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1.
Front Surg ; 11: 1343823, 2024.
Article in English | MEDLINE | ID: mdl-39132667

ABSTRACT

Background and purpose: Surgical indications for Bernese periacetabular osteotomy (PAO) are well-established. However, the extent of postoperative functional recovery varies widely, as observed in clinical follow-ups. Thus, preoperative evaluation is crucial. This study aims to identify factors that influence functional recovery post-PAO and to develop a predictive nomogram. Patients and methods: Retrospective data were collected between December 2016 and March 2022 at The First Affiliated Hospital of Shandong First Medical University. The dataset included demographic and imaging data of patients who underwent PAO. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identify influencing factors, which were further analyzed using multivariate logistic regression to construct a predictive nomogram for post-PAO functional recovery. Result: The analysis identified critical factors affecting functional recovery post-PAO, namely, the preoperative distance from the innermost surface of the femoral head to the ilioischial line, the surgical approach, preoperative acetabular depth, and the continuity of the preoperative Calve line. A nomogram was developed using these significant predictors. The model's validity was demonstrated by the receiver operating characteristic curve, with an area under the curve of 0.864. Additionally, the calibration curve confirmed the nomogram's accuracy, showing a strong correlation between observed and predicted probabilities, indicating high predictive accuracy. Conclusion: This predictive nomogram effectively identifies patients most suitable for PAO, providing valuable guidance for selecting surgical candidates and determining the appropriate surgical approach.

2.
J Cancer Res Clin Oncol ; 150(6): 311, 2024 Jun 19.
Article in English | MEDLINE | ID: mdl-38896142

ABSTRACT

BACKGROUND: Metabolic reprogramming is an emerging hallmark that influences the tumour microenvironment (TME) by regulating the behavior of cancer cells and immune cells. The relationship between metabolism and immunity remains elusive. The purpose of this study was to explore the predictive value of immune- and metabolism-related genes in hepatocellular carcinoma (HCC) and their intricate interplay with TME. METHODS: We established the immune- and metabolism-related signature (IMRPS) based on the LIHC cohort from The Cancer Genome Atlas (TCGA) dataset. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis and Cox regression analysis confirmed the prognostic value of IMRPS. We investigated differences in immune cell infiltration, clinical features, and therapeutic response between risk groups. The quantitative real-time PCR (qPCR) was used to confirm the expression of signature genes. Immunohistochemical staining was performed to evaluate immune infiltration features in HCC tissue samples. We conducted cell experiments including gene knockout, cell counting kit-8 (CCK-8), and flow cytometry to explore the role of the IMRPS key gene UCK2 in HCC. RNA-seq was used to further investigate the potential underlying mechanism involved. RESULTS: The IMRPS, composed of four genes, SMS, UCK2, PFKFB4 and MAPT, exhibited significant correlations with survival, immune cell infiltration, clinical features, immune checkpoints and therapeutic response. The IMRPS was shown to be an excellent predictor of HCC prognosis. It could stratify patients appropriately and characterize the TME accurately. The high-risk HCC group exhibited an immunosuppressive microenvironment with abundant M2-like macrophage infiltration, which was confirmed by the immunohistochemistry results. The results of qPCR revealed that the expression of signature genes in 20 HCC tissues was significantly greater than that in adjacent normal tissues. After the key gene UCK2 was knocked out, the proliferation of the Huh7 cell line was significantly inhibited, and monocyte-derived macrophages polarized towards an M1-like phenotype in the coculture system. RNA-seq and GSEA suggested that the phenotypes were closely related to the negative regulation of growth and regulation of macrophage chemotaxis. CONCLUSIONS: This study established a new IMRS for the accurate prediction of patient prognosis and the TME, which is also helpful for identifying new targets for the treatment of HCC.


Subject(s)
Carcinoma, Hepatocellular , Liver Neoplasms , Tumor Microenvironment , Carcinoma, Hepatocellular/genetics , Carcinoma, Hepatocellular/immunology , Carcinoma, Hepatocellular/pathology , Humans , Liver Neoplasms/genetics , Liver Neoplasms/immunology , Liver Neoplasms/pathology , Tumor Microenvironment/immunology , Tumor Microenvironment/genetics , Prognosis , Biomarkers, Tumor/genetics , Female , Gene Expression Regulation, Neoplastic , Male , Middle Aged , Gene Expression Profiling , Transcriptome
3.
Article in English | MEDLINE | ID: mdl-38890106

ABSTRACT

BACKGROUND: Liver transplantations (LTs) with extended criteria have produced surgical results comparable to those obtained with traditional standards. However, it is not sufficient to predict hepatocellular carcinoma (HCC) recurrence after LT according to morphological criteria alone. The present study aimed to construct a nomogram for predicting HCC recurrence after LT using extended selection criteria. METHODS: Retrospective data on patients with HCC, including pathology, serological markers and follow-up data, were collected from January 2015 to April 2020 at Huashan Hospital, Fudan University, Shanghai, China. Logistic least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to identify and construct the prognostic nomogram. Receiver operating characteristic (ROC) curves, Kaplan-Meier curves, decision curve analyses (DCAs), calibration diagrams, net reclassification indices (NRIs) and integrated discrimination improvement (IDI) values were used to assess the prognostic capacity of the nomogram. RESULTS: A total of 301 patients with HCC who underwent LT were enrolled in the study. The nomogram was constructed, and the ROC curve showed good performance in predicting survival in both the development set (2/3) and the validation set (1/3) (the area under the curve reached 0.748 and 0.716, respectively). According to the median value of the risk score, the patients were categorized into the high- and low-risk groups, which had significantly different recurrence-free survival (RFS) rates (P < 0.01). Compared with the Milan criteria and University of California San Francisco (UCSF) criteria, DCA revealed that the new nomogram model had the best net benefit in predicting 1-, 3- and 5-year RFS. The nomogram performed well for calibration, NRI and IDI improvement. CONCLUSIONS: The nomogram, based on the Milan criteria and serological markers, showed good accuracy in predicting the recurrence of HCC after LT using extended selection criteria.

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