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Background The prognosis for patients with colorectal-cancer liver metastases (CRLM) after curative surgery remains poor and shows great heterogeneity. Early recurrence, defined as tumor recurrence within 6 months of curative surgery, is associated with poor survival, requiring earlier detection and intervention. This study aimed to develop and validate a bedside model based on clinical parameters to predict early recurrence in CRLM patients and provide insight into post-operative surveillance strategies. Material and methods A total of 202 consecutive CRLM patients undergoing curative surgeries between 2012 and 2019 were retrospectively enrolled and randomly assigned to the training (n = 150) and validation (n = 52) sets. Baseline information and radiological, pathological, and laboratory findings were extracted from medical records. Predictive factors for early recurrence were identified via a multivariate logistic-regression model to develop a predictive nomogram, which was validated for discrimination, calibration, and clinical application. Results Liver-metastases number, lymph-node suspicion, neurovascular invasion, colon/rectum location, albumin and post-operative carcinoembryonic antigen, and carbohydrate antigen 19–9 levels (CA19–9) were independent predictive factors and were used to construct the nomogram for early recurrence after curative surgery. The area under the curve was 0.866 and 0.792 for internal and external validation, respectively. The model significantly outperformed the clinical risk score and Beppu’s model in our data set. In the lift curve, the nomogram boosted the detection rate in post-operative surveillance by two-fold in the top 30% high-risk patients. Conclusion Our model for early recurrence in CRLM patients after curative surgeries showed superior performance and could aid in the decision-making for selective follow-up strategies.

Objective: This study was conducted in order to construct a competitive endogenous RNA (ceRNA) network to screen RNA that plays an important role in colon cancer and to construct a model to predict the prognosis of patients.Methods: The gene expression data of colon cancer were downloaded from the TCGA database. The difference was analyzed by the R software and the ceRNA network was constructed. The survival-related RNA was screened out by combining with clinical information, and the prognosis model was established by lasso regression. CIBERSORT was used to analyze the infiltration of immune cells in colon cancer, and the differential expression of immune cells related to survival was screened out by combining clinical information. The correlation between RNA and immune cells was analyzed by lasso regression. PCR was used to verify the expression of seven RNAs in colon cancer patients with different prognoses.Results: Two hundred and fifteen lncRNAs, 357 miRNAs, and 2,955 mRNAs were differentially expressed in colon cancer. The constructed ceRNA network contains 18 lncRNAs, 42 miRNAs, and 168 mRNAs, of which 18 RNAs are significantly related to survival. Through lasso analysis, we selected seven optimal RNA construction models. The AUC value of the model was greater than 0.7, and there was a significant difference in the survival rate between the high- and low-risk groups. Two kinds of immune cells related to the prognosis of patients were screened out. The results showed that the expression of seven RNA markers in colon cancer patients with different prognoses was basically consistent with the model analysis.Conclusion: We have established the regulatory network of ceRNA in colon cancer, screened out seven core RNAs and two kinds of immune cells, and constructed a comprehensive prognosis model of colon cancer patients.

Chromoblastomycosis is a chronic cutaneous fungal infection caused by dematiaceous fungi. It is a therapeutic challenge because of the lack of specific treatments. We describe a refractory case of chromoblastomycosis in which the lesion did not respond to initial treatment, but then use of topical imiquimod cured the lesion successfully.

Purpose: Colorectal liver metastasis (CRLM) is the major cause of death due to colorectal cancer. Although great efforts have been made in treatment of CRLM, about 60–70% of patients will develop hepatic recurrence. Hepatic steatosis was reported to provide fertile soil for metastasis. However, whether hepatic steatosis predicts higher incidence of CRLM recurrence is not clear. Therefore, we aimed to determine the role of hepatic steatosis in CRLM recurrence in the present study.Methods: Consecutive CRLM patients undergoing curative treatment were retrospectively enrolled and CT liver-spleen attenuation ratio was used to detect the presence of hepatic steatosis. In patients with hepatic steatosis, we also detected the presence of fibrosis. Besides, a systematic literature search was performed to do meta-analysis to further analyze the association between hepatic steatosis and CRLM recurrence.Results: A total of 195 eligible patients were included in our center. Patients with hepatic steatosis had a significantly worse overall (P = 0.0049) and hepatic recurrence-free survival (RFS) (P = 0.0012). Univariate and multivariate analysis confirmed its essential role in prediction of RFS. Besides, hepatic fibrosis is associated with worse overall RFS (P = 0.039) and hepatic RFS (P = 0.048). In meta-analysis, we included other four studies, with a total of 1,370 patients in the case group, and 3,735 patients in the control group. The odds ratio was 1.98 (95% CI: 1.25–3.14, P = 0.004), indicating that patients with steatosis had a significantly higher incidence of CRLM recurrence.Conclusion: In summary, patients with hepatic steatosis had a significantly worse overall and hepatic RFS and it's associated with higher incidence of CRLM recurrence.

Background Angiopoietin-like protein 1 (ANGPTL1) has been proved to suppress tumor metastasis in several cancers. However, its extracellular effects on the pre-metastatic niches (PMNs) are still unclear. ANGPTL1 has been identified in exosomes, while its function remains unknown. This study was designed to explore the role of exosomal ANGPTL1 on liver metastasis in colorectal cancer (CRC). Methods Exosomes were isolated by ultracentrifugation. The ANGPTL1 level was detected in exosomes derived from human CRC tissues. The effects of exosomal ANGPTL1 on CRC liver metastasis were explored by the intrasplenic injection mouse model. The liver PMN was examined by vascular permeability assays. Exosomal ANGPTL1 localization was validated by exosome labeling. The regulatory mechanisms of exosomal ANGPTL1 on Kupffer cells were determined by RNA sequencing. qRT-PCR, Western Blot, and ELISA analysis were conducted to examine gene expressions at mRNA and protein levels. Results ANGPTL1 protein level was significantly downregulated in the exosomes derived from CRC tumors compared with paired normal tissues. Besides, exosomal ANGPTL1 attenuated liver metastasis and impeded vascular leakiness in the liver PMN. Moreover, exosomal ANGPTL1 was mainly taken up by KCs and regulated the KCs secretion pattern, enormously decreasing the MMP9 expression, which finally prevented the liver vascular leakiness. In mechanism, exosomal ANGPTL1 downregulated MMP9 level in KCs by inhibiting the JAK2-STAT3 signaling pathway. Conclusions Taken together, exosomal ANGPTL1 attenuated CRC liver metastasis and impeded vascular leakiness in the liver PMN by reprogramming the Kupffer cell and decreasing the MMP9 expression. This study suggests a suppression role of exosomal ANGPTL1 on CRC liver metastasis and expands the approach of ANGPTL1 functioning.

BackgroundDespite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to forecast patient’s prognosis.MethodsIRGs were collected from the ImmPort database. The CRC dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another two CRC datasets from the Gene Expression Omnibus (GEO). Gene function enrichment analysis was conducted. A prognostic nomogram was built incorporating the IRG signature with clinical risk factors.ResultsThe three datasets had 487, 579, and 224 patients, respectively. A prognostic six-gene-signature (CCL22, LIMK1, MAPKAPK3, FLOT1, GPRC5B, and IL20RB) was developed through feature selection that showed good differentiation between the low- and high-risk groups in the training set (p < 0.001), which was later confirmed in the two validation groups (log-rank p < 0.05). The signature outperformed tumor TNM staging for survival prediction. GO and KEGG functional annotation analysis suggested that the signature was significantly enriched in metabolic processes and regulation of immunity (p < 0.05). When combined with clinical risk factors, the model showed robust prediction capability.ConclusionThe immune-related six-gene signature is a reliable prognostic indicator for CRC patients and could provide insight for personalized cancer management.

Background: Colorectal cancer (CRC) is the third most common malignancy in the world and metastasis is responsible for a major proportion of the cancer-related deaths in CRC patients.Aims: To construct machine learning models for predicting lymph node and distant metastases in colorectal cancer and analyze biological functions features of metastasis-related genes.Methods: RNA-seq and miRNA-seq data as well as corresponding clinical data from colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) in non-LNM (N0) and LNM (N1/N2) as well as non-distant metastases (M0) and distant metastases (M1) were analyzed. Six machine learning models including logistic regression (LR), random forest (RF), support vector machine (SVM), Catboost, gradient boosting decision tree (GBDT), and artificial neural network (NN) were constructed to predict cancer metastasis and the feature genes of the optimal model were further analyzed by functional enrichment, protein-protein interaction (PPI) network, and drug-target analyses.Results: Differential RNA expression profiles of LNM and non-LNM as well as M0 vs. M1 were observed in both COAD and READ samples. NN model was determined to be the optimal model for predicting distant metastases, while Catboost and LR models were the optimal models for predicting LNM in COAD and READ samples, respectively. PPI analysis indicated that KIR2DL4, chemokine-related genes CXCL9/10/11/13 and CCL25, and gamma-aminobutyric acid (GABA) receptor genes (GABRR1, GABRB2 and GABRA3) were key genes in metastasis. In addition, atorvastatin and eszopiclone were identified as potential therapeutic agents as they target these genes.Conclusions: We constructed six machine learning models for predicting colorectal cancer metastases and identify the optimal model. We analyzed biological functions features of metastasis-related RNAs in colorectal cancer.

Purpose: This study aimed to investigate the role of small nucleolar RNAs (snoRNAs) and their host genes (SNHGs) in tumorigenesis and progression of esophageal carcinoma (EC).Methods: RNA-seq data and clinical information for EC patients were obtained from The Cancer Genome Atlas database. Differentially expressed snoRNAs (DE-snoRNAs) and SNHGs (DE-SNHGs) between EC samples and normal controls were screened with the edgeR package, followed by the trend analysis with STEM. Survival analysis was performed using the Kaplan–Meier method with the log-rank test. Screening of co-expressed genes and functional enrichment analyses were performed with Pearson’s correlation analysis and enrichment analysis, respectively. Lastly, the co-expression and ceRNA networks were constructed.Results: A total of 19 DE-snoRNAs and nine DE-SNHGs were screened between EC samples and normal controls. Moreover, two snoRNA clusters related to the clinic stage were analyzed through trend analysis. Survival analysis results demonstrated that SNORA70D was significantly associated with the overall survival of EC patients (P=0.034). In addition, a co-regulation network that included six snoRNAs and 29 mRNAs was constructed. A ceRNA network comprised of five SNHGs, 11 mRNAs, and 38 miRNAs was constructed based on the top 50 relationship pairs. KEGG pathway enrichment analysis revealed that SNORA14B, SNORA47, SNORA71C, SNORD12B, and SNORD14E in the co-expression network were mainly enriched in the NOD−like receptor signaling pathway, and neuroactive ligand−receptor interaction pathway. SNHG23, SNHG3, SNHG17, SNHG4, and SNHG8 in the ceRNA regulation network were mainly enriched in calcium signaling pathway, cytokine−cytokine receptor interaction, and the extracellular matrix−receptor interaction pathway.Conclusion: The data revealed a series of snoRNAs, SNHGs, and pathways involved in EC carcinogenesis, which will provide a basis for understanding the underlying molecular mechanism of EC development.

Background: This study aimed to investigate the role of small nucleolar RNAs (snoRNAs) and their host genes (SNHGs) in tumorigenesis and progression of esophageal carcinoma (EC). Methods: RNA-seq data and clinical information for EC patients were obtained from The Cancer Genome Atlas database. Differentially expressed snoRNAs (DE-snoRNAs) and SNHGs (DE-SNHGs) between EC samples and normal controls were screened with the edgeR package, followed by the trend analysis with STEM. Survival analysis was performed using the Kaplan–Meier method with the log-rank test. Identification of co-expressed genes and functional enrichment analyses were performed with Pearson’s correlation analysis and enrichment analysis, respectively. Lastly, the co-expression and ceRNA networks were constructed. Results: A total of 19 DE-snoRNAs and nine DE-SNHGs were identified between EC samples and normal controls. Moreover, two snoRNA clusters related to the clinic stage were identified through trend analysis. Survival analysis results demonstrated that SNORA70D was significantly associated with the overall survival of EC patients (P=0.034). In addition, a co-regulation network that included six snoRNAs and 29 mRNAs was constructed. A ceRNA network comprised of five SNHGs, 11 mRNAs, and 38 miRNAs was constructed based on the top 50 relationship pairs. KEGG pathway enrichment analysis revealed that SNORA14B, SNORA47, SNORA71C, SNORD12B, and SNORD14E in the co-expression network were mainly enriched in the NOD−like receptor signaling pathway, and neuroactive ligand−receptor interaction pathway. SNHG23, SNHG3, SNHG17, SNHG4, and SNHG8 in the ceRNA regulation network were mainly enriched in calcium signaling pathway, cytokine−cytokine receptor interaction, and the extracellular matrix−receptor interaction pathway. Conclusion: The data revealed a series of snoRNAs, SNHGs, and pathways involved in EC carcinogenesis, which will provide a basis for understanding the underlying molecular mechanism of EC development.

Tumor organoids recapitulate pathological properties and would serve as an excellent ex vivo model for drug discovery. Here, we performed an unbiased drug screening on drivers-defined tumor organoids from mouse endometrial cancer, the most prevalent gynecological malignancy in human, with a small molecule library targeting epigenetic factors. Among them, menin-MLL inhibitors MI-136 and MI-463 scored. The therapeutic capacity of MI-136 was further validated in tumor organoids in vitro and an orthotopic model in vivo. CRISPR/cas9-mediated mutations of major components of the menin-MLL complex, Men1, Kmt2a and Ash2l, inhibited the growth of tumor organoids, suggesting that the complex was the target of MI-136. Transcriptome analysis showed that the hypoxia-inducible factor (HIF) pathway was the most significantly downregulated pathway by MI-136 treatment. Consistently, Men1, Kmt2a, and Ash2l knockout also repressed the expressions of the HIF target genes. Loss of Hif1a or Hif1b partially phenocopied the inhibition of the menin-MLL complex by MI-136 or mutations in term of tumor organoid growth. Further, we found that MEN1 was upregulated in human endometrial cancers, which were tightly correlated with the expression levels of HIF1A, and associated with poor prognosis. Importantly, MI-136 also significantly inhibited the growth of endometrial cancer organoids derived from patients. Thus, our study identified MI-136 as a potential inhibitor for endometrial cancer through regulating the HIF pathway, a novel molecular mechanism distinguished from those in AML and prostate cancer.

Objective To achieve universal access to medical resources—a partial goal of the second ambitious health reform since 2010—the Chinese government aimed to build a regional medical consortium and enhance the efficiency of health information exchange (HIE). We analyzed the experience of constructing a medical consortium in Chinese hospitals, which was based on regional health information technology (RHIT) promoted by HIE. Method In this longitudinal study, we analyzed the results of the annual surveys that were conducted by the China Hospital Information Management Association from 2006 to 2015. The survey results mainly concerned whether hospitals should join the regional medical consortium, the methods used for sharing inter-hospital medical data, and the out-of-hospital information interaction system. The Bass diffusion model was adopted to fit and predict the proportion of Chinese hospitals joining the consortium from 2006 to 2025. Result As of 2015, the survey results of 7272 hospitals were obtained. The proportion of hospitals in partnership systems increased from 3.0% in 2007 to 57.2% in 2015. There has been a rapid development in the electronic sharing of medical data between hospitals. The proportion of hospitals that relied solely on paper documents for data interaction decreased from 43.3% in 2011 to 8.0% in 2015. There was a strong positive linear correlation between hospitals joining the consortium and the accessibility of electronic medical data exchange within hospitals (r = 0.925). The proportions of hospitals that supported dual referral systems and appointments, data browsing between hospitals and regional information systems, and remote consultation services increased to 65.0%, 61.6%, and 81.9% in 2015, as compared to 18.8%, 16.8%, and 10.9% in 2011, respectively. The Bass prediction model showed that the goal of recruiting 90% of the hospitals to the consortium by 2020 will likely be achieved (adjusted R2 = 0.93). Conclusion The Chinese government has applied a top-down, high-level design model to promote the rapid development of a medical consortium, in which the RHIT technologies are crucial technical enabler.