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.

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.

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.

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.

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.

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.

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.

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.

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.

Establishment of a risk assessment system for peptic ulcer recurrence and its value in individualized intervention.

OBJECTIVE: To investigate the establishment of a risk assessment system for peptic ulcer (PU) recurrence and implement an individualized intervention for PU patients with a moderate to high recurrence risk to reduce the recurrence of PU in patients with a moderate to high recurrence risk. METHODS: The factors for PU recurrence were collected through consulting the literature, and a risk prediction model for PU recurrence was established using the univariate binary and multivariate multinomial Logistic stepwise regression analysis. According to the model, a total of 235 PU patients were divided into patients with high, moderate and low recurrence risks. A total of 71 PU patients with moderate to high recurrence risks were selected as the study subjects, and further divided into the control group (n=35) and the experimental group (n=36). The control group was not treated with intervention, while the experimental group was treated with individualized intervention. The PU recurrence, adverse emotions and changes of pain degree were assessed in the two groups at 3, 6, 9 and 12 months after intervention. RESULTS: The univariate and multivariate Logistic regression analysis showed that drinking alcohol, smoking, chronic diseases, oral NSAIDS and depression were associated with the recurrence of PU. Individualized intervention improved the recurrence rate, anxiety, depression, pain degree and quality of life of patients with moderate to high PU recurrence risk. CONCLUSION: Drinking alcohol, smoking, chronic diseases, oral NSAIDS and depression were associated with the recurrence of PU. Individualized intervention can improve the quality of prognosis and the recurrence risk of PU in patients, which has positive clinical significance.

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.

New Blood Biomarkers for the Diagnosis of AFP-Negative Hepatocellular Carcinoma.

An early diagnosis of hepatocellular carcinoma (HCC) followed by effective treatment is currently critical for improving the prognosis and reducing the associated economic burden. Alpha-fetoprotein (AFP) is the most widely used biomarker for HCC diagnosis. Based on elevated serum AFP levels as well as typical imaging features, AFP-positive HCC (APHC) can be easily diagnosed, but AFP-negative HCC (ANHC) is not easily detected due to lack of ideal biomarkers and thus mainly reliance on imaging. Imaging for the diagnosis of ANHC is probably insufficient in sensitivity and/or specificity because most ANHC tumors are small and early-stage HCC, and it is involved in sophisticated techniques and high costs. Moreover, ANHC accounts for nearly half of HCC and exhibits a better prognosis compared with APHC. Therefore, the diagnosis of ANHC in clinical practice has been a critical issue for the early treatment and prognosis improvement of HCC. In recent years, tremendous efforts have been made to discover new biomarkers complementary to AFP for HCC diagnosis. In this review, we systematically review and discuss the recent advances of blood biomarkers for HCC diagnosis, including DNA biomarkers, RNA biomarkers, protein biomarkers, and conventional laboratory metrics, focusing on their diagnostic evaluation alone and in combination, in particular on their diagnostic performance for ANHC.

Cytoskeleton-associated membrane protein 4 is upregulated in tumor tissues and is associated with clinicopathological characteristics and prognosis in hepatocellular carcinoma.

The role of cytoskeleton-associated membrane protein 4 (CKAP4) in hepatocellular carcinoma (HCC) is controversial. The present study aimed to investigate the association between tumor CKAP4 mRNA expression and clinicopathological characteristics and prognosis in patients with HCC. Data relating to CKAP4 mRNA expression in HCC tumor and normal adjacent liver tissues, and clinicopathological characteristics, were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The CKAP4 mRNA levels in tumor tissues were compared with those in normal adjacent liver tissues, their association with clinicopathological parameters was analyzed, and diagnostic and prognostic values were evaluated in patients with HCC. In all 4 datasets (total samples, n=693), CKAP4 mRNA levels were significantly higher in tumor tissues compared with adjacent tissues (all P<0.001), with the area under the receiver operating characteristic curve ranging from 0.799-0.898 for HCC diagnosis. In patients with HCC with available clinical data (n=361), the low-level CKAP4 mRNA group exhibited a lower body mass index (P=0.005), higher α-fetoprotein level (P<0.001), more frequent adjacent liver tissue inflammation (P<0.001), poorer tumor histological grade (P<0.001), higher Ishak fibrosis score (P=0.035) and a more advanced tumor node metastasis (TNM) stage (P=0.014) compared with the high-level CKAP4 mRNA group. Patients stratified by all the above parameters, except for TNM stage, exhibited significantly different expression of tissue CKAP4 mRNA (P<0.05-0.001). Furthermore, higher CKAP4 mRNA levels were observed in patients who died within one year following diagnosis compared with those who survived >3 years (P=0.003). The high-level CKAP4 mRNA group also exhibited lower overall survival (OS) and disease-free survival (DFS) rates compared with the low-level group [hazard ratio (HR)=1.494; 95% confidence interval (CI), 1.044-2.138; P=0.028] for OS and (HR=1.616; 95% CI, 1.022-2.555; P=0.040) for DFS. The results of the present study suggest that CKAP4 mRNA is upregulated in HCC tumor tissues compared with normal adjacent tissues, and is associated with poor clinical prognosis, pathological features and survival in patients with HCC. Thus, CKAP4 is a potential biomarker for HCC diagnosis and prognosis.

Fluid Biomarkers for Predicting the Prognosis of Liver Cirrhosis.

Liver cirrhosis is the terminal stage of most chronic liver conditions, with a high risk of mortality. Careful evaluation of the prognosis of cirrhotic patients and providing precise management are crucial to reduce the risk of mortality. Although the liver biopsy and hepatic venous pressure gradient (HVPG) can efficiently evaluate the prognosis of cirrhotic patients, their application is limited due to the invasion procedures. Child-Pugh score and the model for end-stage liver disease (MELD) score had been widely used in the assessment of cirrhotic prognosis, but the defects of subjective variable application in Child-Pugh score and unsuitability to all phases of liver cirrhosis in MELD score limit their prognostic values. In recent years, continuous efforts have been made to investigate the prognostic value of body fluid biomarkers for cirrhotic patients, and promising results have been reported. Since the collection of fluid specimens is easy, noninvasive, and repeatable, fluid biomarkers can be ideal indicators to predict the prognosis of cirrhosis. Here, we reviewed noninvasive fluid biomarkers in different prognostic functions, including the prediction of survival and complication development.

A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.

Objective: This study aimed to develop and validate a simple-to-use nomogram for early hepatocellular carcinoma (HCC) patients undergoing a preoperative consultation and doctors conducting a postoperative evaluation. Methods: A total of 2,225 HCC patients confirmed with stage I or II were selected from the Surveillance, Epidemiology, and End Results database between January 2010 and December 2015. The patients were randomly divided into two groups: a training group (n = 1,557) and a validation group (n = 668). Univariate and multivariate hazards regression analyses were used to identify independent prognostic factors. The Akaike information criterion (AIC) was used to select variables for the nomogram. The performance of the nomogram was validated concerning its ability of discrimination and calibration and its clinical utility. Results: Age, alpha-fetoprotein (AFP), race, the degree of differentiation, and therapy method were significantly associated with the prognosis of early HCC patients. Based on the AIC results, five variables (age, race, AFP, degree of differentiation, and therapy method) were incorporated into the nomogram. The concordance indexes of the simple nomogram in the training and validation groups were 0.707 (95% CI: 0.683-0.731) and 0.733 (95% CI: 0.699-0.767), respectively. The areas under the receiver operating characteristic (ROC) curve of the nomogram in the training and validation groups were 0.744 and 0.764, respectively, for predicting 3-year survival, and 0.786 and 0.794, respectively, for predicting 5-year survival. Calibration plots showed good consistency between the predictions of the nomogram and the actual observations in both the training and validation groups. Decision curve analysis (DCA) showed that the simple nomogram was clinically useful, and the overall survival significantly differed between low- and high-risk groups divided by the median score of the nomogram in the training group (P < 0.001). Conclusion: A simple-to-use nomogram based on a large population-based study is developed and validated, which is a conventional tool for doctors to facilitate the individual consultation of preoperative patients and the postoperative personalized evaluation.

Efficacy and safety of Hou Gu Mi Xi in patients with spleen qi deficiency syndrome who underwent radical gastrectomy for gastric cancer: protocol for a multicenter, randomized, double-blind, placebo-controlled trial.

BACKGROUND: Spleen qi deficiency (SQD), a syndrome based on traditional Chinese medicine (TCM) theory, is common in patients after radical gastrectomy. SQD manifests with chronic gastrointestinal disorders and systemic symptoms and is challenging to manage. Hou Gu Mi Xi (HGMX) is a dietary TCM formula for SQD. This study aims to evaluate the efficacy and safety of HGMX in patients with SQD who have undergone radical gastrectomy for gastric cancer. METHODS AND DESIGN: This study is a multicenter, randomized, double-blind, placebo-controlled trial. One hundred thirty patients with SQD who have undergone radical gastrectomy for gastric cancer will be assigned to receive either HGMX or placebo for 2 years. The main outcome will be changes in SQD symptoms assessed by the Spleen Qi Deficiency Symptoms Grading and Quantifying Scale. The secondary outcomes will be changes in quality of life assessed by the Short Form 36 scale, performance status as assessed by the Eastern Cooperative Oncology Group Performance Status scale, body weight, and body mass index. Progression-free survival will also be assessed as a secondary outcome. Adverse events (AEs), severe AEs, and study withdrawal due to AEs will be recorded to evaluate the safety of HGMX. DISCUSSION: The results of this trial will provide initial evidence for the use of HGMX as an alternative and complementary intervention to manage chronic postoperative complications in patients who have undergone radical gastrectomy for gastric cancer. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03025152 . Registered on 17 January 2017.

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.