The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies seen in clinic and requires novel treatment options. Morin is a natural flavonoid extracted from the flower stalk of a highly valuable medicinal plant Prunella vulgaris L., which exhibits an anti-cancer effect in multiple types of tumors. However, the therapeutic effect and underlying mechanism of morin in treating GC remains elusive. The study aims to explore the therapeutic effect and underlying molecular mechanisms of morin in GC. METHODS: For in vitro experiments, the proliferation inhibition of morin was measured by cell counting kit-8 assay and colony formation assay in human GC cell line MKN45, human gastric adenocarcinoma cell line AGS, and human gastric epithelial cell line GES-1; for apoptosis analysis, microscopic photography, Western blotting, ubiquitination analysis, quantitative polymerase chain reaction analysis, flow cytometry, and RNA interference technology were employed. For in vivo studies, immunohistochemistry, biomedical analysis, and Western blotting were used to assess the efficacy and safety of morin in a xenograft mouse model of GC. RESULTS: Morin significantly inhibited the proliferation of GC cells MKN45 and AGS in a dose- and time-dependent manner, but did not inhibit human gastric epithelial cells GES-1. Only the caspase inhibitor Z-VAD-FMK was able to significantly reverse the inhibition of proliferation by morin in both GC cells, suggesting that apoptosis was the main type of cell death during the treatment. Morin induced intrinsic apoptosis in a dose-dependent manner in GC cells, which mainly relied on B cell leukemia/lymphoma 2 (BCL-2) associated agonist of cell death (BAD) but not phorbol-12-myristate-13-acetate-induced protein 1. The upregulation of BAD by morin was due to blocking the ubiquitination degradation of BAD, rather than the transcription regulation and the phosphorylation of BAD. Furthermore, the combination of morin and BCL-2 inhibitor navitoclax (also known as ABT-737) produced a synergistic inhibitory effect in GC cells through amplifying apoptotic signals. In addition, morin treatment significantly suppressed the growth of GC in vivo by upregulating BAD and the subsequent activation of its downstream apoptosis pathway. CONCLUSION Morin suppressed GC by inducing apoptosis, which was mainly due to blocking the ubiquitination-based degradation of the pro-apoptotic protein BAD. The combination of morin and the BCL-2 inhibitor ABT-737 synergistically amplified apoptotic signals in GC cells, which may overcome the drug resistance of the BCL-2 inhibitor. These findings indicated that morin was a potent and promising agent for GC treatment. Please cite this article as: Wang Y, Sun XY, Ma FQ, Ren MM, Zhao RH, Qin MM, Zhu XH, Xu Y, Cao ND, Chen YY, Dong TG, Pan YF, Zhao AG. Morin inhibits ubiquitination degradation of BCL-2 associated agonist of cell death and synergizes with BCL-2 inhibitor in gastric cancer cells. J Integr Med. 2025; 23(3): 320-332.
Humans ; Flavonoids/therapeutic use* ; Stomach Neoplasms/pathology* ; Animals ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Cell Line, Tumor ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Ubiquitination/drug effects* ; Mice ; Drug Synergism ; Mice, Inbred BALB C ; Mice, Nude ; Xenograft Model Antitumor Assays ; Flavones

This study was analyzed the spatial-temporal distribution and aggregation characteristics of Yersinia pestispositive host animals and vector pathogens in marmot natural foci in the Qilian-Altun mountains,Gansu Province,to provide a scientific basis for precise plague prevention and control.Y.pestissurveillance data for marmot natural foci in Qilian-Altun Mountains of Gansu Province from 2014 to 2023 were obtained from the Disease Control and Prevention Center of Gansu Province.Origin 2024 software was used for data visualization and presentation.Global and local spatial autocorrelation analyses and trend analyses were conducted in ArcGIS 10.8 software,with townships as the spatial scale.Cumulatively,440 strains of Y.pestis were isolated from the natural marmot foci in the Qilian-Altun mountainsof Gansu Province from 2014 to 2023.Most strains was isolated from marmots(345 strains,78.41%),and the remainder were isolated from vectors.Temporal distribution analysis indicated that the highest number of detected bacteria was reported in July and August(both 121 strains,27.50%).Regional distribution analysis revealed that Aksai County reported the highest number of detected bacteria(255 strains,57.95%).Global spatial autocorrelation analysis showed a spatially clustered distribution of the number of bacteria detected annually in the townships containing natural foci,except in2014,2016,and 2021-2023.The strongest spatial clustering was observed in 2020(Moran's I=0.521 2,Z=14.397 0,P<0.001).Local spatial autocorrelation analysis indicated a"high-high"aggregation area in the natural foci every year from 2014 to 2023,primarily in Hongliuwan Town of Aksai County and Dangchengwan Town of Subei County.The distribution of the"low-low"aggregation area was essentially consistent with the low activity area of the Yersinia pestisepidemic.The trend in annual total bacterial count gradually increased from east to west,and peaked in the western part of the epidemic focus.Clear spatial aggregation characteristics of the number of Y.pestis were detected in the marmot natural foci in the Qilian-Altun mountains at the townshiplevel as a whole in Gansu Province from 2014 to 2023.The aggregation area was mainly in the western section of Qilian Mountain to the Altun mountain section of the epidemic source area.Monitoring and prevention and control efforts should be focused in this key area,with prevention and control measures tailored to the local conditions,and classified guidance to decrease the risk of plague occurrence and spread.

Objective:To investigate the differences in electrophysiological brain development of neonates in Tibet and Beijing.Methods:This prospective cohort study included neonates with gestational ages of 28 to 40 weeks and 6 days, without asphyxia, hypoxia, or brain injury, who were born between January 2022 and June 2024 at the Tibet Autonomous Region People's Hospital and Peking University First Hospital. The first electroencephalographic (EEG) monitoring was completed within 48 hours to 7 days after birth, which included a 4-channel amplitude-integrated EEG (aEEG) and a 12-channel continuous EEG (cEEG). Two electrophysiology experts scored the EEG results according to a rating scale, and the intraclass correlation coefficient (ICC) was used to explore the consistency between different evaluators. Preterm infants with gestational ages of 32 to 36 weeks and 6 days and post-menstrual age (PMA) less than full-term at the first EEG monitoring were re-examined with aEEG and cEEG at PMA of 37 to 40 weeks and 6 days. Infants were grouped based on PMA at the first EEG monitoring. Spearman rank correlation was used to analyze the correlations between total aEEG+cEEG scores, individual aEEG and cEEG scores, and PMA, gestational age, birth weight, and head circumference at the first EEG monitoring. Mann-Whitney U test, Kruskal-Wallis H test, and Bonferroni correction were used to compare the differences in total aEEG+cEEG scores, individual aEEG and cEEG scores between Tibet and Beijing, among adjacent PMA groups, and for premature infants at full-term PMA. Results:(1) A total of 341 neonates were included in this study, including 154 cases from Tibet (nine cases in the PMA of 28-29 weeks and 6 days group, 13 cases in the PMA of 30-31 weeks and 6 days group, 28 cases in the PMA of 32-33 weeks and 6 days group, 38 cases in the PMA of 34-36 weeks and 6 days group, and 66 cases in the PMA of 37-40 weeks and 6 days group) and 187 cases from Beijing (10 cases in the PMA of 28-29 weeks and 6 days group, 10 cases in the PMA of 30-31 weeks and 6 days group, 16 cases in the PMA of 32-33 weeks and 6 days group, 91 cases in the PMA of 34-36 weeks and 6 days group, and 60 cases in the PMA of 37-40 weeks and 6 days group). (2) Inter-rater consistency:the consistency of PMA inferred based on the total aEEC+CEEC score and actual PMA was high in two raters ( ICCrater one=0.96, ICCrater two=0.94, both P<0.01). (3) The correlation between total aEEG+cEEG score and PMA ( r=0.80) was stronger than that between the aEEG alone or cEEG scores and PMA ( r were 0.79 and 0.66, respectively). The total aEEG+cEEG score also correlated with gestational age at birth ( r=0.74), birth weight ( r=0.69), and head circumference at first EEG monitoring ( r=0.69) (all P<0.01). (4) Regardless of whether in Tibet or Beijing, the total aEEG+cEEG score increased sequentially in the PMA of 30- 31 weeks and 6 days, 32-33 weeks and 6 days, 34-36 weeks and 6 days, and 37-40 weeks and 6 days groups; the cEEG score increased sequentially in the PMA of 32-33 weeks and 6 days group, 34-36 weeks and 6 days group, and 37-40 weeks and 6 days groups; the aEEG score in the PMA 32- 33 weeks and 6 days group was higher than that in the 30-31 weeks and 6 days group, and the score in the PMA 37-40 weeks and 6 days group was higher than that in the 34-36 weeks and 6 days group (Bonferroni correction, all P<0.05). (5) At PMA of 34-36 weeks and 6 days, the total aEEG+cEEG score [25 points (22-26 points) vs. 26 points (24-28 points), Z=－2.62, P=0.009] and cEEG score [12 points (12-14 points) vs. 15 points (13-16 points), Z=－4.77, P<0.001] of newborns in Tibet were lower than those in Beijing, while the aEEG score was higher than those in Beijing [12 points (10-13 points) vs. 11 points (10-12 points), Z=2.17, P=0.030]; at PMA of 37-40 weeks and 6 days, the cEEG score of newborns in Tibet was lower than those in Beijing [16 points (15-17 points) vs. 17 points (15-18 points), Z=－2.27, P=0.023]. (6) The total aEEG+cEEG score of preterm infants born at 32 to 33 weeks and 6 days in Tibet was lower at PMA full-term compared to those in Beijing [27 points (26-28 points) vs. 29 points (28 -30 points), Z=－2.94], and also lower compared to the total aEEG+cEEG score of full-term gestational age newborns in Tibet during their first EEG monitoring [29 points (27-30 points)] (both P<0.05). Conclusions:In the high-altitude hypobaric hypoxic environment, the electroencephalographic development of newborns, especially premature infants, maybe lag behind of plain areas. The combined use of aEEG+cEEG may provide a better evaluation of neonatal brain development than using cEEG or aEEG alone.

Objective To design a training device of the flight crew for plateau normobaric low-oxygen acclimatization so as to enhance the flight crew's ability to adapt to the low oxygen environment after rushing into the plateau and reduce the incidence of acute plateau reaction.Methods The training device comprised a plateau environment simulation controller,a multimodal physiological acquisition system and hypoxia exercise training evaluation software.The plateau environment simulation controller was composed of an environment monitor for plateau acclimatization,two composite sensor sets,a control valve and an alarm device;the multimodal physiological acquisition system was made up of 20 groups of vital signs acquisi-tion devices,with a wearable dynamic ECG and respiration recorder,a wrist oximeter and an arm sphygmomano-meter included in each group.The hypoxia exercise training evaluation software was developed with a B/S architecture,Java language and JetBrains 2020.3.Results The training device proved to have the simulation altitude ranging from 0 to 6 000 m and facilitated simultaneous training of 20 persons for normobaric low-oxygen acclimatization,screening for hypoxia endurance,real-time monitoring of physiological parameters and assessment of training effect,with none of the trainees having acute plateau reaction.Conclusion The training device assists the flight crew for plateau normobaric low-oxygen acclimatization,and can be used for acclimatization training before plateau missions.[Chinese Medical Equipment Journal,2025,46(8):18-24]

Neuronal reprogramming is an innovative technique for converting non-neuronal somatic cells into neurons that can be used to replace lost or damaged neurons, providing a potential effective therapeutic strategy for central nervous system (CNS) injuries or diseases. Transcription factors have been used to induce neuronal reprogramming, while their reprogramming efficiency is relatively low, and the introduction of exogenous genes may result in host gene instability or induce gene mutation. Therefore, their future clinical application may be hindered by these safety concerns. Compared with transcription factors, small-molecule compounds have unique advantages in the field of neuronal reprogramming, which can overcome many limitations of traditional transcription factor-induced neuronal reprogramming. Here, we review the recent progress in the research of small-molecule compound-mediated neuronal reprogramming and its application in CNS regeneration and repair.
Humans ; Cellular Reprogramming/drug effects* ; Neurons/cytology* ; Animals ; Transcription Factors ; Small Molecule Libraries/pharmacology* ; Nerve Regeneration

This study explored the drying kinetics of Salviae Miltiorrhizae Radix et Rhizoma(SM), established the suitable models simulating the drying kinetics, and then analyzed the dynamic changes of active components during the drying processes with different methods, aiming to provide a basis for the establishment of suitable drying methods and the quality control of SM. The drying kinetics were studied based on the drying curve, drying rate, moisture effective diffusion coefficient, and drying activation energy, and the appropriate drying kinetics model of SM was established. The drying performance of different methods, such as hot air drying, infrared drying, and microwave drying of SM was evaluated, and the changes in the content of 10 salvianolic acids and 6 tanshinones during drying were analyzed by UPLC-TQ-MS. The Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) was employed to evaluate the quality of SM dried with different methods. The results showed that the drying rate and moisture effective diffusion coefficient of SM increased with the rise in drying temperature, and the maximum drying rates of different methods were in the order of microwave drying > infrared drying > hot air drying, slice > whole root. The drying rate decreased with the rise in temperature and the extension of drying time. The activation energy of hot air drying was higher than that of infrared drying in SM. The most suitable model for simulating the drying process of SM was the Page model. The TOPSIS results suggested infrared drying at 50 ℃ was the optimal drying method for SM. During the drying process, the content of salvianolic acids increased in different degrees with the loss of moisture, among which salvianolic acid B showed the largest increase of 44 times compared with that in the fresh medicinal material. Tanshinones also existed in the fresh herb of SM, and the content of tanshinone Ⅱ_A increased by 3 times after drying. The results provided a basis for the establishment of suitable drying methods and the quality control of SM.
Salvia miltiorrhiza/chemistry* ; Desiccation/methods* ; Drugs, Chinese Herbal/chemistry* ; Rhizome/chemistry* ; Kinetics ; Quality Control ; Abietanes

The efficacy mechanism of the alcoholic extract of Gnaphalium affine was investigated by observing its influence on oxidative stress and intestinal flora in rats modeled for chronic obstructive pulmonary disease(COPD). UPLC-MS was used to evaluate the quality of the alcoholic extract of G. affine, and 72 rats were randomly divided into six groups, with COPD models established in five groups by cigarette smoke combined with airway drip lipopolysaccharide, and the rats were given the positive drug of Danlong Oral Solution, as well as low-, medium-, and high-doses alcoholic extract of G. affine, respectively. After two weeks of continuous gastric gavage, the body weights and general morphology observations were performed; HE staining and Masson staining were used to verify the effects of the alcoholic extract of G. affine on alveolar inflammation and collagen deposition area in COPD rats; the oxidative stress indexes CAT and GSH in serum and SOD and MDA in lung tissue of the rats were measured, and the mRNA expression of HO-1, Nrf2, and NQO1 were determined by qRT-PCR. The protein expressions of HO-1, Nrf2, and NQO1 were determined by the Western blot method, and the mechanism by which the alcoholic extract of G. affine affected oxidative stress in COPD rats was explored. Finally, the influence of G. affine on the changes in intestinal flora caused by COPD was studied by 16S rRNA sequencing. The results showed that a total of 121 chemical components were identified by UPLC-MS, including 70 positive and 51 negative ion modes. In animal experiments, it was found that the alcoholic extracts of G. affine were able to reduce the percentage of collagen deposition, affect the oxidative stress indexes such as CAT, GSH, SOD, MDA, as well as the mRNA and protein expression of Nrf2, HO-1, and NQO1. The 16S rRNA sequencing results showed an increase in the level of Lactobacillales and a decrease in the level of Desulfovibrio and Desulfovibrionales, suggesting that the alcoholic extracts of G. affine could reverse the changes in intestinal flora caused by COPD. In conclusion, the alcoholic extracts of G. affine may exert anti-COPD effects by affecting the oxidative stress pathway and modulating the changes in intestinal flora.
Animals ; Oxidative Stress/drug effects* ; Pulmonary Disease, Chronic Obstructive/genetics* ; Rats ; Male ; Gastrointestinal Microbiome/drug effects* ; Rats, Sprague-Dawley ; Drugs, Chinese Herbal/administration & dosage* ; NF-E2-Related Factor 2/metabolism* ; Humans ; Lung/metabolism*

Surgical resection remains the primary option for achieving radical cure and long-term survival in the treatment of liver cancer.In recent years,profound changes have taken place in the field of liver surgery:the surgical concept of liver resection for liver cancer has been constantly updated;Significant progress has also been made in many aspects such as liver imaging technology,liver resection techniques and equipment,and perioperative management.Liver resection for liver cancer has gradually developed into a more precise,minimally invasive and safer treatment model.However,liver resection for liver cancer still faces many new challenges up to now:the prevention strategies for recurrence and metastasis are limited;There is a lack of predictive indicators for the efficacy of targeted immunotherapy.Insufficient precision in individualized treatment,etc.