Diagnostic accuracy of midkine for hepatocellular carcinoma: A meta-analysis

Alpha-fetoprotein, glypican-3, and kininogen-1 as biomarkers for the diagnosis of hepatocellular carcinoma

The hepatocarcinoma (HCC) is the most important liver tumor. It represents 90% of liver cancer cases. One of the main problems is the limited prompt cancer diagnosis and the advanced stages where the chances of treatment are limited. The main diagnostic methods for HCC are imaging techniques and liver biopsy. With advances in technology, proteins as significant diagnostic biomarkers have increased. The objective of this review is to describe the role of Alpha-fetoprotein (AFP), Glipican 3 (GPC-3), and Kininogen 1 (KNG-1) as biomarkers for the diagnosis of hepatocellular carcinoma. A systematic search of studies was carried out in the literature and the diagnostic values of these proteins were compared. The results showed that the combined use of biomarkers increases the diagnostic capacity for the detection of hepatocellular carcinoma.

Evaluation of serum midkine levels and metabolic parameters in patients with hidradenitis suppurativa

The exact pathogenesis of Hidradenitis suppurativa (HS) has not been known yet. Midkine (MK) is a heparin-binding protein that has crucial roles in many processes such as cell proliferation, cell survival, cell migration, anti-apoptotic activity, and also inflammation and angiogenesis. No data exists in the literature on the evaluation of MK in patients with HS. The present study aimed to determine the serum levels of MK and metabolic parameters in patients with HS and to compare them with healthy subjects. Forty-five patients with HS and 45 healthy controls were included. The severity of the disease was assessed with Hurley staging system in the patient group. Levels of MK, C-reactive protein (CRP), lipids, and fasting glucose were measured with the enzyme-linked immunosorbent assay (ELISA) method. MK levels were significantly higher in the patient group (p < 0.05). Moreover, smoking habit, total cholesterol, triglyceride (TG), low-density lipoprotein (LDL) and CRP were significantly higher in the patient group (for all p < 0.05). Correlation analyses revealed that MK value was correlated positively with waist circumference, TG, total cholesterol level and diastolic blood pressure (for all p < 0.05), while negatively correlated with high-density lipoprotein (HDL) level (p = 0.046, r = - 0.219). There was no difference in the MK levels according to the severity of the disease among patients. This is the first preliminary study showing that these patients with HS have higher serum MK levels, which may also be related to autoinflammation, angiogenesis, atherosclerosis and metabolic syndrome risk.

Multi-omics analysis of pyroptosis regulation patterns and characterization of tumor microenvironment in patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a primary malignant tumor of the liver, and pyroptosis has been identified as a novel cellular program that plays a role in numerous diseases including cancer. However, the functional role of pyroptosis in HCC remains unclear. The purpose of this study is to explore the relationship between the two found hub genes and provide targets for clinical treatment.

Methods

The Cancer Genome Atlas (TCGA) database was used to collect the gene data and clinically-related information of patients with HCC. After the differentially expressed genes (DEGs) were identified, they were intersected with the genes related to pyroptosis, and a risk prediction model was established to predict the overall survival (OS). Subsequently, drug sensitivity analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) was used to analyze the biological characteristics of the DEGs. Different immune cell infiltration and related pathways were analyzed, and hub genes were identified by protein-protein interaction (PPI). Finally, the expression of hub genes was verified by real-time quantitative PCR (qRT-PCR) and immunohistochemistry.

Results

We conducted a comprehensive bioinformatics analysis to investigate the molecular mechanisms of pyroptosis in hepatocellular carcinoma (HCC). A total of 8,958 differentially expressed genes were identified, and 37 differentially expressed genes were associated with pyroptosis through intersection. Moreover, we developed an OS model with excellent predictive ability and discovered the differences in biological function, drug sensitivity, and immune microenvironment between high-risk and low-risk groups. Through enrichment analysis, we found that the differentially expressed genes are related to various biological processes. Then, 10 hub genes were identified from protein-protein interaction networks. Finally, midkine (MDK) was screened from the 10 hub genes and further verified by PCR and immunohistochemistry, which revealed its high expression in HCC.

Conclusion

We have developed a reliable and consistent predictive model based on the identification of potential hub genes, which can be used to accurately forecast the prognosis of patients, thus providing direction for further clinical research and treatment.

Shedding more light on the role of Midkine in hepatocellular carcinoma: New perspectives on diagnosis and therapy

One of the most common malignant tumors is hepatocellular carcinoma (HCC). Progression of HCC mainly results from highly complex molecular and pathological pathways. Midkine (MDK) is a growth factor that impacts viability, migration, and other cell activities. Since MDK has been involved in the inflammatory responses, it has been claimed that MDK has a crucial role in HCC. MDK acts as an anti-apoptotic factor, which mediates tumor cell viability. In addition, MDK blocks anoikis to promote metastasis. There is also evidence that MDK is involved in angiogenesis. It has been shown that the application of anti-MDK approaches might be promising in the treatment of HCC. Besides, due to the elevated expression in HCC, MDK has been proposed as a biomarker in the prognosis and diagnosis of HCC. In this review, we will discuss the role of MDK in HCC. It is hoped that the development of new strategies concerning MDK-based therapies will be promising in HCC management.

Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.

Diagnostic accuracy of midkine for hepatocellular carcinoma: A meta-analysis

Background

There have been many reports on midkine as a promising marker in the diagnosis of hepatocellular carcinoma (HCC). However, the results are inconsistent and even conflicting.

Methods

This meta‐analysis was performed to investigate the accuracy of midkine in the diagnosis of HCC. Meta‐DiSc 1.4 software was used to extract data and to calculate the overall sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Data are presented as forest plots and summary receiver operating characteristic (SROC) curve analysis was used to summarize the overall test performance.

Results

Ten studies with a total of 753 HCC patients and 977 non‐HCC patients were included. The overall pooled diagnostic data were as follows: the pooled sensitivity of 0.86 (95% confidence interval [CI]: 083–0.88), the pooled specificity of 0.75 (95% CI: 0.73–0.78), the pooled PLR of 4.71 (95% CI: 2.80–7.90), the pooled NLR of 0.18 (95% CI: 0.11–0.30), and the pooled DOR of 36.83 (95% CI: 13.56–100.05). The area under curve value was 0.9266 in the overall SROC curve.

Conclusion

Midkine has moderate diagnostic accuracy for HCC. Due to the design limitations, results inpublished studies should be carefully interpreted. In addition, more well‐designed studies with large sample sizes should be performed to rigorously evaluate the diagnostic accuracy of the MDK.