Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis.

BACKGROUND: Gastric cancer (GC) is a prevalent malignant cancer of digestive system. To identify key genes in GC, mRNA microarray GSE27342, GSE29272, and GSE33335 were downloaded from GEO database. METHODS: Differentially expressed genes (DEGs) were obtained using GEO2R. DAVID database was used to analyze function and pathways enrichment of DEGs. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape software. Then, the influence of hub genes on overall survival (OS) was performed by the Kaplan-Meier plotter online tool. Module analysis of the PPI network was performed using MCODE. Additionally, potential stem loop miRNAs of hub genes were predicted by miRecords and screened by TCGA dataset. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst. RESULTS: In total, 67 DEGs were identified; upregulated DEGs were mainly enriched in biological process (BP) related to angiogenesis and extracellular matrix organization and the downregulated DEGs were mainly enriched in BP related to ion transport and response to bacterium. KEGG pathways analysis showed that the upregulated DEGs were enriched in ECM-receptor interaction and the downregulated DEGs were enriched in gastric acid secretion. A PPI network of DEGs was constructed, consisting of 43 nodes and 87 edges. Twelve genes were considered as hub genes owing to high degrees in the network. Hsa-miR-29c, hsa-miR-30c, hsa-miR-335, hsa-miR-33b, and hsa-miR-101 might play a crucial role in hub genes regulation. In addition, the transcription factors-hub genes pairs were displayed with 182 edges and 102 nodes. The high expression of 7 out of 12 hub genes was associated with worse OS, including COL4A1, VCAN, THBS2, TIMP1, COL1A2, SERPINH1, and COL6A3. CONCLUSIONS: The miRNA and TFs regulation network of hub genes in GC may promote understanding of the molecular mechanisms underlying the development of gastric cancer and provide potential targets for GC diagnosis and treatment.

The hOGG1 Ser326Cys polymorphism contributes to digestive system cancer susceptibility: evidence from 48 case-control studies.

The Ser326Cys polymorphism in the human 8-oxogunaine DNA glycosylase (hOGG1) gene had been implicated in cancer susceptibility. Studies investigating the associations between the Ser326Cys polymorphism and digestion cancer susceptibility showed conflicting results. Therefore, a meta-analysis was performed to derive a more precise estimation of the relationship. We conducted a meta-analysis of 48 studies that included 12,073 cancer cases and 19,557 case-free controls. We assessed the strength of the association using odds ratios (ORs) with 95% confidence intervals (CIs). In our analysis, the hOGG1 Ser326Cys polymorphism was significantly associated with the risk of digestive system cancers (Cys/Cys vs. Ser/Ser: OR = 1.17, 95% CI = 1.00-1.35, P < 0.001; Cys/Cys vs. Cys/Ser + Ser/Ser: OR = 1.14, 95% CI = 1.00-1.29, P < 0.001). In subgroup analyses by cancer types, we found that the hOGG1 Ser326Cys polymorphism may increase hepatocellular cancer and colorectal cancer risks, but decrease the risk of oral cancer. These findings supported that hOGG1 Ser326Cys polymorphism may contribute to the susceptibility of digestive cancers.

Predicting brain age using Tri-UNet and various MRI scale features.

In the process of human aging, significant age-related changes occur in brain tissue. To assist individuals in assessing the degree of brain aging, screening for disease risks, and further diagnosing age-related diseases, it is crucial to develop an accurate method for predicting brain age. This paper proposes a multi-scale feature fusion method called Tri-UNet based on the U-Net network structure, as well as a brain region information fusion method based on multi-channel input networks. These methods address the issue of insufficient image feature learning in brain neuroimaging data. They can effectively utilize features at different scales of MRI and fully leverage feature information from different regions of the brain. In the end, experiments were conducted on the Cam-CAN dataset, resulting in a minimum Mean Absolute Error (MAE) of 7.46. The results demonstrate that this method provides a new approach to feature learning at different scales in brain age prediction tasks, contributing to the advancement of the field and holding significance for practical applications in the context of elderly education.

Preparation of Al3Ti-Al2O3/Al Inoculant and Its Inoculation Effect on Al-Cu-Mn Alloy.

The grain size plays a pivotal role in determining the properties of the alloy. The grain size can be significantly decreased by adding inoculants. Aiming to address the shortcomings of existing inoculants, the Al3Ti-Al2O3/Al inoculant was successfully prepared using Al-Ti master alloy and Al2O3 whiskers as raw materials. With the aid of ultrasonic energy, the Al2O3 whiskers were uniformly dispersed within the inoculants. Under the combined action of ultrasonic and titanium, the Al2O3 whiskers were broken into small particles at high temperature. To enhance the morphology of Al3Ti and achieve even particle dispersion throughout the matrix, vacuum rapid quenching treatment was applied to the inoculant. The SEM test results indicated a significant reduction in particle size after vacuum rapid quenching. The Al3Ti-Al2O3/Al inoculants exhibited excellent grain refinement effects on the weldable Al-Cu-Mn alloy. Crystallographic calculations and HRTEM analysis revealed that Al2O3 and Al have orientation relationships, indicating their potential as effective heterogeneous nucleation sites. The mechanical properties of the Al-Cu-Mn alloy were obviously improved after the Al3Ti-Al2O3/Al inoculant was added.

The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study.

Purpose: To establish and validate a machine learning based radiomics model for detection of perineural invasion (PNI) in gastric cancer (GC). Methods: This retrospective study included a total of 955 patients with GC selected from two centers; they were separated into training (n=603), internal testing (n=259), and external testing (n=93) sets. Radiomic features were derived from three phases of contrast-enhanced computed tomography (CECT) scan images. Seven machine learning (ML) algorithms including least absolute shrinkage and selection operator (LASSO), naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was constructed by aggregating the radiomic signatures and important clinicopathological characteristics. The predictive ability of the radiomic model was then assessed with receiver operating characteristic (ROC) and calibration curve analyses in all three sets. Results: The PNI rates for the training, internal testing, and external testing sets were 22.1, 22.8, and 36.6%, respectively. LASSO algorithm was selected for signature establishment. The radiomics signature, consisting of 8 robust features, revealed good discrimination accuracy for the PNI in all three sets (training set: AUC = 0.86; internal testing set: AUC = 0.82; external testing set: AUC = 0.78). The risk of PNI was significantly associated with higher radiomics scores. A combined model that integrated radiomics and T stage demonstrated enhanced accuracy and excellent calibration in all three sets (training set: AUC = 0.89; internal testing set: AUC = 0.84; external testing set: AUC = 0.82). Conclusion: The suggested radiomics model exhibited satisfactory prediction performance for the PNI in GC.

A CT-based radiomics signature for prediction of HER2 overexpression and treatment efficacy of trastuzumab in advanced gastric cancer.

Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is very important for appropriate management of advanced gastric cancer (AGC) patients. In this study, we aimed to develop and validate a computed tomography (CT)-based radiomics signature for preoperative prediction of HER2 overexpression and treatment efficacy of trastuzumab in AGC. Methods: We retrospectively enrolled 536 consecutive AGC patients (median age, 59 years; interquartile range, 52-65 years; 377 male, 159 female) and separated them into a training set (n=357) and a testing set (n=179). Radiomic features were extracted from 3 different phase images of contrast-enhanced CT scans, and a radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. The predictive performance of the radiomics signature was assessed in the training and testing sets. Univariable and multivariable logistical regression analyses were used to identify independent risk factors of HER2 overexpression. Univariable and multivariable Cox regression analyses were used to identify the risk factors of overall survival (OS) and progression-free survival (PFS). The predictive value of the radiomics signature for treatment efficacy of trastuzumab was also evaluated. Results: The radiomics signature comprised eight robust features that demonstrated good discrimination ability for HER2 overexpression in the training set [area under the curve (AUC) =0.85] and the testing set (AUC =0.81). Multivariable Cox regression analysis revealed that the radiomics signature was an independent risk factor for OS [hazard ratio (HR) =2.01, P=0.001] and PFS (HR =1.32, P=0.01). The radiomics score of patients who achieved disease control was significantly lower than that of patients with progressive disease (P=0.023). Conclusions: The proposed radiomics signature showed favorable accuracy for prediction of HER2 overexpression and prognosis in AGC. It has promising potential as a noninvasive approach for selecting patients for target therapy.

Multifunctional nanoprobes combined with radiotherapy and hypoxia-activated therapy synergistically improve antitumor efficacy.

The presence of hypoxia in tumors is characteristic of most solid tumors and it promotes not only tumor angiogenesis but also tumor cell invasion and metastasis. It also results in resistance of tumor tissue to radiation, leading to poor outcomes of tumor radiotherapy. Therefore, to address this conundrum, highly selective gold nanoclusters were prepared as fluorescent imaging agents and radiosensitizers and then loaded with tumor hypoxia-activated prodrugs to prepare nanoprobes which synergistically improved the anti-tumor efficacy by combining radiotherapy and hypoxia-activated therapy. The designed nanoprobes have ultra-small size, high selectivity for integrin αvß3 receptor-positive tumor cells and tumor neovascular endothelial cells, and excellent fluorescence imaging performance. The experimental procedures were carried out in vitro and in vivo to demonstrate that the developed nanoprobes have a high level of biocompatibility, efficient radiosensitization effect, and anti-tumor efficacy at cell and tissue levels. The combined application of radiotherapy and hypoxia-activated therapy can overcome the radiation resistance caused by tumor hypoxia, compensate for the limitations of single radiotherapy, inhibit tumor growth, improve the efficacy of tumor radiotherapy, and provide new possibilities for the development of more precise and effective treatment strategies.

A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer.

Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC. Materials and Methods: Seven hundred and forty-five consecutive AGC patients (median age, 59 years; interquartile range, 52-66 years; 515 male and 230 female) were enrolled and separated into training set (n = 521) and testing set (n = 224) in this retrospective study. Radiomics features were extracted from three phases images of contrast-enhanced CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. Univariable and multivariable logistical regression analysis were used to establish predictive model with independent risk factors of HER2 overexpression. The predictive performance of radiomics model was assessed in the training and testing sets. Results: The positive rate of HER2 was 15.9% and 13.8% in the training set and testing set, respectively. The positive rate of HER2 in intestinal-type GC was significantly higher than that in diffuse-type GC. The radiomics signature comprised eight robust features demonstrated good discrimination ability for HER2 overexpression in the training set (AUC = 0.84) and the testing set (AUC = 0.78). A radiomics-based model that incorporated radiomics signature and pathological type showed good discrimination and calibration in the training (AUC = 0.85) and testing (AUC = 0.84) sets. Conclusion: The proposed radiomics model showed favorable accuracy for prediction of HER2 overexpression in AGC.

A CT-based Radiomics Model for Prediction of Lymph Node Metastasis in Early Stage Gastric Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a CT-based radiomics model for preoperative prediction of lymph node metastasis (LNM) in early stage gastric cancer (EGC). MATERIALS AND METHODS: Four hundred and sixty-three consecutive EGC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. The predictive performance of radiomics signature was tested in the training and testing cohorts. Multivariate logistic regression analysis was conducted to build a radiomics-based model combined radiomics signature and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. RESULTS: The radiomics signature comprised six robust features showed significant association with LNM in both cohorts. A radiomics model that incorporated radiomics signature and CT-reported lymph node status showed good calibration and discrimination in the training cohort (AUC = 0.91) and testing cohort (AUC = 0.89). Decision curve analysis confirmed the clinical utility of this model. CONCLUSION: The CT-based radiomics model showed favorable accuracy for prediction of LNM in EGC and may help to improve clinical decision-making.

A radiomics-based model for prediction of lymph node metastasis in gastric cancer.

PURPOSE: To develop and validate a radiomics-based model for preoperative prediction of lymph node metastasis (LNM) in gastric cancer (GC). METHOD: A total of 768 GC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography (CT) scans. A radiomics signature was built with highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method in the training cohort (n = 486). The signature was further validated in internal validation (n = 240) and external testing cohorts (n = 42). Multivariate logistic regression analysis was conducted to build a model that combined radiomics signature, serum biomarkers, and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. The predictive value of the model was also evaluated in early stage GC (EGC) subgroup. RESULTS: The radiomics signature comprised 7 robust features showed favorable prediction efficacy in all cohorts. A radiomics-based model that incorporated radiomics signature, serum CA72-4, and CT-reported lymph node status had good calibration and discrimination in training cohort [AUC, 0.92; 95% confidence interval (CI), 0.89-0.95] and validation cohort (AUC 0.86; 95% CI, 0.81-0.91). The model also showed a favorable predictive performance for EGC patients with an AUC of 0.85 (95% CI, 0.76-0.94). Decision curve analysis confirmed the clinical utility of this model. CONCLUSIONS: The radiomics-based model showed favorable accuracy for prediction of LNM in GC. The model may also serve as a noninvasive tool for preoperative evaluation of LNM in EGC.

A seven long-noncoding RNA signature predicts prognosis of lung squamous cell carcinoma.

Aim: This study profiled differentially expressed long noncoding RNAs (lncRNAs) in lung squamous cell carcinoma (LSCC) to predict LSCC overall survival (OS) using The Cancer Genome Atlas data. Materials & methods: The RNA-seq and clinical dataset of 475 LSCC patients was retrieved from The Cancer Genome Atlas database and statistically analyzed. Results: There were 67 upregulated and 32 downregulated lncRNAs in LSCCs and 12 lncRNAs associated with OS. The seven-lncRNA signature was associated with poor OS and RP11-150O12.6 and CTA-384D8.35 were associated with better OS (p < 0.001). The seven lncRNAs-mRNA interaction network analysis showed their association with 187 protein-coding genes for cancer development, cell migration, adhesion, proliferation, apoptosis, angiogenesis and the MAPK signaling pathways. Conclusion: This seven-lncRNA signature is useful to predict LSCC OS.

A CT-based radiomics signature for evaluating tumor infiltrating Treg cells and outcome prediction of gastric cancer.

BACKGROUND: Tumor infiltrating regulatory T (TITreg) cells are highly infiltrated in gastric cancer (GC) and associated with worse prognosis of GC patients. We aim to develop and validate a radiomics signature for evaluation of TITreg cells and outcome prediction of GC patients. METHODS: A total of 165 GC patients from three independent cohorts were enrolled in this retrospective study. The abundance of TITreg cells were evaluated by using multispectral immunohistochemical analysis and CIBERSORT algorithm. The radiomics features were extracted by using PyRadiomics software and the radiomics signature was generated by using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The receiver operator characteristic (ROC) curves were applied to assess the performance of radiomics signature for estimating TITreg cells. Univariable and multivariable Cox regression analysis were used for identifying risk factor of overall survival (OS). The prognostic value of the radiomics signature and the TITreg cells were evaluated by using the Kaplan-Meier method and log-rank test. RESULTS: Six robust features were selected for building the radiomics signature. The radiomics signature showed good ability for estimating TITreg in the training, validation and testing cohort, with area under the curve (AUC) of 0.884, 0.869 and 0.847, respectively. Multivariable Cox regression analysis showed that the radiomics signature was an independent risk factor of unfavorable OS of GC patients. CONCLUSIONS: The proposed CT-based radiomics signature is a promising non-invasive biomarker of TITreg cells and outcome prediction of GC patients.

T-cell receptor gene therapy targeting melanoma-associated antigen-A4 by silencing of endogenous TCR inhibits tumor growth in mice and human.

Genetically engineered T cells expressing a T-cell receptor (TCR) are powerful tools for cancer treatment and have shown significant clinical effects in sarcoma patients. However, mismatch of the introduced TCR α/ß chains with endogenous TCR may impair the expression of transduced TCR, resulting in an insufficient antitumor capacity of modified T cells. Here, we report the development of immunotherapy using human lymphocytes transduced with a codon-optimized melanoma-associated antigen (MAGE)-A4 and HLA-A*2402-restricted TCR, which specifically downregulate endogenous TCR by small interfering RNA (si-TCR). We evaluated the efficacy of this immunotherapy in both NOD-SCID mice and uterine leiomyosarcoma patients. Our results revealed that transduced human lymphocytes exhibited high surface expression of the introduced tumor-specific TCR, enhanced cytotoxic activity against antigen-expressing tumor cells, and increased interferon-γ production by specific MAGE-A4 peptide stimulation. Retarded tumor growth was also observed in NOD-SCID mice inoculated with human tumor cell lines expressing both MAGE-A4 and HLA-A*2402. Furthermore, we report the successful management of a case of uterine leiomyosarcoma treated with MAGE-A4 si-TCR/HLA-A*2402 gene-modified T cells. Our results indicate that the TCR-modified T cell therapy is a promising novel strategy for cancer treatment.

A novel microRNA signature predicts survival in stomach adenocarcinoma.

Recent microRNA (miRNA) expression profiling studies suggest the clinical use of miRNAs as potential prognostic biomarkers in various malignancies. In this study, aiming to identify microRNAs with prognostic value for overall survival (OS) in stomach adenocarcinoma (STAD) patients, we analyzed the miRNA expression profiles and the associated clinical characteristics in 380 STAD samples from The Cancer Genome Atlas (TCGA) dataset. An eight-miRNA signature for predicting OS in STAD patients was identified and self-validated by survival analysis and semi-supervised principal components method. We developed a linear prognostic model composed of these miRNAs to divide patients into high- and low-risk groups according to the calculated prognostic scores. Kaplan-Meier analysis demonstrated that patients in the high-risk group had worse OS compared with patients in the low-risk group. Notably, this miRNA prognostic model showed prognostic significance to the STAD patients in early stages and the chemo-resistant patients, who would potentially benefit from additional medical interventions. Finally, this eight-miRNA signature is an independent prognostic biomarker and demonstrates a good predictive performance for 5-year survival. Thus, this signature may serve as a novel biomarker for predicting survival as well as chemotherapy response in STAD patients.

Identification of a seven-miRNA signature as prognostic biomarker for lung squamous cell carcinoma.

BACKGROUND: Specific biomarkers for outcome prediction of lung squamous cell carcinoma (LUSC) are still lacking. This study assessed the prognostic value of differentially expressed miRNAs of LUSC patients. RESULTS: Twelve of the 133 most significantly altered miRNAs were associated with overall survival (OS) across different clinical subclasses of the Cancer Genome Atlas (TCGA) LUSC cohort. A linear prognostic model of seven miRNAs was developed to divide patients into high- and low-risk groups. Patients assigned to the high-risk group exhibited poor OS compared with patients in the low-risk group, which was further validated in the validation cohort and entire LUSC cohort. METHODS: MiRNA expression profiles with clinical information of 447 LUSC patients were obtained from TCGA. Most significantly altered miRNAs were identified between tumor and normal samples. Using survival analysis and supervised principal components method, a seven-miRNA signature for prediction of OS of LUSC patients was established. Survival receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. The biological relevance of predicted miRNA targets was also analyzed using bioinformatics method. CONCLUSIONS: The current study suggests that seven-miRNA signature may have clinical implications in the outcome prediction of LUSC.

Plasma miR-324-3p and miR-1285 as diagnostic and prognostic biomarkers for early stage lung squamous cell carcinoma.

BACKGROUND: Specific biomarkers for early detection and outcome prediction of lung squamous cell carcinoma (LSCC) are still lacking. This study assessed the differentially expressed miRNAs as potential biomarkers for early stage LSCC. RESULTS: Base on the results of multi-phase study, we found that miR-324-3p was significantly up-regulated, whereas mir-1285 was significantly down-regulated in plasma of stage I LSCC patients compared to healthy controls. ROC analysis showed that AUC of miR-324-3p and miR-1285 were 0.79 and 0.85, respectively. The combination of these two miRNAs could further improve the diagnostic accuracy (AUC = 0.89). The multivariate analysis revealed that plasma miR-324-3p level was an independent prognostic predictor for early stage LSCC. METHODS: 395 patients and 195 healthy controls were enrolled in this study. We screened the differentially expressed plasma miRNAs using TaqMan Low Density Arrays (TLDA) followed by three-phase qRT-PCR validation. We also evaluated the association of candidate miRNAs with overall survival of early stage LSCC patients. Finally, the target genes of the candidate miRNAs were analyzed using public available databases and bioinformatics methods. CONCLUSIONS: The current study suggests that plasma miR-324-3p and miR-1285 levels could serve as LSCC early detection markers while miR-324-3p may serve as a prognostic marker for LSCC patients.

Identification of a three-miRNA signature as a blood-borne diagnostic marker for early diagnosis of lung adenocarcinoma.

BACKGROUND: The subtypes of NSCLC have unique characteristics of pathogenic mechanism and responses to targeted therapies. Thus, non-invasive markers for diagnosis of different subtypes of NSCLC at early stage are needed. RESULTS: Based on the results from the screening and validation process, 3 miRNAs (miR-532, miR-628-3p and miR-425-3p) were found to display significantly different expression levels in early-stage lung adenocarcinoma, as compared to those in healthy controls. ROC analysis showed that the miRNA-based biomarker could distinguish lung adenocarcinoma from healthy controls with high AUC (0.974), sensitivity (91.5%), and specificity (97.8%). Importantly, these three miRNAs could also distinguish lung adenocarcinoma from lung benigh diseases and other subtypes of lung cancer. METHODS: Two hundreds and one early-stage lung adenocarcinoma cases and one hundreds seventy eight age- and sex-matched healthy controls were recruited to this study. We screened the differentially expressed plasma miRNAs using TaqMan Low Density Arrays (TLDA) followed by three-phase qRT-PCR validation. A risk score model was established to evaluate the diagnostic value of the plasma miRNA profiling system. CONCLUSIONS: Taken together, these findings suggest that the 3 miRNA-based biomarker might serve as a novel non-invasive approach for diagnosis of early-stage lung adenocarcinoma.

Diagnostic and prognostic value of circulating miR-21 for cancer: a systematic review and meta-analysis.

BACKGROUND: MicroRNAs (miRNAs) have been reported to be aberrantly expressed in patients with cancer. Many studies have shown that circulating miRNAs could play potential roles as diagnostic and prognostic biomarkers of cancers. The aim of this meta-analysis is to summarize the role of circulating miR-21 as a biomarker in patients with a variety of carcinomas. MATERIAL AND METHODS: Eligible studies were identified and assessed for quality through multiple search strategies. For diagnostic meta-analysis, the sensitivity, specificity, and other measures of miR-21 in the diagnosis of cancer were pooled using bivariate random-effects approach models. For prognostic meta-analysis, pooled hazard ratios (HRs) of circulating miR-21 for survival were calculated. RESULTS: A total of 36 studies dealing with various carcinomas were included for the systemic review. Among them, 23 studies were finally enrolled in the global meta-analysis (17 studies for diagnosis and 6 studies for prognosis). For diagnostic meta-analysis, the overall pooled results for sensitivity, specificity, positive likelihood ratio (LRP), negative likelihood ratios (LRN) and diagnostic odds ratio (DOR) were 75.7% (95% CI: 67.1%-82.6%), 79.3% (95% CI: 74.2%-83.5%), 3.65 (95% CI: 2.83-4.70), 0.31 (95% CI: 0.22-0.43), and 11.88 (95% CI: 6.99-20.19), respectively. For prognostic meta-analysis, the pooled HR of higher miR-21 expression in circulation was 2.37 (95% CI: 1.83-3.06, P<0.001), which could significantly predict poorer survival in general carcinomas. Importantly, subgroup analysis suggested that higher expression of miR-21 correlated with worse overall survival (OS) significantly in carcinomas of digestion system (HR, 5.77 [95% CI: 2.65-12.52]). CONCLUSIONS: Our findings suggest that circulating miR-21 may not suitable to be a diagnostic biomarker, but it has a prognostic value in patients with cancer.