BACKGROUND:A large number of studies have demonstrated that stem cell exosomes play an important role in the repair of bone defects,either directly as carriers for loading other small molecules or surface modifications,or by binding to biomaterials to promote the repair and regeneration of bone tissue.OBJECTIVE:To summarize the osteogenic mechanisms of stem cell exosomes from different sources and their research progress in bone defect repair.METHODS:Chinese search terms"stem cell,exosome,bone,biomaterial,carrier,bioceramic,polymer,metal,hydrogel,engineered exosome"were used to search CNKI.English search terms"stem cell,exosome,bone defect,biomaterial,carrier,bioceramic,ploymer,metal material,hydrogel,engineering exosome"were used to search PubMed database.According to the inclusion and exclusion criteria,77 relevant articles were finally included for summary.RESULTS AND CONCLUSION:Exosomes from stem cells of different origins can promote osteoblast proliferation and differentiation,promote angiogenesis,and regulate osteoclast activity and macrophage phenotype to promote bone formation and bone mineralization.In addition,many achievements of exosomes in the field of bone defect repair were described from two aspects:biomaterial-assisted exosomes and engineered exosomes.However,the current research on stem cell exosomes in bone tissue engineering is still insufficient,and most of these studies are limited to small animal models,while the treatment of bone defects in large animals,including humans,will be more complex,which will also become a major challenge for the treatment of bone defects.This will also be a great challenge in the dissemination of exosome therapy.

BACKGROUND:A large number of studies have demonstrated that stem cell exosomes play an important role in the repair of bone defects,either directly as carriers for loading other small molecules or surface modifications,or by binding to biomaterials to promote the repair and regeneration of bone tissue.OBJECTIVE:To summarize the osteogenic mechanisms of stem cell exosomes from different sources and their research progress in bone defect repair.METHODS:Chinese search terms"stem cell,exosome,bone,biomaterial,carrier,bioceramic,polymer,metal,hydrogel,engineered exosome"were used to search CNKI.English search terms"stem cell,exosome,bone defect,biomaterial,carrier,bioceramic,ploymer,metal material,hydrogel,engineering exosome"were used to search PubMed database.According to the inclusion and exclusion criteria,77 relevant articles were finally included for summary.RESULTS AND CONCLUSION:Exosomes from stem cells of different origins can promote osteoblast proliferation and differentiation,promote angiogenesis,and regulate osteoclast activity and macrophage phenotype to promote bone formation and bone mineralization.In addition,many achievements of exosomes in the field of bone defect repair were described from two aspects:biomaterial-assisted exosomes and engineered exosomes.However,the current research on stem cell exosomes in bone tissue engineering is still insufficient,and most of these studies are limited to small animal models,while the treatment of bone defects in large animals,including humans,will be more complex,which will also become a major challenge for the treatment of bone defects.This will also be a great challenge in the dissemination of exosome therapy.

OBJECTIVE To investigate the neuroprotective effect and mechanism of eleutheroside B （ELB） on Parkinson’s disease （PD） model mice by regulating the IκB kinase β （IKKβ）/nuclear factor-κB （NF-κB） signaling pathway. METHODS Fifty mice were randomly divided into normal control group， model group， positive control group （selegiline hydrochloride， 10 mg/kg）， and ELB low-dose and high-dose groups （80， 160 mg/kg）， with 10 mice in each group. Each group was given relevant medicine or normal saline intragastrically for 14 consecutive days. Starting from the 10th day of administration， the model group and all administration groups were intraperitoneally injected with 1-methyl-4-phenyl-1，2，3，6-tetrahydropyridine （MPTP） 30 mg/kg， for five consecutive days to establish the chronic PD model. After the last administration for 24 h， six mice were randomly selected from each group to test their behavioral abilities； detect the levels of interleukin-1β （IL-1β）， IL-10， tumor necrosis factor-α （TNF-α） in brain tissue and their mRNA expressions were measured， and positive expression of tyrosine hydroxylase （TH）， protein expressions of TH， α -synuclein （ α -syn）， ionized calcium-binding adaptor molecule 1 （Iba-1）， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in the brain tissue were detected. The ultrastructure of neurons in substantia nigra was observed. RESULTS Compared with the model group， rotarod endurance time and climbing score of each administration group （except for the ELB low-dose group） were increased significantly （ P ＜0.05）， while the levels and mRNA expressions of IL-1β， TNF-α， α -syn， and Iba-1， as well as phosphorylation levels of IKKβ and NF-κB p65 proteins in brain tissue were decreased significantly （except for TNF-α in the ELB low-dose group）. Conversely， the level and mRNA expression of IL-10 （except for the ELB low-dose group）， TH positive expression and protein expressions were significantly increased （ P ＜0.05）. Typical neurodegenerative pathological changes， such as neuronal karyopyknosis， mitochondrial swelling and vacuolization， and endoplasmic reticulum dilation， all showed varying degrees of improvement. CONCLUSIONS ELB may exert neuroprotective effects by inhibiting the activation of the IKKβ/NF-κB signaling pathway， alleviating inflammatory responses， reducing abnormal α -syn aggregation and neuronal loss， and further improving motor dysfunction in PD mice.

Purpose: Dual anti–human epidermal growth factor receptor 2 (HER2) drugs have become the standard regimen for neoadjuvant systemic treatment (NST) to HER2-positive breast cancer patients. However, the efficacy varies greatly among patients with different HER2 protein expression levels. Materials and Methods: A total of 575 HER2-positive breast cancer patients from multiple centers throughout China from 2013 to 2022 were retrospectively analyzed. We compared clinicopathological features in different HER2 immunohistochemistry classes (HER2 2+/in situ hybridization [ISH] + or HER2 3+), and their difference in response to NST and survival with single or dual anti-HER2 drugs. Drug sensitivity assays were used to evaluate different efficacy of anti-HER2 drugs in vitro. Results: Compared to HER2 3+ subgroup, the HER2 2+/ISH+ group had a higher proportion of hormone receptor–positive status (48.7% vs. 76.1%, p < 0.001), more HER2 protein loss after NST, lower pathological complete response (pCR) rate (46.07% vs. 16.24%, p < 0.001), and tended to have worse disease-free survival (DFS). In HER2 2+/ISH+ patients, treated with pertuzumab and trastuzumab in combination had no significant improvement in pCR (19.12% vs. 12.24%, p=0.287) and DFS (p=0.908) than using alone. Drug sensitivity assay showed poor efficacy with dual anti-HER2 drugs in HER2 2+/ISH+ cell lines; however, fam-trastuzumab deruxtecan drugs had a satisfactory effect. Conclusion Owing to the differences in clinicopathological features and treatment efficacy, we considered the HER2 2+/ISH+ group to be a distinct subtype and defined it as the HER2-moderate–positive subgroup. In this subgroup, dual anti-HER2 drugs did not exert significant improvement in pCR and DFS. Therefore, treatment optimization is warranted, with antibody-drug conjugate drugs as potential options.

Objective To understand the epidemiological characteristics and trends of postoperative pneumonia(POP)in tertiary general hospitals in Jiangsu Province,and provide theoretical basis for carrying out targeted pre-vention and control measures.Methods Surgery patients from 22 tertiary general hospitals in 12 cities in north,central,and south of Jiangsu Province from January 1,2022 to December 31,2023 were chosen as studied subjects,occurrence of POP was analyzed and compared.Results A total of 848 274 surgical procedures were performed in 22 hospitals,and 3 606 cases of POP occurred,with an incidence of 0.43％.The incidence in 2023 was 0.37％,which was lower than that in 2022(0.49％),with statistically significant difference(P＜0.001).The top three de-partments with high incidence of POP were neurosurgery(6.71％),cardiothoracic surgery(2.91％),and general surgery(0.77％).Among hospitals of different grades,the incidence of POP in tertiary first-class hospitals was 0.44％,which was higher than that in other tertiary hospitals(0.37％).There was no statistically significant difference in the incidence of POP between municipal and district/county hospitals(P＞0.05).The incidence of POP in hospitals with a bed:infection control full-time staff ratio＜200∶1 was lower than that in hospitals with the ratio ≥200∶1(0.39％vs 0.47％,P＜0.001),while the incidence of POP in hospitals with a proportion ≥30％of full-time staff being doctors was higher than that in hospitals with a proportion＜30％(0.45％vs 0.36％,P＜0.001).The incidence of POP in male patients was higher than that in female patients(0.62％vs 0.26％,P＜0.001).The incidence of POP in elderly patients aged≥65 was higher than that in patients aged＜65(0.73％vs 0.26％,P＜0.001).A total of 2 667 strains of infectious pathogens were detected,with the top three being Acine-tobacter baumannii,Klebsiella pneumoniae,and Pseudomonas aeruginosa,accounting for 28.95％,22.72％,and 15.45％,respectively.The detection rates of carbapenem-resistant Acinetobacter baumannii(CRAB),carba-penem-resistant Klebsiella pneumoniae(CRKP),and carbapenem-resistant Pseudomonas aeruginosa(CRPA)were 60.75％,21.45％,and 32.28％,respectively.The detection rate of CRKP decreased in 2023 compared with 2022,with statistically significant difference(P＜0.05).Conclusion The overall incidence of POP in tertiary general hos-pitals in Jiangsu Province is relatively low,but there are significant differences among different hospitals.There-fore,perioperative prevention and control measures should be carried out based on the epidemiological characteristics of patients.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To investigate the effects of miR-483-3p on hypoxia/reoxygenation(H/R)-induced apoptosis and pyroptosis of H9c2 cardiomyocytes and its possible mechanism.Methods Rat H9c2 cardiomyocytes were cultured in vitro,adeno-associated virus-infected H9c2 and the H/R model were constructed by triple-air incuba-tor,and the cells were randomly divided into blank control(Sham)group,model(H/R)group,AAV-miR-483-3p mimic+H/R(AAV-miR-483-3p)group,AAV-miR-483-3p negative control+H/R(AAV-NC)group.The growth status of cells in each group was observed using an inverted microscope;cell proliferation activity was detected by cell counting kit-8(CCK-8);LDH release by lactate dehydrogenase(LDH)kit;apoptosis rate by flow cytometry;apoptosis by notched end labeling(TUNEL).Western blot(WB)was used to detect the expression levels of IL-1β and GSDMD proteins in each group.Results Compared with the Sham group,the H/R group showed abnormal cell status and increased cell death,decreased cell activity,increased LDH release,increased apoptosis rate and apopto-sis level,and increased expression levels of IL-1β and GSDMD proteins(P<0.05);compared with the H/R group,the AAV-miR-483-3p group showed improved cell status and less cell death,increased cell proliferation activity,increased LDH release,and increased IL-1β and GSDMD protein expression levels(P<0.05).Compared with the H/R group,the AAV-miR-483-3p group showed improved cell status and less cell death,increased cell proliferation activity,decreased LDH release,decreased apoptosis rate and apoptosis level,and decreased expression of IL-1β and GSDMD proteins(P<0.05).Conclusion Over-expression of miR-483-3p can improve H/R-inducedH9c2 cardiomyocyte injury by enhancing cell activity and cell metabolism,and inhibiting apoptosis and cell charring.

Objective To understand the epidemiological characteristics and trends of postoperative pneumonia(POP)in tertiary general hospitals in Jiangsu Province,and provide theoretical basis for carrying out targeted pre-vention and control measures.Methods Surgery patients from 22 tertiary general hospitals in 12 cities in north,central,and south of Jiangsu Province from January 1,2022 to December 31,2023 were chosen as studied subjects,occurrence of POP was analyzed and compared.Results A total of 848 274 surgical procedures were performed in 22 hospitals,and 3 606 cases of POP occurred,with an incidence of 0.43％.The incidence in 2023 was 0.37％,which was lower than that in 2022(0.49％),with statistically significant difference(P＜0.001).The top three de-partments with high incidence of POP were neurosurgery(6.71％),cardiothoracic surgery(2.91％),and general surgery(0.77％).Among hospitals of different grades,the incidence of POP in tertiary first-class hospitals was 0.44％,which was higher than that in other tertiary hospitals(0.37％).There was no statistically significant difference in the incidence of POP between municipal and district/county hospitals(P＞0.05).The incidence of POP in hospitals with a bed:infection control full-time staff ratio＜200∶1 was lower than that in hospitals with the ratio ≥200∶1(0.39％vs 0.47％,P＜0.001),while the incidence of POP in hospitals with a proportion ≥30％of full-time staff being doctors was higher than that in hospitals with a proportion＜30％(0.45％vs 0.36％,P＜0.001).The incidence of POP in male patients was higher than that in female patients(0.62％vs 0.26％,P＜0.001).The incidence of POP in elderly patients aged≥65 was higher than that in patients aged＜65(0.73％vs 0.26％,P＜0.001).A total of 2 667 strains of infectious pathogens were detected,with the top three being Acine-tobacter baumannii,Klebsiella pneumoniae,and Pseudomonas aeruginosa,accounting for 28.95％,22.72％,and 15.45％,respectively.The detection rates of carbapenem-resistant Acinetobacter baumannii(CRAB),carba-penem-resistant Klebsiella pneumoniae(CRKP),and carbapenem-resistant Pseudomonas aeruginosa(CRPA)were 60.75％,21.45％,and 32.28％,respectively.The detection rate of CRKP decreased in 2023 compared with 2022,with statistically significant difference(P＜0.05).Conclusion The overall incidence of POP in tertiary general hos-pitals in Jiangsu Province is relatively low,but there are significant differences among different hospitals.There-fore,perioperative prevention and control measures should be carried out based on the epidemiological characteristics of patients.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To investigate the molecular mechanism by which STAT1 regulates the expression of APOL6 in order to mediate lipid deposition in tumor-associated macrophages(TAM)in laryngeal cancer tissues.Methods Real-time polymerase chain reaction,Western blotting,immunohistochemistry,and enzyme-linked immunosorbent assays were used to detect the expression levels of STAT1 and APOL6 in laryngeal cancer tissues,as well as the regulatory effect of STAT1 on APOL6 expression.Chromatin immunoprecipitation was used to elucidate the molecular mechanisms underlying APOL6 regulation by ST AT1.Oil Red O staining was used to evaluate the lipid deposition in TAM.Results The expression levels of STAT1 and APOL6 in laryngeal cancer tissues were significantly higher than those in the adjacent normal tissues(P＜0.01).STAT1 transcriptionally activated APOL6 gene expression.STAT1 overexpression sig-nificantly promoted the expression and secretion of APOL6 in laryngeal cancer cells and induced lipid deposition in TAM.Conclusion STAT1 is a novel transcription factor for the APOL6 gene.STAT1 promotes lipid deposition in the TAM of laryngeal cancer tissues by regu-lating APOL6 expression,thereby reshaping the lipid metabolism of TAM.