ObjectiveTo investigate the functional role and underlying molecular mechanisms of fumarylacetoacetate hydrolase （FAH） in the progression of glioblastoma （GBM）. MethodsDifferential expression analysis was performed on the TCGA-GBM， GSE4290， and GSE116520 datasets. Weighted gene co-expression network analysis （WGCNA） was used to identify key modules， and Cox regression and risk modeling were used to screen prognostic genes. Immune infiltration analysis of prognostic genes was carried out by using single-cell RNA sequencing panels. The clinical expression signature of FAH in GBM was analyzed in the TCGA and HPA databases. The functional role of FAH was validated by in vitro and in vivo experiments， and pathway analysis was performed to explore the underlying mechanisms. ResultsA total of 152 overlapping genes were identified across the three GBM datasets （P<0.05）. WGCNA revealed that the turquoise module was most strongly associated with tumor purity， stromal score， immune score， and ESTIMATE score （P<0.001）. Compared with normal tissues， three prognostic genes （CTSD， FAH， and THBD） were upregulated in GBM and correlated with immune infiltration （P<0.05）. FAH mRNA and protein levels were elevated in GBM tissues relative to normal tissues， and its expression was significantly associated with age stratification and TP53 mutation （P<0.05）. CCK-8 assay results showed that， compared with the shNC group， the proliferative activity of GBM cells in the shFAH group was reduced （P<0.001）. Transwell migration and invasion assays demonstrated that， relative to the shNC group， the numbers of migrated and invaded cells in the shFAH group decreased （P<0.05）. Western blot analysis revealed that the protein expression levels of PI3K， p-AKT， and p-mTOR in the shFAH group decreased compared with those in the shNC group （P<0.05）. In vivo subcutaneous xenograft experiments further confirmed that tumor volume and weight significantly decreased in the shFAH group compared with the shNC group （P<0.001）. ConclusionFAH promotes GBM progression by activating the PI3K/AKT/mTOR signaling pathway and may serve as a potential therapeutic target for GBM.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Objective:To investigate the expressions and prognostic value of lysyl oxidase like protein 2(LOXL2),chemo-kine c-c-motif ligand 18(CCL-18)and chitinase protein 40(YKL-40)in connective tissue disease with pulmonary interstitial lesions.Methods:A total of 308 patients with connective tissue disease who were treated in the First Affiliated Hospital of Hebei North Univer-sity from July 2021 to February 2022 were selected,including 108 patients with pulmonary interstitial disease(pathological group),200 patients without pulmonary interstitial disease(non pathological group),and another 35 healthy volunteers as the control group.Serum levels of LOXL2,CCL-18 and YKL-40 were detected by ELISA;Logistic regression analysis was applied to analyze the factors affecting the prognosis of patients with connective tissue disease and pulmonary interstitial disease;the predictive value of serum LOXL2,CCL-18 and YKL-40 levels on the prognosis of patients with connective tissue disease and pulmonary interstitial disease was analyzed by receiver operating characteristic(ROC)curve analysis.Results:Compared with control group,the serum levels of LOXL2,CCL-18 and YKL-40 in patients with connective tissue disease in the non pathological group and the pathological group were obviously increased(P<0.05);the serum levels of LOXL2,CCL-18 and YKL-40 in patients with connective tissue disease in the le-sion group were higher than those in the non lesion group(P<0.05);compared with the survival group,the serum levels of LOXL2,CCL-18 and YKL-40 in patients with connective tissue disease and pulmonary interstitial disease in the death group were obviously higher(P<0.05);Logistic regression analysis showed that LOXL2,CCL-18 and YKL-40 were risk factors for the prognosis of patients with connective tissue disease and pulmonary interstitial disease(P<0.05);the area under the ROC curve(AUC)of the combined detection of serum LOXL2,CCL-18 and YKL-40 in predicting the prognosis of patients with connective tissue disease and pulmonary interstitial disease was 0.967,which was better than their respective independent prediction(Zcombined detection-LOXL2=1.735,P=0.041;Zcombined detection-CCL-18=2.481,P=0.007;Zcombined detection-YKL-40=2.008,P=0.022).Conclusion:The serum levels of LOXL2,CCL-18 and YKL-40 in patients with connective tissue disease and pulmonary interstitial disease are elevated,which has certain value in the prognosis evaluation of patients with connective tissue disease combined with pulmonary interstitial disease.

One of the basic questions in the aging field is whether there is a fundamental difference between the aging of lower invertebrates and mammals. A major difference between the lower invertebrates and mammals is the abundancy of noncoding RNAs, most of which are not conserved. We have previously identified a noncoding RNA Terc-53 that is derived from the RNA component of telomerase Terc. To study its physiological functions, we generated two transgenic mouse models overexpressing the RNA in wild-type and early-aging Terc-/- backgrounds. Terc-53 mice showed age-related cognition decline and shortened life span, even though no developmental defects or physiological abnormality at an early age was observed, indicating its involvement in normal aging of mammals. Subsequent mechanistic study identified hyaluronan-mediated motility receptor (Hmmr) as the main effector of Terc-53. Terc-53 mediates the degradation of Hmmr, leading to an increase of inflammation in the affected tissues, accelerating organismal aging. adeno-associated virus delivered supplementation of Hmmr in the hippocampus reversed the cognition decline in Terc-53 transgenic mice. Neither Terc-53 nor Hmmr has homologs in C. elegans. Neither do arthropods express hyaluronan. These findings demonstrate the complexity of aging in mammals and open new paths for exploring noncoding RNA and Hmmr as means of treating age-related physical debilities and improving healthspan.
Animals ; Mice ; RNA, Untranslated/metabolism* ; Aging/genetics* ; Mice, Transgenic ; Telomerase/metabolism* ; RNA/genetics* ; Hippocampus/metabolism* ; Humans ; Mice, Inbred C57BL