specific editing tools targeting lysine lactylation,it remains challenging to definitively determine whether lactylation plays a predominant regulatory role.This article reviews the re-search progress of non-histone lactylation in tumors in recent years.

Ferroptosis is a new form of programmed cell death with iron-dependent lipid peroxidation as its core.A variety of metabolites are involved in the regulation of ferroptosis,among which lipid metabo-lism plays an important role.The most classic lipid metabolism-mediated ferroptosis mechanism is that phospholipids containing polyunsaturated fatty acyl chain(PUFA-PLs)located on biological membranes undergo super-threshold peroxidation,which leads to the destruction of membrane structure and function.In addition,special lipids containing polyunsaturated fatty acid(PUFA),such as phospholipid with dia-cyl-PUFA tails(PL-PUFA2),polyunsaturated ether phospholipid(PUFA-ePL),cholesterol ester con-taining polyunsaturated fatty acyl chain(PUFA-CE)have also been found to be involved in the ferropto-sis process by providing PUFA for peroxidation.Lipid droplets was also found to regulate the sensitivity of ferroptosis through storing and releasing PUFA.Intermediates and derivatives of cholesterol metabolism are mainly involved in the negative regulation of ferroptosis,whereas different classes of sphingolipids were reported to have inconsistent regulatory directions for ferroptosis.A large number of previous studies have confirmed that ferroptosis is closely related to the metastasis and drug resistance of gastrointestinal tumors,therefore,we further summarized the lipid metabolism mechanisms related to ferroptosis resist-ance in gastrointestinal tumor cells,such as weakening the anabolism and peroxidation processes of PU-FA-PLs,enhancing the ferroptosis defense system and so on.At the same time,we elaborated on the re-lationship between cholesterol metabolism,lipid drop metabolism,sphingolipid metabolism,and ferropto-sis resistance in gastrointestinal tumors.Targeting these specific lipids,metabolic enzymes,and pathways to regulate ferroptosis has important clinical potential value.It is expected to provide new ideas for finding new diagnostic and prognostic markers,therapeutic drugs,and reversing chemotherapy resistance for gas-trointestinal tumors.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective:To investigate the effect of GRIM-19 on the resistance of carcinoma cells to the chemotherapeutic agent docetaxel in the treatment of PCa.Methods:Using siRNA technology to interfere with the gene expression in PCa cells,we estab-lished a model of GRIM-19 overexpression/knockdown in PCa cells.We investigated the effect of different expression levels of GRIM-19 on docetaxel-induced death of the PCa cells by qPCR,Western blot and flow cytometry,and assessed the value of GRIM-19 in re-ducing the chemotherapy-resistance of PCa cells.Results:GRIM-19 was down-regulated in PCa tissues and cells.Knockout of GRIM-19 significantly decreased the expression of siGRIM19 in the PC-3 and LNCaP cells,and reduced their death rate when treated with docetaxel compared with the control group.The expressions of GRIM-19 mRNA and protein were remarkably upregulated after transfection with GRIM-19,and the overexpressed GRIM-19 promoted the death of the PC-3 and LNCaP cells treated with docetaxel in a dose-dependent manner.Flow cytometry analysis showed a lower apoptosis rate of PC-3-R cells than that of PC-3 cells at different time points of docetaxel-induction at different doses.Conclusion:GRIM-19 is a PCa suppressor gene with a significant facilitating effect on the apoptosis of PCa cells,and the overexpression of GRIM-19 promotes docetaxel-induced PCa cell death and improves the sensitivity of chemotherapy.