Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration.

Objective: Amino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH). Methods: The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography-tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index. Results: Eleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively. Conclusion: The KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.

Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: A retrospective cohort study.

BACKGROUND: Posthepatectomy liver failure (PHLF) is the leading cause of mortality in patients undergoing hepatectomy. However, practical models for accurately predicting the risk of PHLF are lacking. This study aimed to develop precise prediction models for clinically significant PHLF. METHODS: A total of 226 patients undergoing hepatectomy at a single center were recruited. The study outcome was clinically significant PHLF. Five pre- and postoperative machine learning (ML) models were developed and compared with four clinical scores, namely, the MELD, FIB-4, ALBI, and APRI scores. The robustness of the developed ML models was internally validated using 5-fold cross-validation by calculating the average of the evaluation metrics and was externally validated on an independent temporal dataset, including the area under the curve (AUC) and the area under the precision‒recall curve (AUPRC). SHapley Additive exPlanations analysis was performed to interpret the best performance model. RESULTS: Clinically significant PHLF was observed in 23 of 226 patients (10.2%). The variables in the preoperative model included creatinine, total bilirubin, and Child‒Pugh grade. In addition to the above factors, the extent of resection was also a key variable for the postoperative model. The pre- and postoperative artificial neural network (ANN) models exhibited excellent performance, with mean AUCs of 0.766 and 0.851, respectively, and mean AUPRC values of 0.441 and 0.645, whereas the MELD, FIB-4, ALBI, and APRI scores reached AUCs of 0.714, 0.498, 0.536 and 0.551, respectively, and AUPRC values of 0.204, 0.111, 0.128 and 0.163, respectively. In addition, the AUCs of the pre- and postoperative ANN models were 0.720 and 0.731, respectively, and the AUPRC values were 0.380 and 0.408, respectively, on the temporal dataset. CONCLUSION: Our online interpretable dynamic ML models outperformed common clinical scores and could function as a clinical decision support tool to identify patients at high risk of PHLF pre- and postoperatively.

C9orf72 controls hepatic lipid metabolism by regulating SREBP1 transport.

Sterol regulatory element binding transcription factors (SREBPs) play a crucial role in lipid homeostasis. They are processed and transported to the nucleus via COPII, where they induce the expression of lipogenic genes. COPII maintains the homeostasis of organelles and plays an essential role in the protein secretion pathways in eukaryotes. The formation of COPII begins at endoplasmic reticulum exit sites (ERES), and is regulated by SEC16A, which provides a platform for the assembly of COPII. However, there have been few studies on the changes in SEC16A protein levels. The repetitive expansion of the hexanucleotide sequence GGGGCC within the chromosome 9 open reading frame 72 (C9orf72) gene is a prevalent factor in the development of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Here, we found that the absence of C9orf72 leads to a decrease in SEC16A protein levels, resulting in reduced localization of the guanine nucleotide exchange factor SEC12 at the ERES. Consequently, the small GTP binding protein SAR1 is unable to bind the endoplasmic reticulum normally, impairing the assembly of COPII. Ultimately, the disruption of SREBPs transport decreases de novo lipogenesis. These results suggest that C9orf72 acts as a novel role in regulating lipid homeostasis and may serve as a potential therapeutic target for obesity.

Laparoscopic hepatectomy for hepatocellular carcinoma in patients with clinically significant portal hypertension: a systematic review and meta-analysis.

OBJECTIVE: To compare the effects of laparoscopic hepatectomy (LH) on the short-term and long-term outcomes in hepatocellular carcinoma (HCC) patients with and without clinically significant portal hypertension (CSPH). METHODS: A systematic literature search of the PubMed, EMBASE, and Cochrane databases was performed for articles published from inception to March 1, 2023. Meta-analysis of surgical and oncological outcomes was performed using a random effects model. Data were summarized as mean difference and risk ratio with 95% confidence intervals. RESULTS: Five cohort studies with a total of 310 HCC patients were included (CSPH 143; Non-CSPH 167). In terms of surgical outcomes, estimated blood loss and the length of hospital stay were significantly lower in the Non-CSPH group than in the CSPH group. There were no significant differences between the two groups regarding other surgical outcomes, including the operative time, ratio of conversion to open surgery, and overall complication rate. In addition, there were also no significant differences between the two groups regarding the oncological outcomes, such as 1-, 3-, and 5-year overall survival. CONCLUSIONS: HCC patients with and without CSPH who underwent LH had comparable surgical and oncological outcomes. LH is a safe and effective treatment for HCC patients with CSPH under the premise of rational screening of patients.

Robotic versus laparoscopic liver resection for liver malignancy: a systematic review and meta-analysis of propensity score-matched studies.

OBJECTIVE: How different surgical procedures, including the robotic-assisted liver resection (RLR) and laparoscopic liver resection (LLR), can affect the prognosis of patients with liver malignancies is unclear. Thus, in this study, we compared the effects of RLR and LLR on the surgical and oncological outcomes in patients with liver malignancies through propensity score-matched cohort studies. METHODS: The PubMed, Embase, and Cochrane databases were searched using Medical Subject Headings terms and keywords from inception until May 31, 2023. The quality of the included studies was assessed using the Newcastle-Ottawa quality assessment scale. The mean difference with 95% confidence interval (95% CI) was used for analysis of continuous variables; the risk ratio with 95% CI was used for dichotomous variables; and the hazard ratio with 95% CI was used for survival-related variables. Meta-analysis was performed using a random-effects model. RESULTS: Five high-quality cohort studies with 986 patients were included (370 and 616 cases for RLR and LLR, respectively). In terms of surgical outcomes, there were no significant differences in the operation time, conversion rate to open surgery, overall complication rate, major complication rate, and length of hospital stay between the RLR and LLR groups. In terms of oncological outcomes, there were no significant differences in the 5-year overall survival and disease-free survival between the two groups. CONCLUSION: Surgical and oncological outcomes are comparable between RLR and LLR on patients with liver malignancies. Therefore, the benefits of applying RLR in patients with liver malignancies need to be further explored.

Expression profiles of lncRNAs, miRNAs, and mRNAs during the proliferative phase of liver regeneration in mice with liver fibrosis.

The role of lncRNAs in the regeneration of fibrotic liver is unclear. To address this issue, we established a 70% hepatectomy model of liver fibrosis in mice, used high-throughput sequencing technology to obtain the expression profiles of lncRNAs, miRNAs, and mRNAs, and constructed a lncRNA-miRNA-mRNA regulatory network. A total of 1329 lncRNAs, 167 miRNAs, and 6458 mRNAs were differentially expressed. On this basis, a lncRNA-miRNA-mRNA ceRNA regulatory network consisting of 38 DE lncRNAs, 24 DE miRNAs, and 299 DE mRNAs was constructed, and a transcription factor (TF) - mRNA regulatory network composed of 20 TFs and 98 DE mRNAs was built. Through the protein network analysis, a core protein interaction network composed of 20 hub genes was derived. Furthermore, Xist/miR-144-3p/Cdc14b and Snhg3/miR-365-3p/Map3k14 axes in the ceRNA regulatory network were verified by Real-Time quantitative PCR. Therefore, we concluded that these new insights may further our understanding of liver regeneration.

Integrated analysis of lncRNA/circRNA-miRNA-mRNA in the proliferative phase of liver regeneration in mice with liver fibrosis.

BACKGROUND: Non-coding RNAs play important roles in liver regeneration; however, their functions and mechanisms of action in the regeneration of fibrotic liver have not been elucidated. We aimed to clarify the expression patterns and regulatory functions of lncRNAs, circRNAs, miRNAs, and mRNAs in the proliferative phase of fibrotic liver regeneration. METHODS: Based on a mouse model of liver fibrosis with 70% hepatectomy, whole-transcriptome profiling was performed using high-throughput sequencing on samples collected at 0, 12, 24, 48, and 72 h after hepatectomy. Hub genes were selected by weighted gene co-expression network analysis and subjected to enrichment analysis. Integrated analysis was performed to reveal the interactions of differentially expressed (DE) lncRNAs, circRNAs, miRNAs, and mRNAs, and to construct lncRNA-mRNA cis- and trans-regulatory networks and lncRNA/circRNA-miRNA-mRNA ceRNA regulatory networks. Real-Time quantitative PCR was used to validate part of the ceRNA network. RESULTS: A total of 1,329 lncRNAs, 48 circRNAs, 167 miRNAs, and 6,458 mRNAs were differentially expressed, including 812 hub genes. Based on these DE RNAs, we examined several mechanisms of ncRNA regulatory networks, including lncRNA cis and trans interactions, circRNA parental genes, and ceRNA pathways. We constructed a cis-regulatory core network consisting of 64 lncRNA-mRNA pairs (53 DE lncRNAs and 58 hub genes), a trans-regulatory core network consisting of 103 lncRNA-mRNA pairs (18 DE lncRNAs and 85 hub genes), a lncRNA-miRNA-mRNA ceRNA core regulatory network (20 DE lncRNAs, 12 DE miRNAs, and 33 mRNAs), and a circRNA-miRNA-mRNA ceRNA core regulatory network (5 DE circRNAs, 5 DE miRNAs, and 39 mRNAs). CONCLUSIONS: These results reveal the expression patterns of lncRNAs, circRNAs, miRNAs, and mRNAs in the proliferative phase of fibrotic liver regeneration, as well as core regulatory networks of mRNAs and non-coding RNAs underlying liver regeneration. The findings provide insights into molecular mechanisms that may be useful in developing new therapeutic approaches to ameliorate diseases that are characterized by liver fibrosis, which would be beneficial for the prevention of liver failure and treatment of liver cancer.

C9orf72 regulates the unfolded protein response and stress granule formation by interacting with eIF2α.

Rationale: A C9orf72 hexanucleotide repeat expansion (GGGGCC) is the most common genetic origin of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Haploinsufficiency of C9orf72 has been proposed as a possible disease mechanism (loss-of-function mechanism). Additionally, the aberrantly activated unfolded protein response (UPR) and stress granule (SG) formation are associated with the etiopathology of both ALS and FTD. However, the molecular determinants in this pathogenesis are not well characterized. Methods: We performed an immunoprecipitation-mass spectrometry (IP-MS) assay to identify potential proteins interacting with the human C9orf72 protein. We used C9orf72 knockout cell and rat models to determine the roles of C9orf72 in translation initiation and the stress response. Results: Here, we show that C9orf72, which is genetically and pathologically related to ALS and FTD, interacts with eukaryotic initiation factor 2 subunit alpha (eIF2α) and regulates its function in translation initiation. C9orf72 knockout weakens the interaction between eIF2α and eIF2B5, leading to global translation inhibition. Moreover, the loss of C9orf72 results in primary ER stress with activated UPR in rat spleens, which is one of the causes of splenomegaly with inflammation in C9orf72 -/- rats. Finally, C9orf72 delays SG formation by interacting with eIF2α in stressed cells. Conclusions: In summary, these data reveal that C9orf72 modulates translation initiation, the UPR and SG formation, which have implications for understanding ALS/FTD pathogenesis.