Acute lung injury: a view from the perspective of necroptosis.

BACKGROUND: ALI/ARDS is a syndrome of acute onset characterized by progressive hypoxemia and noncardiogenic pulmonary edema as the primary clinical manifestations. Necroptosis is a form of programmed cell necrosis that is precisely regulated by molecular signals. This process is characterized by organelle swelling and membrane rupture, is highly immunogenic, involves extensive crosstalk with various cellular stress mechanisms, and is significantly implicated in the onset and progression of ALI/ARDS. METHODS: The current body of literature on necroptosis and ALI/ARDS was thoroughly reviewed. Initially, an overview of the molecular mechanism of necroptosis was provided, followed by an examination of its interactions with apoptosis, pyroptosis, autophagy, ferroptosis, PANOptosis, and NETosis. Subsequently, the involvement of necroptosis in various stages of ALI/ARDS progression was delineated. Lastly, drugs targeting necroptosis, biomarkers, and current obstacles were presented. CONCLUSION: Necroptosis plays an important role in the progression of ALI/ARDS. However, since ALI/ARDS is a clinical syndrome caused by a variety of mechanisms, we emphasize that while focusing on necroptosis, it may be more beneficial to treat ALI/ARDS by collaborating with other mechanisms.

Assessing the impact of different contact patterns on disease transmission: Taking COVID-19 as a case.

Human-to-human contact plays a leading role in the transmission of infectious diseases, and the contact pattern between individuals has an important influence on the intensity and trend of disease transmission. In this paper, we define regular contacts and random contacts. Then, taking the COVID-19 outbreak in Yangzhou City, China as an example, we consider age heterogeneity, household structure and two contact patterns to establish discrete dynamic models with switching between daytime and nighttime to depict the transmission mechanism of COVID-19 in population. We studied the changes in the reproduction number with different age groups and household sizes at different stages. The effects of the proportion of two contacts patterns on reproduction number were also studied. Furthermore, taking the final size, the peak value of infected individuals in community and the peak value of quarantine infected individuals and nucleic acid test positive individuals as indicators, we evaluate the impact of the number of random contacts, the duration of the free transmission stage and summer vacation on the spread of the disease. The results show that a series of prevention and control measures taken by the Chinese government in response to the epidemic situation are reasonable and effective, and the young and middle-aged adults (aged 18-59) with household size of 6 have the strongest transmission ability. In addition, the results also indicate that increasing the proportion of random contact is beneficial to the control of the infectious disease in the phase with interventions. This work enriches the content of infectious disease modeling and provides theoretical guidance for the prevention and control of follow-up major infectious diseases.

ODF3B affects the proliferation and apoptosis of glioma via the JAK/STAT pathway.

The pathogenesis of glioma has remained unclear. In this study, it was found that high expression of the outer dense fibers of sperm tail 3B (ODF3B) in gliomas was positively correlated with the grade of glioma. The higher the grade, the worse the prognosis. ODF3B is closely related to the growth and apoptosis of glioma. In terms of mechanism, ODF3B was found to affect the proliferation and apoptosis of glioma through the JAK1 and JAK2/STAT3 pathways. ODF3B was also found to affect the growth and apoptosis of glioma in vivo. We conclude that ODF3B affects glioma proliferation and apoptosis via the JAK/STAT pathway and is a potential therapeutic target.

[Longitudinal study on catch-up growth in preterm infants with small for gestational age at corrected ages 0-24 months]. / æ—©äº§å°äºŽèƒŽé¾„å„¿æ ¡æ­£0~24æœˆé¾„è¿½èµ¶ç”Ÿé•¿çš„çºµå‘ç ”ç©¶.

OBJECTIVES: To understand the growth and development status and differences between small for gestational age (SGA) and appropriate for gestational age (AGA) preterm infants during corrected ages 0-24 months, and to provide a basis for early health interventions for preterm infants. METHODS: A retrospective study was conducted, selecting 824 preterm infants who received regular health care at the Guangzhou Women and Children's Medical Center from July 2019 to July 2022, including 144 SGA and 680 AGA infants. The growth data of SGA and AGA groups at birth and corrected ages 0-24 months were analyzed and compared. RESULTS: The SGA group had significantly lower weight and length than the AGA group at corrected ages 0-18 months (P<0.05), while there were no significant differences between the two groups at corrected age 24 months (P>0.05). At corrected age 24 months, 85% (34/40) of SGA and 79% (74/94) of AGA preterm infants achieved catch-up growth. Stratified analysis by gestational age showed that there were significant differences in weight and length at corrected ages 0-9 months between the SGA subgroup with gestational age <34 weeks and the AGA subgroups with gestational age <34 weeks and 34 weeks (P<0.05). In addition, the weight and length of the SGA subgroup with gestational age 34 weeks showed significant differences compared to the AGA subgroups with gestational age <34 weeks and 34 weeks at corrected ages 0-18 months and corrected ages 0-12 months, respectively (P<0.05). Catch-up growth for SGA infants with gestational age <34 weeks and 34 weeks mainly occurred at corrected ages 0-12 months and corrected ages 0-18 months, respectively. CONCLUSIONS: SGA infants exhibit delayed early-life physical growth compared to AGA infants, but can achieve a higher proportion of catch-up growth by corrected age 24 months than AGA infants. Catch-up growth can be achieved earlier in SGA infants with a gestational age of <34 weeks compared to those with 34 weeks.

Identification and characterization of human hematopoietic mesoderm.

The embryonic mesoderm comprises heterogeneous cell subpopulations with distinct lineage biases. It is unclear whether a bias for the human hematopoietic lineage emerges at this early developmental stage. In this study, we integrated single-cell transcriptomic analyses of human mesoderm cells from embryonic stem cells and embryos, enabling us to identify and define the molecular features of human hematopoietic mesoderm (HM) cells biased towards hematopoietic lineages. We discovered that BMP4 plays an essential role in HM specification and can serve as a marker for HM cells. Mechanistically, BMP4 acts as a downstream target of HDAC1, which modulates the expression of BMP4 by deacetylating its enhancer. Inhibition of HDAC significantly enhances HM specification and promotes subsequent hematopoietic cell differentiation. In conclusion, our study identifies human HM cells and describes new mechanisms for human hematopoietic development.

An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

INTRODUCTION: Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging. OBJECTIVES: This study aims to develop an interpretable AI framework for SL prediction and subsequently utilize it to design SL-based synergistic combination therapies. METHODS: We propose a knowledge and data dual-driven AI framework for SL prediction (KDDSL). Specifically, we use gene knowledge related to the SL mechanism to guide the construction of the model and develop a method to identify the most relevant gene knowledge for the predicted results. RESULTS: Experimental and literature-based validation confirmed a good balance between predictive and interpretable ability when using KDDSL. Moreover, we demonstrated that KDDSL could help to discover promising drug combinations and clarify associated biological processes, such as the combination of MDM2 and CDK9 inhibitors, which exhibited significant anti-cancer effects in vitro and in vivo. CONCLUSION: These data underscore the potential of KDDSL to guide SL-based combination therapy design. There is a need for biomedicine-focused AI strategies to combine rational biological knowledge with developed models.

Cascading effects of hypobaric hypoxia on the testis: insights from a single-cell RNA sequencing analysis.

Most mammals tolerate exposure to hypobaric hypoxia poorly as it may affect multiple regulatory mechanisms and inhibit cell proliferation, promote apoptosis, limit tissue vascularization, and disrupt the acid-base equilibrium. Here, we quantified the functional state of germ cell development and demonstrated the interaction between the germ and somatic cells via single-cell RNA sequencing (scRNA-seq). The present study elucidated the regulatory effects of hypobaric hypoxia exposure on germ cell formation and sperm differentiation by applying enrichment analysis to genomic regions. Hypobaric hypoxia downregulates the genes controlling granule secretion and organic matter biosynthesis, upregulates tektin 1 (TEKT1) and kinesin family member 2C (KIF2C), and downregulates 60S ribosomal protein 11 (RPL11) and cilia- and flagella-associated protein 206 (CFAP206). Our research indicated that prosaposin-G protein-coupled receptor 37 (PSAP-GPR37) ligands mediate the damage to supporting cells caused by hypobaric hypoxic exposure. The present work revealed that hypoxia injures peritubular myoid (PTM) cells and spermatocytes in the S phase. It also showed that elongating spermatids promote maturation toward the G2 phase and increase their functional reserve for sperm-egg binding. The results of this study provide a theoretical basis for future investigations on prophylactic and therapeutic approaches toward protecting the reproductive system against the harmful effects of hypobaric hypoxic exposure.

Research progress on the activation mechanism of NLRP3 inflammasome in septic cardiomyopathy.

Sepsis is an uncontrolled host response to infection, resulting in a clinical syndrome involving multiple organ dysfunctions. Cardiac damage is the most common organ damage in sepsis. Uncontrolled inflammatory response is an important mechanism in the pathogenesis of septic cardiomyopathy (SCM). NLRP3 inflammasome promotes inflammatory response by controlling the activation of caspase-1 and the release of pro-inflammatory cytokines interleukin IL-1ß and IL-18. The role of NLRP3 inflammasome has received increasing attention, but its activation mechanism and regulation of inflammation in SCM remain to be investigated.

A study related to the treatment of gastric cancer with Xiang-Sha-Liu-Jun-Zi-Tang based on network analysis.

Purpose: Xiang-Sha-Liu-Jun-Zi-Tang(XSLJZT) is a common formula for the treatment of Gastric Cancer(GC) and is widely used in clinical practice, however, there is a lack of investigation into its mechanism. Methods: We collected and organized drug and disease targets, constructed the "XSLJZT-Active Ingredient-Target" visualization network, and performed GO and KEGG functional enrichment analysis of crossover genes, followed by molecular docking of active ingredients and core targets. The best docked monomers were combined with weighted gene co-expression network analysis(WGCNA) and macroscopically analyzed by GO and KEGG enrichment techniques. The results of cluster gene difference analysis, ROC evaluation, and CIBERSORT immune infiltration analysis were evaluated and finally supported by cellular experiments. Results: The main components of XSLJZT are quercetin, stigmasterol, and naringenin, effectively treat GC by targeting STAT3, TP53 and MAPK3, which are involved in IL-17, TNF and HIF-1 signaling pathways. The results of molecular docking showed that quercetin bound better to the core targets. We performed an in-depth analysis of this monomer and found that quercetin acts on the core targets of TP53, MMP9, TIMP1 and MYC, and is involved in two key signaling pathways, TNF and IL-17, thus effectively treating GC. The experimental results are consistent with our analysis that quercetin inhibits the proliferation of GC cells and promotes apoptosis, and TP53, MYC and TIMP1 are the quercetin targets for the treatment of GC. Conclusion: The present study tentatively suggests that quercetin, the main active ingredient in XSLJZT, can exert a therapeutic effect on GC by targeting TIMP1.

MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning.

Cancer is an extremely complex disease and each type of cancer usually has several different subtypes. Multi-omics data can provide more comprehensive biological information for identifying and discovering cancer subtypes. However, existing unsupervised cancer subtyping methods cannot effectively learn comprehensive shared and specific information of multi-omics data. Therefore, a novel method is proposed based on shared and specific representation learning. For each omics data, two autoencoders are applied to extract shared and specific information, respectively. To reduce redundancy and mutual interference, orthogonality constraint is introduced to separate shared and specific information. In addition, contrastive learning is applied to align the shared information and strengthen their consistency. Finally, the obtained shared and specific information for all samples are used for clustering tasks to achieve cancer subtyping. Experimental results demonstrate that the proposed method can effectively capture shared and specific information of multi-omics data and outperform other state-of-the-art methods on cancer subtyping.

A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction.

Combination therapy is a promising clinical treatment strategy for cancer and other complex diseases. Multiple drugs can target multiple proteins and pathways, greatly improving the therapeutic effect and slowing down drug resistance. To narrow the search space of synergistic drug combinations, many prediction models have been developed. However, drug combination datasets always have the characteristics of class imbalance. Synergistic drug combinations receive the most attention in clinical application but are in small numbers. To predict synergistic drug combinations in different cancer cell lines, in this study, we propose a genetic algorithm-based ensemble learning framework, GA-DRUG, to address the problems of class imbalance and high dimensionality of input data. The cell-line-specific gene expression profiles under drug perturbations are used to train GA-DRUG, which contains imbalanced data processing and the search of global optimal solutions. Compared to 11 state-of-the-art algorithms, GA-DRUG achieves the best performance and significantly improves the prediction performance in the minority class (Synergy). The ensemble framework can effectively correct the classification results of a single classifier. In addition, the cellular proliferation experiment performed on several previously unexplored drug combinations further confirms the predictive ability of GA-DRUG.

The relationship between polycystic ovary syndrome and coronary heart disease: a bibliometric analysis.

Background: Polycystic ovary syndrome (PCOS) is one of the most common gynecological endocrine diseases for women of puberty and reproductive age. PCOS can affect women's health for the rest of their lives since the incidence of coronary heart disease (CHD) may increase in the perimenopausal and senile periods among PCOS women compared with non-PCOS women. Method: A literature retrieval based on the Science Citation Index Expanded (SCI-E) database. All obtained records results were downloaded in plain text format for subsequent analysis. VOSviewer v1.6.10, Citespace and Microsoft Excel 2010 software were utilized for analyzing the following terms: countries, institutions, authors, journals, references and keywords. Results: There were 312 articles retrieved from January 1, 2000 to February 8, 2023, and the frequency of citations was 23,587. The United States, England, and Italy contributed the majority of the records. Harvard University, the University of Athens, and Monash University were the top 3 most productive institutions with publications on the relationship between PCOS and CHD. Journal of clinical endocrinology & metabolism ranked first with the highest publications (24 records), followed by Fertility and sterility (18 records). The keywords were divided into six clusters in the overlay keywords network: (1) the correlation between CHD risk factors and PCOS women; (2) the relationship between cardiovascular disease and female reproductive system hormone secretion; (3) the interaction between CHD and metabolic syndrome; (4) the relationship between c-reactive protein and endothelial function and oxidative stress in PCOS patients; (5) the potential positive effect of metformin on reducing CHD risk factors in PCOS patients; (6) the study of serum cholesterol and body-fat distribution in patients with CHD in PCOS. Oxidative stress, genome-wide association, obesity, primary prevention, and sex difference were main hotspots in this field in recent five years according to the keyword citation burst analysis. Conclusion: The article obtained the hotspots and trends and provided a reference for subsequent research on the association between PCOS and CHD. Moreover, it is hypothesized that oxidative stress and genome-wide association were frontier hotspots in studies that explore the relationship between PCOS and CHD, and prevention research may be valued in the future.

A hybrid deep forest-based method for predicting synergistic drug combinations.

Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.

Exploring novel disease-disease associations based on multi-view fusion network.

Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy. Availability of data and materials: The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.

Infectious thrombosis of the superior sagittal sinus with subarachnoid hemorrhage: A case report.

RATIONALE: Cerebral venous sinus thrombosis (CVST) represents 0.5% to 1% of all strokes. CVST can cause headaches, epilepsy, and subarachnoid hemorrhage (SAH). CVST is easily misdiagnosed because of the variety and non-specificity of symptoms. Herein, we report a case of infectious thrombosis of the superior sagittal sinus with SAH. PATIENT CONCERNS: A 34-year-old man presented to our hospital with a 4-hour history of sudden and persistent headache and dizziness with tonic convulsions of the limbs. Computed tomography revealed SAH with edema. Enhanced magnetic resonance imaging showed an irregular filling defect in the superior sagittal sinus. DIAGNOSES: The final diagnosis was hemorrhagic superior sagittal sinus thrombosis and secondary epilepsy. INTERVENTIONS: He was treated with antibiotic, antiepileptic, fluids to rehydrate, and intravenous dehydration. OUTCOMES: After treatment, the seizures did not recur and the symptoms were relieved. One month after the antibiotic treatment, the muscle strength of the patient's right extremity was restored to level 5, and there was no recurrence of his neurological symptoms. LESSONS: We describe a case of infectious thrombosis of the superior sagittal sinus manifested as SAH, which is easily misdiagnosed, especially when patients present with an infection. Clinicians must therefore take care during the diagnosis and selection of the treatment strategy.

DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.

Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interactions (DDIs), which may increase the risks for combination therapy, cannot be detected by existing computational synergy prediction methods. We propose DEML, an ensemble-based multi-task neural network, for the simultaneous optimization of five synergy regression prediction tasks, synergy classification, and DDI classification tasks. DEML uses chemical and transcriptomics information as inputs. DEML adapts the novel hybrid ensemble layer structure to construct higher order representation using different perspectives. The task-specific fusion layer of DEML joins representations for each task using a gating mechanism. For the Loewe synergy prediction task, DEML overperforms the state-of-the-art synergy prediction method with an improvement of 7.8% and 13.2% for the root mean squared error and the R2 correlation coefficient. Owing to soft parameter sharing and ensemble learning, DEML alleviates the multi-task learning 'seesaw effect' problem and shows no performance loss on other tasks. DEML has a superior ability to predict drug pairs with high confidence and less adverse DDIs. DEML provides a promising way to guideline novel combination therapy strategies for cancer treatment.

GADRP: graph convolutional networks and autoencoders for cancer drug response prediction.

Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations， and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.

LncRNA-mRNA co-expression analysis revealed 8 core lncRNAs in rheumatoid arthritis of collagen-induced arthritis rats.

BACKGROUNDS: Rheumatoid arthritis (RA) is a chronic inflammatory and autoimmune disease. Current studies suggest that long noncoding RNAs (lncRNAs) may be key regulators in pathogenesis. METHODS: Analyzed lncRNAs and mRNAs using microarrays to find key differentially expressed lncRNAs in RA. GO and KEGG enrichment analysis together with coding non-coding co-expression (CNC) network was used for comprehensive analysis. Verify that their expression levels are consistent with the chip results by qRT-PCR. RESULTS: There are 268 differentially expressed lncRNAs (DELs) and 286 differentially expressed mRNAs (DEMs). We found 8 core lncRNAs through the CNC network. Eight highly significantly differentially expressed lncRNAs corrected with microarray profiles. The functions and associated pathways of significantly differentially expressed lncRNAs were predicted by GO and KEGG analysis. They may be involved in the pathogenesis of RA. CONCLUSION: The differential expression profiles of lncRNAs and mRNAs in the collagen-induced arthritis rat model preliminarily predicted functions through comprehensive analysis. However, its exact role and specific mechanism remain to be further studied.

Expression and function analysis of microRNA in collagen-induced arthritis rats. / MicroRNAåœ¨èƒ¶åŽŸè¯±å¯¼åž‹å ³èŠ‚ç‚Žå¤§é¼ ä¸­çš„å¼‚å¸¸è¡¨è¾¾åŠåŠŸèƒ½åˆ†æž.

OBJECTIVES: Rheumatoid arthritis is a common autoimmune disease, and microRNAs (miRNAs) are involved in its pathogenesis. This study aims to examine the differentially expressed miRNAs in collagen-induced arthritis (CIA) rats, to analyze the biological functions and the related pathways of the miRNA target genes. METHODS: The total RNA in the synovium of experimental animals was extracted. The miRNA gene profile was obtained by miRNA microarray. Then the differentially expressed miRNAs were screened and the relevant target mRNAs were predicted. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the significantly differentially expressed miRNAs. RESULTS: There were 69 differentially expressed miRNAs including rno-miR-6215 and rno-miR-709 in CIA rats, of which 22 (31.9%) were up-regulated and 47 (68.1%) were down-regulated. GO and KEGG enrichment analysis showed that the up-regulated miRNA target genes were mainly enriched in cellular metabolism, and they were involved in MAPK and Wnt signaling pathways. The down-regulated miRNA target genes were mainly enriched in nervous system development, and they were involved in axon guidance signaling pathway. CONCLUSIONS: There are differentially expressed miRNAs in the CIA rat model, which may be involved in metabolism biological functions and signal pathways such as MAPK and Wnt.

NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.

BACKGROUND: Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. METHODS: In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. RESULTS: Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. CONCLUSIONS: NeRD's feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.