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.

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.

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.

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.

Circular RNAs(CircRNAs)are a class of non-coding RNAs with a covalently closed-loop structure,high stability,and tissue specificity,with the production mechanisms different from linear RNAs.Recent studies have discovered that some CircRNAs can encode proteins via cap-independent translation mechanisms such as internal ribosome entry site,N6-methyladenosine,and rolling loop translation.The encoded proteins regulate homologous linear proteins or downstream signaling pathways via protein bait or other mecha-nisms,thereby exerting biological functions.Studies have shown that CircRNAs play a role in various diseases,especially in tumor progression,proliferation,invasion,and metastasis and immune regulation.Therefore,by elucidating the expression and roles of proteins encoded by CircRNAs in tumorigenesis and development,this pa-per is expected to provide new tumor markers and potential targets for tumor diagnosis and treatment.