The Diagnostic Performance of lncRNAs from Blood Specimens in Patients with Hepatocellular Carcinoma: A Meta-Analysis

Long non‑coding RNAs as diagnostic and prognostic biomarkers for colorectal cancer (Review)

Colorectal cancer (CRC) ranks as the 3rd most common cancer globally and is the 2nd leading cause of cancer-related death. Owing to the lack of specific early symptoms and the limitations of existing early diagnostic methods, most patients with CRC are diagnosed at advanced stages. To overcome these challenges, researchers have increasingly focused on molecular biomarkers, with particular interest in long non-coding RNAs (lncRNAs). These non-protein-coding RNAs, which exceed 200 nucleotides in length, play critical roles in the development and progression of CRC. The stability and detectability of lncRNAs in the circulatory system make them promising candidate biomarkers. The analysis of circulating lncRNAs in peripheral blood represents a potential option for minimally invasive diagnostic tests based on liquid biopsy samples. The present review aimed to evaluate the efficacy of lncRNAs with altered expression levels in peripheral blood as diagnostic markers for CRC. Additionally, the clinical significance of lncRNAs as prognostic markers for this disease were summarized.

Serum lnc34a is a potential prediction biomarker for bone metastasis in hepatocellular carcinoma patients

Background

Early screening and intervention therapies are crucial to improve the prognosis of hepatocellular carcinoma (HCC) patients with bone metastasis. We aimed to identify serum lncRNA as a prediction biomarker in HCC bone metastasis.

Methods

The expression levels of lnc34a in serum samples from 157 HCC patients were detected by quantitative real-time polymerase chain reaction (PCR). Univariate analysis and multivariate analysis were performed to determine statistically significant variables.

Results

Expression levels of lnc34a in serum from HCC patients with bone metastasis were significantly higher than those without bone metastasis. The high expressions of lnc34a, vascular invasion and Barcelona Clinic Liver Cancer (BCLC) stage were associated with bone metastasis by analysis. Moreover, lnc34a expression was specifically associated with bone metastasis rather than lung or lymph node metastasis in HCC.

Conclusions

High serum lnc34a expression was a independent risk factor for developing bone metastasis in HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07808-6.

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.