Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer.

BACKGROUND: Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. METHODS: A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. RESULTS: Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. CONCLUSIONS: Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Bibliometric analysis of aptamer-conjugated nanoparticles for diagnosis in the last two decades.

Objective.Aptamer-conjugated nanoparticles for diagnosis have recently gained increasing attention. Here, we performed a bibliometric analysis to provide an overview of this field over the past two decades.Methods. The terms 'aptamer, nanoparticles and diagnosis' were used to search for relevant original articles published in English from 2003 to 2022 in the Web of Science database. VOSviewer and CiteSpace software were employed to analyze the development process, knowledge structure, research hotspots, and potential trends in the field of aptamer-conjugated nanoparticles for diagnosis.Results. A total of 1076 original articles were retrieved, with a rapid increase in the annual output and citation. The journal 'Biosensors and Bioelectronics' has contributed the most in this field, and the most influential researcher, institution and country were Weihong Tan, the Chinese Academy of Sciences, China, respectively. Gold nanoparticles and quantum dots were the most used, but in the past three years, research hotspots focused on carbon dots and graphene quantum dots. Diagnostic directions primarily focused on cancer. The most used strategy was label-free electrochemical detection, but in the past two years, colorimetric analysis and fluorescence imaging emerged as hot topics.Conclusion.The bibliometric analysis reveals a rapid increase in the research on aptamer-conjugated nanoparticles for diagnosis, major contributors at the levels of journals, authors, institutions, and countries, and research preferences in diagnostic objects, nanoparticle types, and detection methods, as well as the evolution of research hotspots and future trends.

Data-driven decision-making for precision diagnosis of digestive diseases.

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

A novel four-gene signature for predicting the prognosis of hepatocellular carcinoma.

OBJECTIVE: To identify and utilize gene signatures for the prognostic evaluation of postoperative patients with hepatocellular carcinoma (HCC). METHODS: The gene mRNA expression profiles and corresponding clinicopathological data of postoperative patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Highly differentially expressed genes (DEGs) in tumor tissues compared to adjacent tissues were identified, and their associations with the overall survival (OS) of HCC patients were analyzed. The strongly associated genes were used to develop a prognostic score for the survival stratification of HCC, and the underlying mechanisms were analyzed using bioinformatics. RESULTS: A total of 376 DEGs were identified and four DEGs (ADH4, COL15A1, RET and KCNJ16) were independently associated with OS. A prognostic score derived from the four genes could effectively stratify HCC patients with different OS outcomes, independent of clinical parameters. Patients with high scores exhibited poorer OS than patients with low scores (HR 5.526, 95% CI: 2.451-12.461, p < .001). The four genes were involved in cancer-related biological processes and were independent of each other in bioinformatics analyses. CONCLUSION: Four genes strongly associated with the prognosis of postoperative patients with HCC were identified, and the derived prognostic score was simple and valuable for overall survival prediction.

Blood-derived molecular signatures as biomarker panels for the early detection of colorectal cancer.

Colorectal cancer (CRC) is one of the leading causes of tumor morbidity and mortality worldwide. Endoscopy is currently the main screening method, but the invasiveness and high cost hamper the application of endoscopy in asymptomatic patients with a risk of CRC and lead to a low diagnostic rate for early CRC. In recent years, the progress of transcriptomics, epigenetics, immunomics and metabolomics has greatly contributed to the identification of novel molecular markers for the noninvasive screening of CRC, and many molecules in various biological processes have been identified and evaluated for CRC detection. However, individual molecules always have insufficient diagnostic performance as biomarkers for the detection of CRC; therefore, a frequent strategy to overcome this deficiency is the use of molecule signatures as biomarker panels to improve the diagnostic power. Here, we reviewed the diagnostic performance of blood-derived molecular signatures (mRNAs, microRNAs, autoantibodies, and metabolites) as biomarker panels for CRC detection, particularly for early detection, and discussed their limitations and prospects.

Pretreatment risk management of a novel nomogram model for prediction of thoracoabdominal extrahepatic metastasis in primary hepatic carcinoma.

BACKGROUND: Extrahepatic metastasis is the independent risk factor of poor survival of primary hepatic carcinoma (PHC), and most occurs in the chest and abdomen. Currently, there is still no available method to predict thoracoabdominal extrahepatic metastasis in PHC. In this study, a novel nomogram model was developed and validated for prediction of thoracoabdominal extrahepatic metastasis in PHC, thereby conducted individualized risk management for pretreatment different risk population. METHODS: The nomogram model was developed in a primary study that consisted of 330 consecutive pretreatment patients with PHC. Large-scale datasets were extracted from clinical practice. The nomogram was based on the predictors optimized by data dimension reduction through Lasso regression. The prediction performance was measured by the area under the receiver operating characteristic (AUROC), and calibrated to decrease the overfit bias. Individualized risk management was conducted by weighing the net benefit of different risk population via decision curve analysis. The prediction performance was internally and independently validated, respectively. An independent-validation study using a separate set of 107 consecutive patients. RESULTS: Four predictors from 55 high-dimensional clinical datasets, including size, portal vein tumor thrombus, infection, and carbohydrate antigen 125, were incorporated to develop a nomogram model. The nomogram demonstrated valuable prediction performance with AUROC of 0.830 (0.803 in internal-validation, and 0.773 in independent-validation, respectively), and fine calibration. Individual risk probability was visually scored. Weighing the net benefit, threshold probability was classified for three-independent risk population, which was < 19.9%, 19.9-71.8% and > 71.8%, respectively. According to this classification, pretreatment risk management was based on a treatment-flowchart for individualized clinical decision-making. CONCLUSIONS: The proposed nomogram is a useful tool for pretreatment risk management of thoracoabdominal extrahepatic metastasis in PHC for the first time, and may handily facilitate timely individualized clinical decision-making for different risk population.

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms.

Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop non-invasive methods for the early detection of gastrointestinal cancers. Biomedical optical spectroscopy, including infrared spectroscopy, Raman spectroscopy, diffuse scattering spectroscopy and autofluorescence, is capable of providing structural and chemical information about biological specimens with the advantages of non-destruction, non-invasion and reagent-free and waste-free analysis and has thus been widely investigated for the diagnosis of oesophageal, gastric and colorectal cancers. This review will introduce the advances of biomedical optical spectroscopy techniques, highlight their applications for the early detection of gastrointestinal cancers and discuss their limitations.

Noninvasive detection of gastric cancer.

Gastric cancer (GC) is the fifth most common cancer and the third common cause of cancer death worldwide. Endoscopy is the most effective method for GC screening, but its application is limited by the invasion. Therefore, continuous efforts have been made to develop noninvasive methods for GC detection and promising results have been reported. Here, we review the advances in GC detection by protein and nucleic acid tumor markers, circulating tumor cells, and tumor-associated autoantibodies in peripheral blood. Some potential new noninvasive methods for GC detection are also reviewed, including exhaled breath analysis, blood spectroscopy analysis and molecular imaging.

Risk factors for colorectal neoplasms based on colonoscopy and pathological diagnoses of Chinese citizens: a multicenter, case-control study.

PURPOSE: Since observational data in the urban residents are required to better assess the risk factors of colorectal neoplasm occurrence and the effectiveness of colonoscopy screening and surveillance, we conducted a case-control study at multicenters in China to identify patient characteristics and neoplasm features of colorectal adenoma (CRA) and colorectal carcinoma (CRC). METHODS: A total of 4089 patients who had undergone a colonoscopy from 19 hospitals were enrolled, of which 1106 had CRA and 466 had CRC. They were compared with controls. The analysis provides features and risk factors of colorectal neoplasm using multivariate logistic regression. RESULTS: Increasing age, a family history of colorectal cancer or previous cases of colorectal adenoma or hypertension disease, gastrointestinal surgery, regular intake of pickled food (adjusted odds ratio [aOR] 1.42, 95 % confidence interval [CI], 1.048-1.924), consumption of alcohol, and a positive result of fecal occult blood testing (FOBT; aOR 2.509, 95 % CI 1.485-4.237) were associated with an increased risk of CRA. In the CRC group, increasing age, regular intake of pickled foods, and a positive FOBT result were risk factors. In addition, a positive abdominal computed tomography (CT) before a colonoscopy and physical signs of emaciation were also significantly associated with an increasing risk of colorectal carcinoma. Regular intake of vegetables decreased the risk of both CRA and CRC. CONCLUSIONS: Age, pickled foods, and a positive FOBT are risk factors for colorectal neoplasm. Vegetable intake was associated with a decreased risk of CRA and CRC.

Evaluation and clinical significance of the stomach age model for evaluating aging of the stomach-a multicenter study in China.

BACKGROUND: A higher prevalence of chronic atrophic gastritis (CAG) occurs in younger adults in Asia. We used Stomach Age to examine the different mechanisms of CAG between younger adults and elderly individuals, and established a simple model of cancer risk that can be applied to CAG surveillance. METHODS: Stomach Age was determined by FISH examination of telomere length in stomach biopsies. Δψm was also determined by flow cytometry. Sixty volunteers were used to confirm the linear relationship between telomere length and age while 120 subjects were used to build a mathematical model by a multivariate analysis. Overall, 146 subjects were used to evaluate the validity of the model, and 1,007 subjects were used to evaluate the relationship between prognosis and Δage (calculated from the mathematical model). ROC curves were used to evaluate the relationship between prognosis and Δage and to determine the cut-off point for Δage. RESULTS: We established that a tight linear relationship between the telomere length and the age. The telomere length was obvious different between patients with and without CAG even in the same age. Δψm decreased in individuals whose Stomach Age was greater than real age, especially in younger adults. A mathematical model of Stomach Age (real age + Δage) was successfully constructed which was easy to apply in clinical work. A higher Δage was correlated with a worse outcome. The criterion of Δage >3.11 should be considered as the cut-off to select the subgroup of patients who require endoscopic surveillance. CONCLUSION: Variation in Stomach Age between individuals of the same biological age was confirmed. Attention should be paid to those with a greater Stomach Age, especially in younger adults. The Δage in the Simple Model can be used as a criterion to select CAG patients for gastric cancer surveillance.

Evolution and Trends of Aptamers in Cancer Research: A Bibliometric Analysis of the Literature over the Last Decade.

OBJECTIVE: Aptamers are increasingly applied in cancer research. Here, we have performed the first bibliometric analysis to demonstrate the evolution of aptamers in cancer research over the past decade and to reveal future trends. METHOD: Original articles and reviews on aptamers in cancer research published from 2013 to 2022 were retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and R software were used for bibliometric analysis of the literature and visualization of the results. RESULTS: A total of 1627 eligible publications were analyzed. Annual and cumulative publications have been found to be steadily increased. China was the most productive country (884 publications) and Hunan University was the most productive institution (97 publications). The United States had the highest level of international collaboration (betweenness centrality = 0.55). Wei-Hong Tan was the most productive author (68 publications) and Craig Tuerk was the most cited author (387 citations). Analytical Chemistry and Biosensors and Bioelectronics were the most influential journals in this field. Three major themes were identified: aptamer selection techniques, aptamer-targeted drug delivery, and aptasensors for cancer detection. The research hotspots have shifted from aptamer selection, targeted drug delivery, molecular imaging, and biomarker detection to electrochemical aptasensors and therapeutic applications. The future may focus on high specificity and affinity in aptamer selection, aptasensor fabrication, aptamer- targeted drug delivery, and therapeutic aptamer development. CONCLUSION: The field of aptamers in cancer research has been steadily developing over the past decade, and future research may focus on aptamer application in cancer detection and therapy and the improvement of aptamer selection.

The Index sAGP is Valuable for Distinguishing Atypical Hepatocellular Carcinoma from Atypical Benign Focal Hepatic Lesions.

Purpose: The differential diagnosis of atypical hepatocellular carcinoma (aHCC) and atypical benign focal hepatic lesions (aBFHL) usually depends on pathology. This study aimed to develop non-invasive approaches based on conventional blood indicators for the differential diagnosis of aHCC and aBFHL. Patients and Methods: Hospitalized patients with pathologically confirmed focal hepatic lesions and their clinical data were retrospectively collected, in which patients with HCC with serum alpha-fetoprotein (AFP) levels of ≤200 ng/mL and atypical imaging features were designated as the aHCC group (n = 224), and patients with benign focal hepatic lesions without typical imaging features were designated as the aBFHL group (n = 178). The performance of indexes (both previously reported and newly constructed) derived from conventional blood indicators by four mathematical operations in distinguishing aHCC and aBFHL was evaluated using the receiver operating characteristic (ROC) curve and diagnostic validity metrics. Results: Among ten previously reported derived indexes related to HCC, the index GPR, the ratio of γ-glutamyltransferase (GGT) to platelet (PLT), showed the best performance in distinguishing aHCC from aBFHL with the area under ROC curve (AUROC) of 0.853 (95% CI 0.814-0.892), but the other indexes were of little value (AUROCs from 0.531 to 0.700). A new derived index, sAGP [(standardized AFP + standardized GGT)/standardized PLT], was developed and exhibited AUROCs of 0.905, 0.894, 0.891, 0.925, and 0.862 in differentiating overall, BCLC stage 0/A, TNM stage I, small, and AFP-negative aHCC from aBFHL, respectively. Conclusion: The sAGP index is an efficient, simple, and practical metric for the non-invasive differentiation of aHCC from aBFHL.

Single-primer-limited amplification: a method to generate random single-stranded DNA sub-library for aptamer selection.

The amplification of a random single-stranded DNA (ssDNA) library by polymerase chain reaction (PCR) is a key step in each round of aptamer selection by systematic evolution of ligands by exponential enrichment (SELEX), but it can be impeded by the amplification of by-products due to the severely nonspecific hybridizations among various sequences in the PCR system. To amplify a random ssDNA library free from by-products, we developed a novel method termed single-primer-limited amplification (SPLA), which was initiated from the amplification of minus-stranded DNA (msDNA) of an ssDNA library with reverse primer limited to 5-fold molar quantity of the template, followed by the amplification of plus-stranded DNA (psDNA) of the msDNA with forward primer limited to 10-fold molar quantity of the template and recovery of psDNA by gel excision. We found that the amount of by-products increased with the increase of template amount and thermal cycle number. With the optimized template amount and thermal cycle, SPLA could amplify target ssDNA without detectable by-products and nonspecific products and could produce psDNA 16.1 times as much as that by asymmetric PCR. In conclusion, SPLA is a simple and feasible method to efficiently generate a random ssDNA sub-library for aptamer selection.

Combined use of AFP, CEA, CA125 and CAl9-9 improves the sensitivity for the diagnosis of gastric cancer.

BACKGROUND: The detection of serum tumor marker becomes a common method for screening tumors. However, this method has not been widely used for routine gastric cancer screening. In this study we aimed to determine whether the combined use of tumor markers may increase the sensitivity for the diagnosis of gastric cancer. METHODS: Serum AFP, CEA, CA125 and CA19-9 levels were measured in 149 patients with gastric cancer, 111 patients with benign gastric diseases and 124 healthy people, who visited the First Affiliated Hospital of Nanchang University from May 2011 to May 2012. Statistical analysis including receiver operating characteristic (ROC) curve, the area under the curve (AUC), and logistic regression analysis was performed to evaluate the diagnostic value of these markers on gastric cancer. RESULTS: Serum levels of CEA, CA125, and CA19-9 in gastric cancer group were higher than that in the benign gastric disease group and the healthy control group (P <0.005). The sensitivity of AFP, CEA, CA125 and CA19-9 in the diagnosis of gastric cancer was 4.7-20.8% individually, and increased to 40.3% in combination. By using optimal cut-off value, the sensitivity of CEA, CA125, and CA19-9 for the diagnosis of gastric cancer was improved. Especially, the sensitivity of CEA increased to 58.4% and the sensitivity of combined use of four markers increased to 69.1%. The age and gender had no effects on the diagnostic value of these markers. CONCLUSIONS: The determination and application of optimal cut-off values based on ROC curve and logistic regression analysis could improve the diagnosis of gastric cancer based on common tumor markers.

Combination of multiple nucleic acid aptamers for precision detection of tumors based on optical methods.

BACKGROUND AND PURPOSE: Nucleic acid aptamers are a novel molecular recognition tool that is functionally similar to antibodies but superior to antibodies in terms of thermal stability, structural modification, preparation, and cost, and therefore hold great promise for molecular detection. However, due to the limitations of a single aptamer in molecular detection, the multiple aptamer combination for bioanalysis has received much attention. Here, we reviewed the progress of tumor precision detection based on the combination of multiple nucleic acid aptamers and optical methods and discussed its challenges and prospects. METHODS: The relevant literature in PubMed was collected and reviewed. RESULTS: The combination of two or more aptamers with modern nanomaterials and analytical methods allows the fabrication of various detection systems for the simultaneous detection of different structural domains of a substance and/or different substances, including soluble tumor markers, tumor cell surface and intracellular markers, circulating tumor cells, and other tumor-related biomolecules, which has great potential for application in efficient and precise tumor detection. CONCLUSION: The combination of multiple nucleic acid aptamers provides a new approach for the precise detection of tumors and will play an important role in precision medicine for tumors.

Analysis of aptamer-target binding and molecular mechanisms by thermofluorimetric analysis and molecular dynamics simulation.

Introduction: Aptamers are valuable for bioassays, but aptamer-target binding is susceptible to reaction conditions. In this study, we combined thermofluorimetric analysis (TFA) and molecular dynamics (MD) simulations to optimize aptamer-target binding, explore underlying mechanisms and select preferred aptamer. Methods: Alpha-fetoprotein (AFP) aptamer AP273 (as the model) was incubated with AFP under various experimental conditions, and melting curves were measured in a real-time PCR system to select the optimal binding conditions. The intermolecular interactions of AP273-AFP were analysed by MD simulations with these conditions to reveal the underlying mechanisms. A comparative study between AP273 and control aptamer AP-L3-4 was performed to validate the value of combined TFA and MD simulation in selecting preferred aptamers. Results: The optimal aptamer concentration and buffer system were easily determined from the dF/dT peak characteristics and the melting temperature (Tm) values on the melting curves of related TFA experiments, respectively. A high Tm value was found in TFA experiments performed in buffer systems with low metal ion strength. The molecular docking and MD simulation analyses revealed the underlying mechanisms of the TFA results, i.e., the binding force and stability of AP273 to AFP were affected by the number of binding sites, frequency and distance of hydrogen bonds, and binding free energies; these factors varied in different buffer and metal ion conditions. The comparative study showed that AP273 was superior to the homologous aptamer AP-L3-4. Conclusion: Combining TFA and MD simulation is efficient for optimizing the reaction conditions, exploring underlying mechanisms, and selecting aptamers in aptamer-target bioassays.

Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on spatial and channel attention mechanisms.

BACKGROUND: The pre-operative non-invasive differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) mainly depends on imaging. However, the accuracy of conventional imaging and radiomics methods in differentiating between the two carcinomas is unsatisfactory. In this study, we aimed to establish a novel deep learning model based on computed tomography (CT) images to provide an effective and non-invasive pre-operative differential diagnosis method for HCC and ICC. MATERIALS AND METHODS: We retrospectively investigated the CT images of 395 HCC patients and 99 ICC patients who were diagnosed based on pathological analysis. To differentiate between HCC and ICC we developed a deep learning model called CSAM-Net based on channel and spatial attention mechanisms. We compared the proposed CSAM-Net with conventional radiomic models such as conventional logistic regression, least absolute shrinkage and selection operator regression, support vector machine, and random forest models. RESULTS: With respect to differentiating between HCC and ICC, the CSAM-Net model showed area under the receiver operating characteristic curve (AUC) values of 0.987 (accuracy = 0.939), 0.969 (accuracy = 0.914), and 0.959 (accuracy = 0.912) for the training, validation, and test sets, respectively, which were significantly higher than those of the conventional radiomics models (0.736-0.913 [accuracy = 0.735-0.912], 0.602-0.828 [accuracy = 0.647-0.818], and 0.638-0.845 [accuracy = 0.618-0.849], respectively. The decision curve analysis showed a high net benefit of the CSAM-Net model, which suggests potential efficacy in differentiating between HCC and ICC in the diagnosis of liver cancers. CONCLUSIONS: The proposed CSAM-Net model based on channel and spatial attention mechanisms provides an effective and non-invasive tool for the differential diagnosis of HCC and ICC on CT images, and has potential applications in diagnosis of liver cancers.

New indexes derived from routine blood tests and their clinical application in hepatocellular carcinoma.

Considerable efforts have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), but the prognosis of patients with HCC remains poor. The development of officious and easy-to-use indicators that are applicable to all levels of hospitals for the diagnosis, prognosis and risk prediction of HCC may play an important role in improving the current undesirable situation. The occurrence of HCC can cause a series of local and systemic changes, involving liver function, inflammation, immunity, and nutrition, which can be reflected in routine clinical indicators, especially laboratory metrics. A comprehensive analysis of these routine indicators is capable of providing important information for the clinical management of HCC. Routine clinical indicators are daily medical data that are readily available, easily repeatable, and highly acceptable, which has attracted clinicians to derive a number of comprehensive indexes from routine clinical indicators by means of four arithmetic operations, scoring system, and mathematical modeling. These indexes integrate several clinical indicators into a new single indicator that performs better than any of original individual indicators in the risk prediction, clinical diagnosis and prognostic evaluation of HCC and is easy to use. Herein, we reviewed recent indexes derived from routine clinical indicators for the diagnosis, prognosis and risk prediction of HCC.

Aptamer-Based Triple Serum Fluorescence Intensity Assay: A Novel and Feasible Method for the Clinical Diagnosis of Primary Hepatic Carcinoma.

Background and Aims: Aptamers are artificial ligands that bind to biological targets with high specificity and affinity. We previously selected a group of aptamers against the serum of primary hepatic carcinoma (PHC) via systematic evolution of ligands by exponential and enrichment (SELEX) method, and some of the aptamers were valuable for PHC diagnosis in polyacrylamide gel electrophoresis (PAGE) analysis. Here, we used aptamers to develop a novel method suitable for the clinical diagnosis of PHC. Methods: The intensities of serum autofluorescence, cell-free DNA (cfDNA)-related fluorescence and aptamer-related fluorescence, named the aptamer-based triple serum fluorescence intensity (ATSFI), were sequentially measured at 8 °C and 37 °C in one tube by using a real-time polymerase chain reaction (PCR) system as a fluorimeter in patients with PHC (n=346) or liver cirrhosis (n=321). The diagnostic performances of ATSFI indicators alone and in combination were evaluated by area under the receiver operator characteristic curve (AUROC), and the underlying clinical mechanisms were analyzed by bivariate correlation. Results: The measurement of ATSFI was high throughput, rapid, convenient, and low cost. The aptamer-related fluorescence indicator SEA-SE37 was the most valuable for PHC diagnosis among all fluorescence indicators and superior to alpha-fetoprotein (AFP) (AUROC 0.879 vs. 0.836). The logistic model of ATSFI indicators exhibited excellent diagnostic performance for PHC, including AFP-negative, early and small PHCs, with AUROCs of 0.935-0.950 and accuracies of 86.8-88.3%. The diagnostic performance was further improved when ATSFI indicators were combined with AFP, with AUROCs of approximately 0.95 and accuracies of approximately 90%, suggesting ATSFI was independent of but complementary to AFP in PHC diagnosis. ATSFI models were highly valuable in clinical decision-making. The aptamer-related fluorescence intensity was generally independent of the clinicopathological characteristics of PHC but correlated with laboratory characteristics of PHC serum. Conclusions: The ATSFI assay is a novel, robust and feasible method for the clinical diagnosis of PHC.

AFP-L3 for the diagnosis of early hepatocellular carcinoma: A meta-analysis.

BACKGROUND: The present study aimed to systematically evaluate the diagnostic value of an isoform of alpha-fetoprotein (AFP), AFP-L3, for early hepatocellular carcinoma (HCC) by a meta-analysis. METHODS: Diagnostic reports of AFP-L3% in early HCC were searched in the PubMed, Web of Science, Cochrane Library, and Embase databases up to December 2019. The retrieved literature was reviewed, and eligible articles were selected. Data were extracted from the eligible articles, and the risk of bias was evaluated according to the Quality Assessment of Diagnostic Accuracy Studies scale. Statistical analyses were conducted by MetaDiSc1.4 and RevMan5.3 software. The sensitivities, specificities, and diagnostic odds ratios were pooled. The summary receiver operating characteristic curve was drawn, and the area under the curve was calculated. RESULTS: Six studies with acceptable quality were included in the meta-analysis involving 2447 patients. No threshold effect was observed among the 6 studies, but there was obvious heterogeneity. The pooled sensitivity, specificity, and positive and negative likelihood ratios of AFP-L3% for the diagnosis of early HCC were 0.34 (95% CI 0.30-0.39, P < .0001), 0.92 (95% CI 0.91-0.93, P < .0001), 4.46 (95% CI 2.94-6.77, P = .0033), and 0.71 (95% CI 0.61-0.82, P = .0004), respectively. The diagnostic odds ratio was 6.78 (95% CI 4.02-11.44, P = .0074). The the area under the curve of the summary receiver operating characteristic was 0.755 (95% CI 0.57-0.94). CONCLUSION: AFP-L3% has high specificity but low sensitivity for diagnose early HCC, suggesting that AFP-L3% is more valuable for excluding HCC in conditions with elevated AFP than for diagnosing early HCC. In addition, a hypersensitive detection method can improve the diagnostic accuracy of AFP-L3% for early HCC.