Early Diagnosis Of Liver Cancer Research Articles

Nanoparticles and their application in the diagnosis of hepatocellular carcinoma

Abstract Most patients are at advanced stages when they are diagnosed with hepatocellular carcinoma, leading to poor prognosis and a low 5-year survival rate. Serological markers, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and liver biopsy are the common clinical diagnostic techniques for liver cancer. Effective interventions in the early stage will be beneficial to improve the prognosis of liver cancer patients and reduce the global burden. Therefore, it is urgent to develop new diagnostic methods to improve the diagnosis and management of liver cancer. Nanotechnology has become a new frontier subject in medical detection along with the application of nanomaterials in the manufacture of drug carriers, diagnostic tools, and therapeutic devices. Many studies have shown that nanoparticles (NPs) can be applied to the clinical diagnosis of liver cancer in combination with existing technologies, providing a new method for the early diagnosis of liver cancer. In this review, we elaborate on the theoretical basis and characteristics of NPs in the diagnosis of liver cancer, and the research progress and prospects of NPs in the diagnosis of liver cancer are summarized.

Pump-free SERS microfluidic chip based on an identification-competition strategy for ultrasensitive and efficient simultaneous detection of liver cancer-related microRNAs

In this study, we present a pump-free SERS microfluidic chip capable of detecting liver cancer-related miR-21 and miR-155 concurrently with ultra-sensitivity and high efficiency. We employed a Fe3O4@cDNA-AuNPs@Raman reporter@H composite structure and a recognition competition strategy. When the target miRNAs (miR-21 and miR-155) are present in the test liquid, they specifically compete with the nucleic acid complementary strand(H) of Fe3O4@cDNA-AuNPs@Raman reporter@H, causing AuNPs to competitively detach from the surface of Fe3O4, resulting in a decrease in the SERS signal. Consequently, this pump-free SERS microfluidic chip enables the detection of the target miRNAs more rapidly and accurately in complex environments. This method offers an approach for the simultaneous and efficient detection of miRNAs and holds promising applications in the early diagnosis of liver cancer.

Signal-Boosted Electrochemical Lateral Flow Immunoassay for Early Point-of-Care Detection of Liver Cancer Biomarker.

The early diagnosis of cancer in a point-of-need manner is of great significance, yet it remains challenging to achieve the necessary sensitivity and speed. Traditional lateral flow immunoassay (LFIA) methods are limited in accuracy and quantification, restricting their suitability for home-based applications. Thus, we explored a new and user-friendly electrochemical LFIA (e-LFIA) test strip to detect α-fetoprotein (AFP), a diagnostic marker for liver cancer. The specific electrochemical immunoprobe utilized in this e-LFIA test strip is characterized by significant signal boosting, resulted from the loading Ag shell into a gold nanoparticle (AuNP)-coated dendritic mesoporous silica nanoscaffold (DMSN). Leveraging the distinct electrochemical characteristics of Ag anodic stripping and the high volume-to-surface area ratio of DMSNs, the developed DMSNs/AuNPs@Ag-based e-LFIA test strip is capable of detecting AFP at a low concentration of 0.85 ng/mL within a rapid 20 min timespan, both of these values are smaller than those in current clinical testing. Furthermore, we utilized homemade screen-printed electrodes in this sensing prototype and demonstrated the high versatility and reliability of this e-LFIA device. We envision that this DMSNs/AuNPs@Ag-based e-LFIA holds substantial potential for the early diagnosis of liver cancer and household health monitoring.

Natural killer (NK) cells-related gene signature reveals the immune environment heterogeneity in hepatocellular carcinoma based on single cell analysis

The early diagnosis of liver cancer is crucial for the treatment and depends on the coordinated use of several test procedures. Early diagnosis is crucial for precision therapy in the treatment of the hepatocellular carcinoma (HCC). Therefore, in this study, the NK cell-related gene prediction model was used to provide the basis for precision therapy at the gene level and a novel basis for the treatment of patients with liver cancer. Natural killer (NK) cells have innate abilities to recognize and destroy tumor cells and thus play a crucial function as the “innate counterpart” of cytotoxic T cells. The natural killer (NK) cells is well recognized as a prospective approach for tumor immunotherapy in treating patients with HCC. In this research, we used publicly available databases to collect bioinformatics data of scRNA-seq and RNA-seq from HCC patients. To determine the NK cell-related genes (NKRGs)-based risk profile for HCC, we isolated T and natural killer (NK) cells and subjected them to analysis. Uniform Manifold Approximation and Projection plots were created to show the degree of expression of each marker gene and the distribution of distinct clusters. The connection between the immunotherapy response and the NKRGs-based signature was further analyzed, and the NKRGs-based signature was established. Eventually, a nomogram was developed using the model and clinical features to precisely predict the likelihood of survival. The prognosis of HCC can be accurately predicted using the NKRGs-based prognostic signature, and thorough characterization of the NKRGs signature of HCC may help to interpret the response of HCC to immunotherapy and propose a novel tumor treatment perspective.

DESMPA-Deep CNN: Double Exponential Smoothing Marine Predator Algorithm-Based Deep CNN for Liver Tumor Segmentation and Classification

Nowadays, liver cancers are well-known cancers attacking the human body. The larger segment of liver carcinomas is highly affected by alcohol-associated hepatitis as well as cirrhosis circumstances. In order to increase the survival rate of humans, premature and early diagnosis of liver cancer is very vital. Currently, discriminating the tumor and liver segments from medical imaging with the help of entirely automated computer-aided software is a highly difficult task, because the liver tumor may differ from one human being to another human being. Thus, this paper endeavors to present a new liver tumor segmentation and classification approach by incorporating an optimization-enabled deep learning model. Here, the adopted model comprises four stages, pre-processing, liver segmentation, liver tumor detection, and classification. To begin with, the collected images are given to the pre-processing stage using the African vulture’s optimization-based adaptive histogram equalization (AVOAHE). The integration of the African vulture’s optimization (AVO) concept in adaptive histogram equalization is named AVOAHE. After the image pre-processing phase, the liver region is segmented by utilizing kernel-based fuzzy C-means clustering. Then, the detection of liver tumor is performed using the Swin-Unet. Finally, tumor detection is done by utilizing the deep convolutional neural network (Deep CNN). Moreover, a hybrid optimization approach, called double exponential smoothing (DES)-marine predator algorithm (MPA), is developed by combining DES and MPA, which is used to train Deep CNN and serves as an enhancement. Finally, the experimental analysis has shown that the adopted technique has achieved better results in terms of accuracy, specificity, sensitivity, and precision with values of 0.957, 0.969, 0.941 and 0.915, respectively.

A DNA-peptide probe coupled mass spectrometry-based method for the detection of LncRNA in hepatocyte and serum samples

Liver cancer, a prominent contributor to cancer-related deaths, necessitates timely identification for successful intervention. In recent years, long non-coding RNAs (lncRNAs) have gained attention as potential biomarkers for liver cancer, and accurate quantification of lncRNA is essential for early diagnosis of liver cancer. However, existing methods for lncRNA detection rely on amplification and labeling techniques, resulting in semi-quantitative or qualitative outcomes. DNA-peptide technology offers a promising alternative. It has successfully translated nucleic acid quantification into peptide quantification, particularly in the context of miRNA analysis. However, the intricate structure of long-chain lncRNAs presents a challenge not encountered with miRNAs, making the design and acquisition of specific DNA-peptide probes for direct quantification more difficult. In this study, a strategy for lncRNA quantification was developed using a DNA-peptide probe coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based quasi-targeted proteomics. The inherent structural complexity of long-chain lncRNAs was overcome by designing DNA-peptide probes based on their unique sequence segments, enabling direct quantification without the need for amplification. Highly upregulated in liver cancer (HULC) was identified as a target lncRNA through bioinformatics analysis using GEO2R online software. Subsequently, the target lncRNA (i.e., HULC), which had been biotinylated and bound to streptavidin agarose beforehand, was hybridized with the probe. Following trypsin digestion, the reporter peptide was released and quantified using LC-MS/MS. Finally, this method was applied to analyze HULC levels in liver cancer cell lines and serum samples, enabling quantitative assessment and early diagnosis. The results demonstrated that liver cancer diagnostics could be enhanced through the integration of mass spectrometry and DNA-peptide probe technology for lncRNA quantification.

Integrated policy of medical expense subsidies and clinical registry for patients with liver cancer and decompensated cirrhosis in Japan.

Chronic hepatitisB and C are among the most significant infectious diseases worldwide, and are major risk factors for liver cirrhosis and liver cancer. In Japan, comprehensive hepatitis measures are implemented for the testing and treatment of viral hepatitis, thus enabling the early diagnosis of liver cancer. Nevertheless, patients with decompensated cirrhosis and liver cancer often have unfavorable prognoses and require repetitive long-term treatment. In fiscal year 2018, an integrated policy of medical expense subsidies and research was established in Japan that aimed to alleviate patients' financial burden and launch the clinical registry of advanced liver disease. Over time, updates to the eligibility for the subsidy increased access to patients and has led to an increased number of beneficiaries. Additionally, the accumulation of clinical data in the registry has revealed the treatment choices for these diseases. However, the disparities in efforts across prefectures have also become evident. Raising public awareness of the policy and tightening the multisector healthcare network are keys to success in supporting qualifying patients with advanced liver disease.

Dual-reporter fluorescent probe for precise identification of liver cancer by sequentially responding to carboxylesterase and polarity

Early treatment significantly improves the survival rate of liver cancer patients, so the development of early diagnostic methods for liver cancer is urgent. Liver cancer can develop from viral hepatitis, alcoholic liver, and fatty liver, thus making the above diseases share common features such as elevated viscosity, reactive oxygen species, and reactive nitrogen species. Therefore, accurate differentiation between other liver diseases and liver cancer is both a paramount practical need and challenging. Numerous fluorescent probes have been reported for the diagnosis of liver cancer by detecting a single biomarker, but these probes lack specificity for liver cancer in complex biological systems. Obviously, using multiple liver cancer biomarkers as the basis for judgment can dramatically improve diagnostic accuracy. Herein, we report the first fluorescent probe, LD-TCE, that sequentially detects carboxylesterase (CE) and lipid droplet polarity in liver cancer cells with high sensitivity and selectivity, with linear detection of CE in the range of 0–6 U/mL and a 65-fold fluorescence enhancement in response to polarity. The probe first reacts with CE and releases weak fluorescence, which is then dramatically enhanced due to the decrease in lipid droplet polarity in liver cancer cells. This approach allows the probe to enable specific imaging of liver cancer with higher contrast and accuracy. The probe successfully achieved the screening of liver cancer cells and the precise identification of liver cancer in mice. More importantly, it is not disturbed by liver fibrosis, which is a common pathological feature of many liver diseases. We believe that the LD-TCE is expected to be a powerful tool for early diagnosis of liver cancer.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma.

The prevention and early diagnosis of liver cancer remains a global medical challenge. During the malignant transformation of hepatocytes, a variety of oncogenic cellular signalling molecules, such as novel high mobility group-Box 3, angiopoietin-2, Golgi protein 73, glypican-3, Wnt3a (a signalling molecule in the Wnt/β-catenin pathway), and secretory clusterin, can be expressed and secreted into the blood. These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy. This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

Utilization of Biopsy-Guided CT Scan in Diagnosing Liver Cancer: A Case Study

Introduction: Liver cancer is one of the most common types of cancer in Indonesia and has a high mortality rate. Early diagnosis of liver cancer is very important to increase the patient's chances of recovery. Biopsy-guided CT scan is an effective method for diagnosing liver cancer.
 Case presentation: We report the case of a 55 year old man with a history of chronic hepatitis B who presented with complaints of right upper abdominal pain and weight loss. Physical examination revealed hepatomegaly and ascites. Investigations, including abdominal ultrasound and liver function tests, showed a mass in the liver. CT scan of the abdomen with contrast showed a hypodense mass in the right hepatic lobe. A CT-guided liver biopsy was performed and the histopathological diagnosis was hepatocellular carcinoma (HCC). The patient then underwent partial resection hepatectomy and chemotherapy.
 Conclusion: Biopsy-guided CT scan is a valuable tool for the diagnosis of HCC in patients with chronic hepatitis B.

A compact microfluidic laser-induced fluorescence immunoassay system using avalanche photodiode for rapid detection of alpha-fetoprotein.

Alpha-fetoprotein (AFP), commonly employed for early diagnosis of liver cancer, serves as a biomarker for cancer screening and diagnosis. Combining the high sensitivity and specificity of fluorescence immunoassay (FIA), developing a low-cost and efficient immunoassay system for AFP detection holds significant importance in disease diagnosis. In this work, we developed a miniaturized oblique laser-induced fluorescence (LIF) immunoassay system, coupled with a microfluidic PMMA/paper hybrid chip, for rapid detection of AFP. The system employed an avalanche photodiode (APD) as the detector, and implemented multi-level filtering in the excitation light channel using the dichroic mirror and optical trap. At first, we employed the Savitzky-Golay filter and baseline off-set elimination methods to denoise and normalize the original data. Then the cutoff frequency of the low-pass filter and the reverse voltage of the APD were optimized to enhance the detection sensitivity of the system. Furthermore, the effect of laser power on the fluorescence excitation efficiency was investigated, and the sampling time during the scanning process was optimized. Finally, a four-parameter logistic (4PL) model was utilized to establish the concentration-response equation for AFP. The system was capable of detecting concentrations of AFP standard solution within the range of 1-500 ng/mL, with a detection limit of 0.8 ng/mL. The entire immunoassay process could be completed within 15 min. It has an excellent potential for applications in low-cost portable diagnostic instruments for the rapid detection of biomarkers.

Factors affecting the compliance of hepatocellular carcinoma screening among high-risk populations: A systematic review and meta-analysis.

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer mortality. HCC has high morbidity, high mortality, and low survival rates. Screening is one of the most significant methods of lowering incidence and death while also increasing survival. The aim of this study was to identify the facilitators and barriers to participation in HCC screening among high-risk populations. A comprehensive and systematic search was undertaken in PubMed, Web of Science, MEDLINE, EMBACE, EBSCOhost and the Cochrane Library. A combination of synonyms of the keywords including HCC, screening, factors and adherence were used for searching. Studies addressing the facilitators and barriers to HCC screening compliance in at-risk individuals were included. Data were synthesized using Review Manager version 5.4. A random/fixed effects model meta-analysis was performed to estimate the pooled data and expressed with odds ratio (OR) and 95% confidence interval (CI). A total of seven articles met the inclusion criteria. Qualitative (n=1) and quantitative (n=6) studies using various types of surgery were conducted. The most commonly mentioned barriers were insufficient knowledge and awareness of HCC screening, unawareness of the necessity for early detection of HCC and lack of physician recommendation. A meta-analysis of seven studies showed that individuals with a family history of HCC increased screening uptake by nearly three times (OR: 2.69, 95% CI: 1.93, 3.75). Other most frequently reported facilitators include age, education level, and perceived risk etal. Many barriers to HCC screening were found. Meanwhile, this review points out that improving the awareness of high-risk populations toward HCC screening is expected to enhance compliance, thereby promoting early diagnosis of liver cancer, reducing mortality, and alleviating the burden of HCC.

Use of UMFNPs/Ce6@MBs in multimodal imaging-guided sono-photodynamic combination therapy for hepatocellular carcinoma.

Early diagnosis of liver cancer and appropriate treatment options are critical for obtaining a good prognosis. However, due to technical limitations, it is difficult to make an early and accurate diagnosis of liver cancer, and the traditional imaging model is relatively simple. Therefore, we synthesized multifunctional diagnostic/therapeutic nanoparticles, UMFNPs/Ce6@MBs, loaded with ultra-small manganese ferrite nanoparticles (UMFNPs) and chlorin e6 (Ce6). This nanoplatform can take full advantage of hypoxia, acidic pH (acidosis) and increased levels of reactive oxygen species (e.g. H2O2) in the tumor microenvironment (TME). Specific imaging and drug release can also enhance tumor therapy by modulating the hypoxic state of the TME to achieve the combined effect of sonodynamic therapy and photodynamic therapy (SPDT). In addition, the prepared UMFNPs/Ce6@MBs have H2O2 and pH-sensitive biodegradability and can release UMFNPs and photosensitizer Ce6 in the TME while producing O2 and Mn2+. The obtained Mn2+ ion nanoparticles can be used for T1 magnetic resonance imaging of tumor-bearing mice, and the released Ce6 can provide fluorescence imaging function at the same time. Because UMFNPs/Ce6@MB ultrasonic microbubbles show good ultrasonic imaging results, UMFNPs/Ce6@MBs can simultaneously provide multi-modal imaging functions for magnetic resonance imaging (MRI), ultrasound and fluorescence imaging. In conclusion, UMFNPs/Ce6@MBs realize the synergistic treatment of SDT and PDT under multi-mode near-infrared fluorescence imaging and CEUS monitoring, demonstrating its great potential in tumor precision medicine.

Methylene blue based near-infrared fluorogenic probe for lighting up esterase activity and its application in biological imaging

In this work, a light-up near-infrared (NIR) fluorescence probe based on methylene blue was developed for the determination of esterase. Probe MB-Ace emits a significant NIR fluorescence at 691 nm while incubated with esterase, which facilitates us to accurately detect the activity of esterase in vitro and vivo. MB-Ace showed high sensitivity (with a detection limit of 5.2 ng/mL) and selectivity to esterase under the interference of various biomolecules. The results of biological imaging experiments showed that MB-Ace had cell membrane permeability and could be used to monitor esterase activity in Hep G2 cells. At the same time, MB-Ace also showed excellent imaging ability for esterase activity in a mice model. The above results reflect the application value of MB-Ace in the early diagnosis of liver cancer and image-guided tumor resection.

MS-FANet: Multi-scale feature attention network for liver tumor segmentation

Accurate segmentation of liver tumors is a prerequisite for early diagnosis of liver cancer. Segmentation networks extract features continuously at the same scale, which cannot adapt to the variation of liver tumor volume in computed tomography (CT). Hence, a multi-scale feature attention network (MS-FANet) for liver tumor segmentation is proposed in this paper. The novel residual attention (RA) block and multi-scale atrous downsampling (MAD) are introduced in the encoder of MS-FANet to sufficiently learn variable tumor features and extract tumor features at different scales simultaneously. The dual-path feature (DF) filter and dense upsampling (DU) are introduced in the feature reduction process to reduce effective features for the accurate segmentation of liver tumors. On the public LiTS dataset and 3DIRCADb dataset, MS-FANet achieved 74.2% and 78.0% of average Dice, respectively, outperforming most state-of-the-art networks, this strongly proves the excellent liver tumor segmentation performance and the ability to learn features at different scales.

FRI-323 - Exploration of a holistic management procedure for liver cancer surveillance to improve the early diagnosis of liver cancer in Chinese population

FRI-323 - Exploration of a holistic management procedure for liver cancer surveillance to improve the early diagnosis of liver cancer in Chinese population

Highly sensitive paper-based immunoassay: Combining nanomaterials modified cellulose with covalent and oriented immobilization of antibody

Among the analytical tools, paper-based analytical devices (PADs) have become a leading alternative for point-of care testing (POCT). In this study, PADs were fabricated using an office laser printer. Then, the paper zone was modified with graphene oxide (GO) and pyrene derivatives, which provide a sufficient amount of carboxylic groups for conjugating antibodies. At an optimal pH, antibodies were covalently bound onto carboxylated cellulose surface in an oriented manner through a two-step strategy: electrostatic adsorption was followed by EDC/NHS coupling. α-fetoprotein (AFP) as a detection model, we compared with cellulose powder modified and unmodified paper zone. The results showed the color intensity and color uniformity on GO modified paper was improved. The activity of immobilized antibodies on GO/1-pyrenebutyric acid (GO/PBA) modified was three times higher than that of GO modified and about 1.8-fold higher than that of GO/1-pyrenecarboxylic acid (GO/PCA) modified. The GO/PBA modified paper-based immunoassay has enhanced sensitivity and low detection limit. A linear correlation between color intensity and concentration of AFP in the range of 0.01～16.5 ng mL−1 with a detection limit of 9.0 pg mL−1 were achieved, respectively. The obtained results point towards rapid, sensitive, and specific early diagnosis of liver cancer at the point of care and other low-resource settings.

Identification of Liver Cancer Driver Mutations from COSMIC Data

Background: Liver cancer accounts for more than 700,000 deaths each year making it the third leading cause of cancer-related deaths worldwide. Late diagnosis of the disease is the reason behind most deaths. Driver mutations are genetic alterations in tumor cells, which are responsible for the development of liver cancer; therefore, the identification of genetic biomarkers is necessary for the prediction and early diagnosis of liver cancer. Objectives: The main objective of this study is to identify pathogenic alleles that may act as potential biomarkers for the prediction of liver cancer. It also identifies the role of novel genes in liver cancer that are not known to cause the disease. Methods: The mutation data of non-coding variants were downloaded from the catalogue of somatic mutations in cancer (COSMIC) databases. Different bioinformatics tools were, then, used to retrieve mutations in liver cancer. The genetic alterations in hepatocellular carcinoma (HCC) were analyzed. Results: The present study successfully identified pathogenic alleles (consistent mutations) along with a set of novel genes that might be involved in the development of liver cancer. It identified non-coding mutations near human genes and transcription factor binding sites of HepG2 cells. This study also identified mutations near the genes that are involved in the Ras/MAFK signaling pathway of the Hepatitis B virus. Conclusions: The pathogenic alleles identified in this study may provide targeted therapy for the treatment of liver cancer. The identification of novel genes may help to understand the progression of liver cancer at the molecular level. The identified driver mutations may act as potential biomarkers and therapeutic targets for early prediction and treatment of liver cancer.

Evaluation and Application of Drug Resistance by Biomarkers in the Clinical Treatment of Liver Cancer.

Liver cancer is one of the most lethal cancers in the world, mainly owing to the lack of effective means for early monitoring and treatment. Accordingly, there is considerable research interest in various clinically applicable methods for addressing these unmet needs. At present, the most commonly used biomarker for the early diagnosis of liver cancer is alpha-fetoprotein (AFP), but AFP is sensitive to interference from other factors and cannot really be used as the basis for determining liver cancer. Treatment options in addition to liver surgery (resection, transplantation) include radiation therapy, chemotherapy, and targeted therapy. However, even more expensive targeted drug therapies have a limited impact on the clinical outcome of liver cancer. One of the big reasons is the rapid emergence of drug resistance. Therefore, in addition to finding effective biomarkers for early diagnosis, an important focus of current discussions is on how to effectively adjust and select drug strategies and guidelines for the treatment of liver cancer patients. In this review, we bring this thought process to the drug resistance problem faced by different treatment strategies, approaching it from the perspective of gene expression and molecular biology and the possibility of finding effective solutions.

Clinical Value of Growth Differentiation Factor 15 Detection in the Diagnosis of Early Liver Cancer Based on Data Mining.

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P < 0.01), there was a significant difference (P < 0.01) compared with adjacent tissue (P < 0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients.