BACKGROUND:While the inflammation-to-carcinoma transition in gallbladder carcinogenesis is well recognized, the molecular pathogenesis of gallbladder adenomyomatosis (GBA), which is clinically associated with both chronic cholecystitis (CCS) and gallbladder carcinoma (GBC), remains poorly understood. Hence, we aimed to evaluate the premalignant potential of GBA and to identify previously unrecognized molecular pathways involved in gallbladder carcinogenesis. METHODS:We first conducted single-cell RNA sequencing and downstream bioinformatics analyses on samples from nine patients with CCS, GBA, and GBC. Key molecular findings were subsequently validated using publicly available bulk RNA-seq datasets (n = 80) and multiplex immunohistochemistry in an independent cohort (n = 62). Functional validation of tumor microenvironment-associated molecules was performed through a series of in vitro and in vivo assays. RESULTS:We profiled the transcriptomic landscape of 89 428 single cells and uncovered distinct epithelial and immune ecosystems across different disease stages. A transitional epithelial subset (SPP1-CCL20⁺ EpiC3) and PTGER4+CCL5⁺ CD8⁺T cells were identified in GBA tissues, indicating malignant potential. Notably, PRDX1 was selectively upregulated in the GBC-derived Macro04 macrophages, which displayed strong interactions with exhausted T cells and malignant epithelial cells. Functionally, PRDX1 knockdown in macrophages disrupted mitochondrial oxidative metabolism, leading to metabolic reprogramming and diminished M2 polarization, thereby attenuating tumor cell proliferation, migration, and invasion both in vitro and in vivo. Mechanistically, PRDX1 loss downregulated PPAR-δ and CPT1A, whereas PPAR-δ agonist GW1516 restored CPT1A expression as well as the associated metabolic and polarization defects, supporting a PRDX1/PPAR-δ/CPT1A regulatory axis governing macrophage immunometabolism in GBC. CONCLUSIONS:Our study reveals that GBA harbors malignant potential at both epithelial and immune microenvironmental levels. The expression of CCL20, CCL5, and PRDX1 may serve as molecular markers for stratifying high-risk GBA, while PRDX1 represents a promising therapeutic target for reprogramming the tumor immune microenvironment in GBC.

Background Docetaxel is a first-line chemotherapy drug for breast cancer and is traditionally dosed based on body surface area (BSA). However, this method often leads to significant inter-patient variability and a high incidence of adverse drug reactions (ADRs). Therapeutic drug monitoring (TDM) offers a personalized dosing approach that may improve drug safety and efficacy. This study aimed to evaluate the clinical and pharmacoeconomic benefits of TDM-guided dosing compared to traditional BSA-based dosing in breast cancer patients receiving docetaxel-based chemotherapy. Methods A randomized controlled study was conducted at the Department of Breast Surgery, IPMCH, from October 2022 to July 2024. A total of 208 breast cancer patients were enrolled and randomly assigned to two groups: the BSA group (n=104) and the TDM-guided pharmacokinetics (PK) group (n=104). Adverse drug reactions—including hematological, gastrointestinal, skin, neurotoxic, and cardiotoxic events—were monitored and compared between groups. Liver function markers (ALT, AST, ALP) and pharmacoeconomic data (treatment-related costs) were also assessed. Statistical analyses included univariate and interaction models to evaluate the impact of dosing strategy on ADRs and costs. Results Patients in the TDM-guided PK group exhibited significantly lower levels of ALT, AST, and ALP, indicating reduced hepatic toxicity. Gastrointestinal ADRs—including nausea, diarrhea, and constipation—were less frequent and less severe in the PK group compared to the BSA group. Overall, the incidence and severity of ADRs were markedly reduced in the PK group. Pharmacoeconomic analysis demonstrated consistently lower treatment-related costs in the PK group. Both univariate and interaction analyses confirmed the clinical and economic benefits of TDM-guided dosing. Conclusion TDM-guided docetaxel dosing significantly reduced ADRs and improved cost efficiency in breast cancer chemotherapy. These findings support the implementation of TDM as a superior strategy to traditional BSA-based dosing, with potential to enhance both patient safety and healthcare resource utilization.

Background and AimsClassification system of tacrolimus elimination and its clinical significance has not been well described in liver transplantation. This study aimed to present a novel tacrolimus clearance clinical-FIS (Fast-Intermediate-Slow) classification and its gene prediction system.MethodsPatients from 3 transplant centers were enrolled in this study. All recipients and their corresponding donor livers from center 1 were genotyped using an Affymetrix DMET Plus microarray, and association analysis was performed using trough blood concentration/weight-adjusted-dose ratios (CDR, (ng/mL)/(mg/kg)). The candidate-associated loci were then sequenced in center 2 and center 3 patients for verification.ResultsA clinical classification based on tacrolimus CDR can effectively divide liver transplantation patients into fast elimination (FE), intermediate elimination (IE), and slow elimination (SE) groups, which we called the clinical-FIS classification. Trough blood concentrations in the clinical-SE group during the early postoperative period were higher than those in the clinical-FE and clinical-IE groups, which could lead to delayed recovery of liver (P = 0.0373) and kidney function (P = 0.0135) and a higher infection rate (P = 0.0086). The prediction accuracy of the current CPIC (Clinical Pharmacogenetics Implementation Consortium)-EIP metabolizer classification based on recipient CYP3A5 rs776746 genotype for clinical-FIS classification was only 35.56%. A newly established genetic-EIP classification including major effect genetic factors (donor and recipient CYP3A5 rs776746) and minor effect genetic factors (recipient SULT1E1 rs3775770 and donor SLC7A8 rs7141505) showed 73.2% overall consistency with the former clinical FIS classification.ConclusionOur study presented a novel tacrolimus clearance classification, clinical-FIS, and then proposed a novel prospective genetic-EIP classification as a genotyping basis for precisely predicting the clinical-FIS.

Background Post-transplant diabetes mellitus (PTDM) is a significant complication following liver transplantation, primarily induced by immunosuppressive agents like tacrolimus. While PTDM shares some clinical features with type 2 diabetes (T2D), its distinct pathogenesis remains poorly understood. Methods We developed mouse models of PTDM and T2D using tacrolimus, streptozotocin (STZ), and a high-fat diet to simulate disease conditions. Integrated whole-transcriptomics and metabolomics analyses were performed on liver, pancreatic, and adipose tissue to map disease-specific molecular landscapes and nominate candidate biomarkers. Results Despite higher body weight, PTDM mice exhibited lower blood glucose and improved insulin tolerance compared to T2D mice. Multi-omics analyses revealed PTDM-specific activation of the MAPK pathway, marked Treg cell infiltration in in the liver and pancreas, and dysregulation of lincRNA-circRNA networks. Metabolomics identified altered metabolites including 2,2-dimethylsuccinic acid, indicating mitochondrial dysfunction. Most notably, integrative analysis nominated 2510002D24Rik (also known as Pants) — a previously uncharacterized, liver-restricted gene — as a hub coordinating immune-metabolic crosstalk, positioning it as a lead candidate biomarker for PTDM. Conclusions Our multi-omics approach uncovers distinct molecular signatures that differentiate PTDM from T2D, providing novel therapeutic interventions for PTDM.

BackgroundDocetaxel is commonly used in breast cancer chemotherapy. The previous drug dose is generally calculated based on body surface area (BSA). However, the metabolism varies greatly among different patients. Docetaxel therapeutic drug monitoring (TDM) helps monitor adverse drug reactions and explore the appropriate range of area under the curve (AUC) to ensure chemotherapy effectiveness and reduce adverse reaction occurrence.MethodsWe conducted a real-world retrospective study and included 180 breast cancer patients, who received a chemotherapy regimen containing docetaxel. The patients’ demographic and tumor data were reviewed. Adverse reaction data during chemotherapy treatment were collected through patient questionnaires and laboratory test results. Univariate logistic regression analysis was performed on 33 patient indexes, including basic information, blood toxicity, liver and kidney function, gastrointestinal reactions, and cardiotoxicity.ResultsThe adverse reactions of chemotherapy were matched with different docetaxel AUC results through univariate analysis. The patients between the groups were no statistically significant differences in terms of demographic and tumor data, including age, height, weight, BSA, and body mass index (p > 0.05). Univariate analysis revealed significant differences in albumin (ALB) levels (p = 0.037), creatinine (CREA) levels (p = 0.002), nausea occurrence (p = 0.008), vomiting occurrence (p = 0.013), rashes occurrence (p = 0.002), and chemotherapy-induced alopecia incidence (CIA) (p = 0.002). Based on the results of the univariate analysis, binary logistic regression analysis was further conducted to identify predictors contributing to the occurrence of chemotherapy adverse reactions. The results demonstrated that an AUC value greater than 2.5 mg h/L was significantly associated with increased risk of certain adverse reactions such as rashes, CIA, CREA, and ALB.ConclusionThe docetaxel TDM provides a reliable basis for monitoring chemotherapy adverse reactions, with high AUC significantly associated with certain adverse reactions. Future studies are expected to include more patients and conduct multi-center trials to obtain a suitable AUC range for Chinese patients, which will guide the determination of clinical chemotherapy doses and reduce the occurrence of adverse reactions.

Objectives: Patients with intermediate or advanced hepatocellular carcinoma (HCC) require repeated disease monitoring, prognosis assessment and treatment planning. In 2018, a novel machine learning methodology “survival path” (SP) was developed to facilitate dynamic prognosis prediction and treatment planning. One year after, a deep learning approach called Dynamic Deephit was developed. The performance of the two state-of-art models in dynamic prognostication have not been compared. Methods: We trained and tested the SP and Dynamic DeepHit models in a large cohort of 2511 HCC patients using time-series data. The time-series data were converted into data of time slices, with an interval of three months. The time-dependent c-index for OS at given prediction time ( t = 1, 6, 12, 18 months) and evaluation time (∆ t = 3, 6, 9, 12, 18, 24, 36, 48 months) were compared. Results: The comparison between SP model and Dynamic DeepHit-HCC model showed the latter had significant better performance at the time of initial admission. The time-dependent c-index of Dynamic DeepHit-HCC model gradually decreased with the extension of time (from 0.756 to 0.639 in the training set; from 0.787 to 0.661 in internal testing set; from 0.725 to 0.668 in multicenter testing set); while the time-dependent c-index of SP model displayed an increased trend (from 0.665 to 0.748 in the training set; from 0.608 to 0.743 in internal testing set; from 0.643 to 0.720 in multicenter testing set). When the prediction time comes to 6 months or later since initial treatment, the survival path model outperformed the dynamic DeepHit model at late evaluation times (∆ t > 12 months). Conclusions: This research highlighted the unique strengths of both models. The SP model had advantage in long term prediction while the Dynamic DeepHit-HCC model had advantages in prediction at near time points. Fine selection of models is needed in dealing with different scenarios.

Lectin-likeoxidized low-density lipoprotein receptor (LOX-1) has been identified to beinvolved in the development of atherosclerosis. There is an increasing experimental evidence which indicated that LOX-1 was implicated in cancer tumorigenesis. However, the expression and the prognostic value of LOX-1 in multiple cancers still require the further analysis. Pubmed, Embase and the Cochrane Library were used for the literature review collection with the confined date up to 31 December 2021. Ten studies including 1982 patients were performed in meta-analysis according to the inclusion and exclusion criteria. Oncomine, Gene Expression Profiling Interactive Analysis(GEPIA), Kaplan-Meier plotter and Tumor Immune Estimation Resource (TIMER) were utilized to analyze the differential expression and the prognostic value of LOX-1 in different cancers. Records from Gene Expression Omnibus (GEO) database were applied for the verification test. The meta-pooled result demonstrated that elevated LOX-1 predicted a poor survival in some cancers (HR = 1.95, 95%CI 1.46-2.44, P < 0.001). In this sense, further analysis using databases found the expression of LOX-1 was higher in breast cancer, colorectal cancer, gastric cancer and pancreatic cancer while the lower expression in lung squamous cell carcinoma was observed. Moreover, the expression of LOX-1 was related to the tumor stages of colorectal cancer, gastric cancer and pancreatic cancer. The survival analysis revealed that LOX-1 was a potential prognostic factor for the patients with colorectal cancer, gastric cancer, pancreatic cancer and lung squamous cell carcinoma. Consequently, this study may provide a novel insight for the expression and the prognostic value of LOX-1 in specific cancers.

Background and objectiveA considerable number of pregnant women who were supplemented with folate and vitamin B12 were selected as major participants in studying the one-carbon metabolic (OCM) pathway. Our study aimed to explore the effects of OCM-related indicators on pregnancy-induced hypertension (PIH) and preeclampsia (PE) in pregnant women with folate and vitamin B12 supplementation.Subjects and methodsA total of 1,178 pregnant women who took multivitamin tablets containing 800 μg folate and 4 μg vitamin B12 daily from 3 months before pregnancy to 3 months after pregnancy were enrolled in this study. These pregnant women were classified into three groups: the normotensive group (n = 1,006), the PIH group (n = 131), and the PE group (n = 41). The information on age, weight, body mass index (BMI), number of embryos, gravidity, parity, and OCM-related indicators (serum level of homocysteine, folate, and vitamin B12; MTHFR C677T genotype) was collected.ResultsThe accuracy of the prediction model based on the screened independent risk factors (hyperhomocysteine, OR = 1.170, 95% CI = 1.061–1.291; high folate status, OR = 1.018, 95% CI = 0.999–1.038; and high BMI, OR = 1.216, 95% CI = 1.140–1.297) for PIH in subjects with MTHFR CC genotype (AUC = 0.802) was obviously higher than that in subjects with MTHFR CT, TT genotype (AUC = 0.684,0.685, respectively) by receiver operating characteristic curve analysis. The homocysteine level of the PIH group was significantly higher than that of the normotensive group only in subjects with the MTHFR CC genotype (p = 0.005). A negative correlation between homocysteine and folate appeared in subjects with MTHFR CT + TT genotype (p = 0.005). A model including multiple embryos, nulliparas, and lower folate could predict the process from PIH to PE (AUC = 0.781, p < 0.0001).ConclusionThe prediction model composed of homocysteine, folate, and BMI for PIH was suitable for subjects with MTHFR CC genotype in pregnant women with supplementation of folate and vitamin B12. Lower folate levels could be an independent risk factor in developing the process from PIH to PE.

Abstract Backgroud: This research delved into the underlying mechanisms responsible for post-transplant diabetes mellitus (PTDM) and comparisons with type 2 diabetes (T2D). Methods: Comprehensive analyses, encompassing both transcriptomics and metabolomics, were conducted on liver and pancreatic tissues from the PTDM and T2D groups. Furthermore, distinctions in competing endogenous RNA (ceRNA) networks were explored. Weighted gene co-expression network analysis (WGCNA)was implemented to identify clusters of genes exhibiting strong correlations among the liver, pancreas, and adipose tissue. Results: Compared to their T2D counterparts, PTDM mice exhibited notable differences in higher body weight (P <0.05), lower blood glucose levels (P <0.05), and enhanced insulin tolerance (P <0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated significant alterations in the MAPK pathway and leukocyte migration within the liver, as well as variations in the differentiation of Th17, Th1, and Th2 cells, along with T cell activation, within the pancreas. In addition, the MAPK signaling pathway and leukocyte migration pathways were significantly modified in adipose tissue. The ceRNA network analysis highlighted substantial changes, revealing 164 long intergenic non-coding RNA (lincRNA) and 154 circular RNA (circRNA) networks significantly changed in the liver, and 445 lincRNA and 135 circRNA networks in the pancreas that were significantly altered in the PTDM group compared to T2D group. Notably, the metabolites Resveratrol, Aldehydo-D-xylose, 3-Hydroxybutyric acid, 5-Aminoimidazole-4-carboxamide, Leucinic acid and (R)-lipoic acid displayed significant changes in the liver in PTDM mice, with their regulation attributed to 2510002D24RIK. Conclusion:In summary, PTDM mice exhibited distinctive multi-omics and pathological characteristics compared to their T2D counterparts.

Hepatocellular carcinoma (HCC) ranks the most common types of cancer worldwide. As the fourth leading cause of cancer-related deaths, its prognosis remains poor. Most patients developed HCC on the basis of chronic liver disease. Cirrhosis is an important precancerous lesion for HCC. However, the molecular mechanisms in HCC development are still unclear. To explore the changes at the level of transcriptome in this process, we performed RNA-sequencing on cirrhosis, HCC and paracancerous tissues. Continuously changing mRNA was identified using Mfuzz cluster analysis, then their functions were explored by enrichment analyses. Data of cirrhotic HCC patients were obtained from TCGA, and a fatty acid metabolism (FAM)-related prognostic signature was then established. The performance and immunity relevance of the signature were verified in internal and external datasets. Finally, we validated the expression and function of ADH1C by experiments. As a result, 2012 differently expressed mRNA were identified by RNA-sequencing and bioinformatics analyses. Fatty acid metabolism was identified as a critical pathway by enrichment analyses of the DEGs. A FAM-related prognostic model and nomogram based on it were efficient in predicting the prognosis of cirrhotic HCC patients, as patients with higher risk scores had shorter survival time. Risk scores calculated by the signature were then proved to be associated with a tumor immune environment. ADH1C were downregulated in HCC, while silence of ADH1C could significantly promote proliferation and motility of the HCC cell line.

Supplementary Figures S1-S3; Supplementary Tables S1-S4.

Background Tacrolimus (FK506) is the cornerstone of immunosuppression after liver transplantation (LT), however, clinically, switching from FK506 to cyclosporine (SFTC) is common in LT patients with tacrolimus intolerance. The aim of this study was to investigate the genetic risk of patients with tacrolimus intolerance. Methods A total of 114 LT patients were enrolled in this retrospective study. SNPs were genotyped using Infinium Human Exome-12 v1.2 BeadChip, and genome-wide gene expression levels were profiled using Agilent G4112F array. Results SFTC was a potential risk factor of dyslipidemia (OR=4.774[1.122-20.311], p = 0.034) and insulin resistance (IR) (OR=6.25[1.451-26.916], p = 0.014), but did not affect the survival of LT patients. Differential expression analysis showed donor CYP3A5 , CYP2C9 , CFTR , and GSTP1 , four important pharmacogenetic genes were significantly up-regulated in the tacrolimus intolerance group. Twelve SNPs of these four genes were screened to investigate the effects on tacrolimus intolerance. Regression analysis showed donor rs4646450 (OR=3.23 [1.22-8.60] per each A allele, p = 0.01), donor rs6977165 (OR=6.44 [1.09-37.87] per each C allele, p = 0.02), and donor rs776746 (OR=3.31 [1.25-8.81] per each A allele, p = 0.01) were independent risk factors of tacrolimus intolerance. Conclusions These results suggested that SFTC was a potential risk factor for dyslipidemia and IR after LT. Besides, rs4646450, rs6977165, and rs776746 of CYP3A5 might be the underlying genetic risks of tacrolimus intolerance. This might help transplant surgeons make earlier clinical decisions about the use of immunosuppression.

Background:The initial dose of tacrolimus after liver transplantation (LT) is critical for rapidly achieving the steady state of the drug concentration, minimizing the potential adverse reactions and warranting long-term patient prognosis. We aimed to develop and validate a genotype-guided model for determining personalized initial dose of tacrolimus. Methods:By combining pharmacokinetic modeling, pharmacogenomic analysis and multiple statistical methods, we developed a genotype-guided model to predict individualized tacrolimus initial dose after LT in the discovery (n = 150) and validation cohorts (n = 97) respectively. This model was further validated in a prospective, randomized and single-blind clinical trial from August, 2021 to February, 2022 (n = 40, ChiCTR2100050288). Findings:Our model included donor's and recipient's genotypes, recipient's weight and total bilirubin, which achieved an area under the curve of receiver operating characteristic curve (AUC of ROC) of 0.88 and 0.79 in the discovery and validation cohorts, respectively. We found that patients who were given tacrolimus within the recommended concentration range (RCR) (4-10 ng/mL), the new-onset metabolic syndromes are lower, especially for new-onset diabetes (p = 0.043). In the clinical trial, compared to those in experience-based (EB) group, patients in the model-based (MB) group were more likely to achieving the RCR (75% vs 40%, p = 0.025) with a more variable individualized dose (0.023-0.096 mg/kg/day vs 0.045-0.057 mg/kg/day). Moreover, significantly fewer medication adjustments were required for the MB group than the EB group (2.75 ± 2.01 vs 6.05 ± 3.35, p = 0.001). Interpretation:Our genotype-based model significantly improved the initial dosing accuracy of tacrolimus and reduced the number of medication adjustments, which are critical for improving the prognosis of LT patients. Funding:National Natural Science Foundation of China, Shanghai three-year action plan, National Science and Technology Major Project of China.

Background. New-onset diabetes mellitus after transplantation (NODAT) is a leading cause of morbidity and mortality after heart transplantation (HT), which still remains a clinical challenge. Methods. In this study, 522,708 follow-up records of HT were reviewed. After screening, 14,452 patients were analyzed when combined with immunosuppression records. We divided all patients into no-NODAT group, NODAT group, and preexisting diabetes group based on whether the patient had diabetes and the time when it occurred. Cox regression models were used to examine independent risk factors. A nomogram was established to predict the incidence of NODAT after HT. The machine learning method were used to confirm the prediction accuracy and reliability of the nomogram. Results. Patients who experienced NODAT after HT had poor survival compared with those without NODAT. Tacrolimus, cyclosporine A (CsA), rapamycin, donor age, and recipient age at the time of transplant were significant predictors of NODAT. Tacrolimus had a more significant association with NODAT, followed by rapamycin and CsA. The nomogram method we adopted in this study had an accuracy of 63% in predicting the incidence of NODAT. Conclusion. The survival probability of HT recipients with NODAT showed a significant decreasing tendency. However, there was no difference in survival probability between patients with preexisting diabetes and patients with NODAT. Tacrolimus had a more significant association with NODAT than CsA and rapamycin.

Purpose Distal metastases are a major cause of poor prognosis in colorectal cancer patients. Approximately 95% of metastatic colorectal cancers are defined as DNA mismatch repair proficient (pMMR). Our previous study found that miR-6511b-5p was downregulated in pMMR colorectal cancer. However, the mechanism of miR-6511b-5p in pMMR colorectal cancer metastases remain unclear. Methods We first used quantitative real-time PCR to evaluate the role of miR-6511b-5p in colorectal cancer. Second, we conducted invasion assays and wound healing assays to investigate the role of miR-6511b-5p and CD44 in colorectal cancer cells metastases. Third, luciferase reporter assay, in situ hybridization (ISH), and immunohistochemistry assays were performed to study the relationship between miR-6511b-5p and BRG1. Finally, real-time quantitative PCR, immunohistochemistry, and chromatin immunoprecipitation (ChIP) assays were performed to analyze the relationship between BRG1 and CD44 in colorectal cancer. Results We found that lower expression of miR-6511b-5p appeared more often in pMMR colorectal cancer patients compared with dMMR (mismatch repair deficient) cases, and was positively correlated with metastases. In vitro, overexpression of miR-6511b-5p inhibited metastasis by decreasing CD44 expression via directly targeting BRG1 in colorectal cancer. Furthermore, BRG1 knockdown decreased the expression of CD44 by promoting CD44 methylation in colorectal cancer cells. Conclusion Our data suggest that miR-6511b-5p may act as a promising biomarker and treatment target for pMMR colorectal cancer, particularly in metastatic patients. Mechanistically, miR-6511b-5p suppresses invasion and migration of colorectal cancer cells through methylation of CD44 via directly targeting BRG1.

There are rarely systematic studies to analyze the prognostic factors among non-surgical liver cancer patients. Whether there is a gender difference in the survival of non-surgical liver cancer patients and what may cause this difference is still unclear. A total of 12,312 non-surgical liver cancer patients were enrolled in this study. Age, race, sex, grade, tumor TNM stage, marital status, tumor size, and histological type were independent risk factors in liver cancer and were confirmed in the validation cohort. Before menopause, females demonstrated a better mean survival probability than males (39.4±1.4 vs. 32.7±0.8 months, respectively; p<0.001), and continued in post-menopause. The results of differentially expressed genes (DEGs) and KEGG pathway analysis showed that there were significant differences in steroid hormone biosynthesis between male and female liver cancer patients. In vitro experiments revealed that estradiol inhibited the proliferation of hepatocellular cancer cell lines and increased apoptosis, but estrone exerted no effect. In conclusion, gender differences in prognosis among non-surgical liver cancer patients were confirmed and attributable primarily to estradiol.

Background: Pancreatic cancer (PC) is one of the most aggressive and lethal malignancies in the world. High cholesterol intake may have a certain association with an elevated risk of PC, though dyslipidemia in PC patients has rarely been reported. In this study, we compared serum lipids levels between PC and non-PC tumor patients and assessed their prognostic value in PC. Methods: 271 patients treated at Wuhan Union Hospital from January 2012 to December 2016 and 204 individuals at Shanghai General Hospital from January 2018 to December 2019 were recruited. Their demographic parameters, laboratory data, pathological information, and clinical outcomes were extracted and analyzed. The mRNA expressions of related lipoprotein, low density lipoprotein receptor (LDLR) and high density lipoprotein binding protein (HDLBP), in PC tissues and paired noncancerous tissues and follow-up information were assessed based on the GEO database (GSE15471 and GSE62165) and TCGA database. Results: A total of 172 non-PC tumor patients and 260 PC patients were finally eligible for our analysis. PC patients exhibited higher levels of serum triglyceride, cholesterol, and low-density lipoprotein (LDL) and a lower serum high-density lipoprotein (HDL) level on admission versus the non-PC tumor group. In PC patients, LDLR mRNA expression was upregulated, and HDLBP mRNA expression was downregulated in cancerous tissues compared to these levels in paired noncancerous tissues. The survival analysis revealed that dyslipidemia had a non-significant association with a poor prognosis, but PC patients with a high LDLR level were at risk of poor survival. Conclusion: Dyslipidemia is detected in PC patients but has a non-significant relation to PC prognosis. However, LDLR may be a potential predictive marker for PC prognosis.

To investigate the postoperative serum triglyceride (TG) levels in predicting the risk of new-onset diabetes mellitus (NODM) in patients following allogeneic liver transplantation. One hundred and forty three patients undergoing allogeneic liver transplantation in Shanghai General Hospital from July 2007 to July 2014 were enrolled in this study. The NODM developed in 33 patients after liver transplantation. The curve of dynamic TG levels in the early period after liver transplantation was generated. Independent risk factors of NODM were determined by univariate and multivariant logistic regression analyses. The clinical value of TG in predicting NODM was analyzed by area under the ROC curve (AUC). Serum TG levels were gradually rising in the first week and then reached the plateau phase (stable TG, sTG) in patients after surgery. The sTG in NODM group were significantly higher than that in non-NODM group (=-2.31, <0.05). Glucocorticoid therapy (=4.054, <0.01), FK506 drug concentration in the first week after operation (=3.482, <0.05) and sTG (=3.156, <0.05) were independent risk factors of NODM. ROC curve analysis showed that the AUC of sTG in predicting NODM was 0.72. TG shows a gradual recovery process in the early period after liver transplantation, and the higher TG level in stable phase may significantly increase the risk of NODM in patients.

Dear Editor, The majority of allograft rejection occurs within 1 month after liver transplantation; with the highest incidence around 7–10 days. In this study, we demonstrate the impact of donor and recipient genotypes on tacrolimus clearance and dosing requirements during the first 28 days following liver transplantation. Tacrolimus is primarily metabolized by cytochrome P450 (CYP) 3A isozymes, CYP3A4 and CYP3A5, which mediate hepatic and intestinal biotransformation.1 However, it is unknown how the influence of CYP3A5 genotype of the donor and recipient contribute to tacrolimus variability as liver performance improves with time in the early post-operative phase.2–7 There remains an unmet medical need to find an optimal dose regimen for immunosuppressants within the first few weeks after transplantation to avoid potential toxicities due to overdose or acute rejection.2 Thus, the goal of our work is to establish personalized immunosuppressive regimens following liver transplantation. By using genetics and patient-related factors, individualized dosing regimens can be initiated and used with current drugmonitoring protocols to decrease toxicity and graft rejection during the early phases of post-transplant. We enrolled adult patients in two independent cohorts undergoing orthotopic liver transplantation. Tacrolimus and mycophenolate mofetil were administered following transplant without steroids. Patients were excluded from undergoing multi-organ transplantation or had incomplete data. Cohort A (index set) comprised 115 from Shanghai General Hospital Affiliated to Shanghai Jiao Tong University. Cohort B (validation set) comprised 95 patients from First Affiliated Hospital of Zhengzhou University. The patient demographics are displayed in Table 1. The research was carried out in accordance with the Declaration of Helsinki and was approved by the Ethics Commit-

Purpose To explore the relationship between rs2291075 polymorphism in SLCO1B1 gene, which encodes an influx transmembrane protein transporter, and tacrolimus dose–corrected trough concentration (C/D, ng ml −1 mg −1 kg −1 ) in the early period after liver transplantation. Methods CYP3A5 rs776746 and SLCO1B1 rs2291075 polymorphisms of 210 liver transplantation patients and their corresponding donor livers were assessed by PCR amplification and DNA sequencing. The influence of gene polymorphisms on C/D values of tacrolimus was analyzed. The early postoperative period after liver transplantation was divided into the convalescence phase (1–14 days) and stationary phase (15–28 days) according to the change of liver function and tacrolimus C/D values. Results The combined analysis of donor and recipient CYP3A5 rs776746 could distinguish the metabolic phenotype of tacrolimus into three groups: fast elimination (FE), intermediate elimination (IE), and slow elimination (SE), which was entitled the FIS classification system. Tacrolimus C/D ratios of recipient SLCO1B1 rs2291075 CT and TT carriers were very close and were significantly lower than those of recipient SLCO1B1 rs2291075 CC genotype carriers in convalescence phase ( p = 0.0195) and in stationary phase ( p = 0.0152). There were no statistically significant differences between tacrolimus C/D ratios of patients carried with SLCO1B1 rs2291075 CT, TT genotype donors, and those carried with SLCO1B1 rs2291075 CC genotype donors. A model consisting of tacrolimus daily dose, total bilirubin, FIS classification, and recipient SLCO1B1 rs2291075 could predict tacrolimus C/D ratios in the convalescence phase by multivariate analysis. However, recipient SLCO1B1 rs2291075 genotype failed to enter forecast model for C/D ratios in stationary phase. Recipient SLCO1B1 rs2291075 genotype had significant effect on tacrolimus C/D ratios in convalescence phase ( p = 0.0300) and stationary phase ( p = 0.0400) in subgroup, which excluded the interference come from donor and recipient CYP3A5 rs776746. Conclusion SLCO1B1 rs2291075 could be a novel genetic locus associated with tacrolimus metabolism. The combined analysis of donor and recipient CYP3A5 rs776746, recipient SLCO1B1 rs2291075 genotypes, could be helpful to guide the personalized administration of tacrolimus in early period after liver transplantation.