Multidisciplinary consensus recommendations for management of hepatocellular carcinoma in Middle East and North Africa region

Waked, Imam; Alsammany, Sherif; Tirmazy, Sayed Hammad; Rasul, Kakil; Bani‐Issa, Jafar; Abdel‐Razek, Wael; Omar, Ashraf; Shafik, Amr; Eid, Salem; Abdelaal, Amr; Hosni, Ahmed; Esmat, Gamal

doi:10.1111/liv.15685

Cited by 2 publications

(1 citation statement)

References 124 publications

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“…Hepatocellular carcinoma (HCC) stands as the sixth most prevalent cancer and ranks third among causes of cancer-related mortality (Sung et al, 2021;Waked et al, 2023). Therefore, HCC remains a significant global health concern that necessitates urgent attention.…”

Section: Introductionmentioning

confidence: 99%

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics

Zhang,

Wang,

Zhang

et al. 2024

Front. Pharmacol.

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Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals.Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You’an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method.Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064).Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.

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Section: Introductionmentioning

confidence: 99%

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics

Zhang,

Wang,

Zhang

et al. 2024

Front. Pharmacol.

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Integrated multiomics analysis identified comprehensive crosstalk between diverse programmed cell death patterns and novel molecular subtypes in Hepatocellular Carcinoma

Chen,

Hu,

et al. 2024

Sci Rep

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Hepatocellular carcinoma (HCC) is a highly aggressive malignancy with increasing global prevalence and is one of the leading causes of cancer-related mortality in the human population. Developing robust clinical prediction models and prognostic stratification strategies is crucial for developing individualized treatment plans. A range of novel forms of programmed cell death (PCD) plays a role in the pathological progression and advancement of HCC, and in-depth study of PCD is expected to further improve the prognosis of HCC patients. Sixteen patterns (apoptosis, autophagy, anoikis, lysosome-dependent cell death, immunogenic cell death, necroptosis, ferroptosis, netosis, pyroptosis, disulfidptosis, entotic cell death, cuproptosis, parthanatos, netotic cell death, alkaliptosis, and oxeiptosis) related to PCD were collected from the literatures and used for subsequent analysis. Supervised (Elastic net, Random Forest, XgBoost, and Boruta) and unsupervised (Nonnegative Matrix Factorization, NMF) clustering algorithms were applied to develop and validate a novel classifier for the individualized management of HCC patients at the transcriptomic, proteomic and single-cell levels. Multiple machine learning algorithms developed a programmed cell death index (PCDI) comprising five robust signatures (FTL, G6PD, SLC2A1, HTRA2, and DLAT) in four independent HCC cohorts, and a higher PCDI was predictive of higher pathological grades and worse prognoses. Furthermore, a higher PCDI was found to be correlated with the presence of a repressive tumor immune microenvironment (TME), as determined through an integrated examination of bulk and single-cell transcriptome data. In addition, patients with TP53 mutation had higher PCDI in comparison with TP53 WT patients. Three HCC subtypes were identified through unsupervised clustering (NMF), exhibiting distinct prognoses and significant biological processes, among the three subtypes, PCDcluster 3 was of particular interest as it contained a large proportion of patients with high risk and low metabolic activity. Construction and evaluation of the Nomogram model was drawn based on the multivariate logistic regression analysis, and highlighted the robustness of the Nomogram model in other independent HCC cohorts. Finally, to explore the prognostic value, we also validated the frequent upregulation of DLAT in a real-world cohort of human HCC specimens by qPCR, western blot, and immunohistochemical staining (IHC). Together, our work herein comprehensively emphasized PCD-related patterns and key regulators, such as DLAT, contributed to the evolution and prognosis of tumor foci in HCC patients, and strengthened our understanding of PCD characteristics and promoted more effective risk stratification strategies.

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Multidisciplinary consensus recommendations for management of hepatocellular carcinoma in Middle East and North Africa region

Cited by 2 publications

References 124 publications

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics

Integrated multiomics analysis identified comprehensive crosstalk between diverse programmed cell death patterns and novel molecular subtypes in Hepatocellular Carcinoma

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