Pharmacological modulation of RB1 activity mitigates resistance to neoadjuvant chemotherapy in locally advanced rectal cancer.

Resistance to neoadjuvant chemotherapy leads to poor prognosis of locally advanced rectal cancer (LARC), representing an unmet clinical need that demands further exploration of therapeutic strategies to improve clinical outcomes. Here, we identified a noncanonical role of RB1 for modulating chromatin activity that contributes to oxaliplatin resistance in colorectal cancer (CRC). We demonstrate that oxaliplatin induces RB1 phosphorylation, which is associated with the resistance to neoadjuvant oxaliplatin-based chemotherapy in LARC. Inhibition of RB1 phosphorylation by CDK4/6 inhibitor results in vulnerability to oxaliplatin in both intrinsic and acquired chemoresistant CRC. Mechanistically, we show that RB1 modulates chromatin activity through the TEAD4/HDAC1 complex to epigenetically suppress the expression of DNA repair genes. Antagonizing RB1 phosphorylation through CDK4/6 inhibition enforces RB1/TEAD4/HDAC1 repressor activity, leading to DNA repair defects, thus sensitizing oxaliplatin treatment in LARC. Our study identifies a RB1 function in regulating chromatin activity through TEAD4/HDAC1. It also provides the combination of CDK4/6 inhibitor with oxaliplatin as a potential synthetic lethality strategy to mitigate oxaliplatin resistance in LARC, whereby phosphorylated RB1/TEAD4 can serve as potential biomarkers to guide the patient stratification.

IGSF6 is a novel biomarker to evaluate immune infiltration in mismatch repair-proficient colorectal cancer.

Immunotherapy has dramatically changed the landscape of treatment for colorectal cancer (CRC), but currently lack of effective predictive biomarker, especially for tumors with mismatch repair (MMR) proficiency. The response of immunotherapy is associated with the cell-cell interactions in tumor microenvironment, encompassing processes such as cell-cell recognition, binding, and adhesion. However, the function of immunoglobulin superfamily (IGSF) genes in tumor immune microenvironment remains uncharacterized. This study quantified the immune landscape by leveraging a gene expression matrix from publicly accessible databases. The associations between IGSF6 gene expression and immune cell infiltration were assessed. The expression levels of IGSF6, CD8+ T cells, CD4+ T cells and CD68+ macrophage cells in cancer tissues from CRC patients and CRC cell lines were evaluated. IGSF6 was more highly expressed in CRC tumor tissues than adjacent normal tissues. And IGSF6 was significantly correlated with immune cell infiltration in MMR-proficient patients. Remarkably, MMR-proficient patients with high IGSF6 expression showed more sensitive to immunotherapy and chemotherapy than those with low IGSF6 expression. In summary, IGSF6 could be a novel biomarker to evaluate immune infiltration and predict therapeutic effect for MMR-proficient CRC.

Proteomic profiling and functional characterization of serum-derived extracellular vesicles in the mucinous and non-mucinous colon adenocarcinoma.

PURPOSE: Mucinous adenocarcinoma (MC) is a distinct pathological subtype of colon adenocarcinoma, which is associated with a worse prognosis compared with non-mucinous adenocarcinoma (AC). However, clear distinctions between MC and AC remain unknown. Extracellular vesicles (EVs) are a class of enclosed vesicles containing proteins, lipids, and nucleic acids that are secreted by cells into surrounding tissues or into serum. The EVs could facilitate tumorigenesis by regulating tumor cell proliferation, invasiveness, metastasis, angiogenesis, and evasion of immune surveillance. METHODS: Quantitative proteomics analysis was performed to determine the characterization and biological differences of serum-derived EVs in two subtypes of colon adenocarcinoma (MC and AC). Serum-derived EVs from patients with MC, AC, and healthy volunteers were included in this study. The role of PLA2G2A in cell migration and invasion were evaluate with transwell assay, and its prognostic predictive value was further assessed based on TCGA database. RESULTS: Quantitative proteomics analysis revealed 846 differentially expressed proteins (DEPs) in EVs from MC patients compared with those from AC patients. Bioinformatics analysis revealed that the most prominent protein cluster included those involved in cell migration and the tumor microenvironment. Overexpression of PLA2G2A, one of the key EV proteins upregulated in patients with MC, in colon cancer cell line SW480 promoted the cell invasion and migration ability. In addition, the high level of PLA2G2A is associated with poor prognosis of colon cancer patients harboring BRAF mutations. Further, after EV stimulation, proteomic analysis of recipient SW480 cells showed that MC-derived EVs activated multiple cancer-related pathways, including the Wnt/ß-Catenin signaling pathway, and might promote the malignancy of mucinous adenocarcinoma through these pathways. CONCLUSIONS: The identification of differential protein profiles between MC and AC helps to elucidate the underlying molecular mechanisms of MC pathogenesis. The PLA2G2A in EVs is a potential prognostic predictive marker for those patients harboring with BRAF mutations.

An immune, stroma, and epithelial-mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II-III colorectal cancer.

BACKGROUND: Debates exist on the treatment decision of the stage II/III colorectal cancer (CRC) due to the insufficiency of the current TNM stage-based risk stratification system. Epithelial-mesenchymal transition (EMT) and tumor microenvironment (TME) have both been linked to CRC progression in recent studies. We propose to improve the prognosis prediction of CRC by integrating TME and EMT. METHODS: In total, 2382 CRC patients from seven datasets and one in-house cohort were collected, and 1640 stage II/III CRC patients with complete survival information and gene expression profiles were retained and divided into a training cohort and three independent validation cohorts. Integrated analysis of 398 immune, stroma, and epithelial-mesenchymal transition (ISE)-related genes identified an ISE signature independently associated with the recurrence of CRC. The underlying biological mechanism of the ISE signature and its influence on adjuvant chemotherapy was further explored. RESULTS: We constructed a 26-gene signature which was significantly associated with poor outcome in Training cohort (p < 0.001, HR [95%CI] = 4.42 [3.25-6.01]) and three independent validation cohorts (Validation cohort-1: p < 0.01, HR [95%CI] = 1.70 [1.15-2.51]; Validation cohort-2: p < 0.001, HR [95% CI] = 2.30 [1.67-3.16]; Validation cohort-3: p < 0.01, HR [95% CI] = 2.42 [1.25-4.70]). After adjusting for known clinicopathological factors, multivariate cox analysis confirmed the ISE signature's independent prognostic value. Subgroup analysis found that stage III patients with low ISE score might benefit from adjuvant chemotherapy (p < 0.001, HR [95%CI] = 0.15 [0.04-0.55]). Hypergeometric test and enrichment analysis revealed that low-risk group was enriched in thr immune pathway while high-risk group was associated with the EMT pathway and CMS4 subtype. CONCLUSION: We proposed an ISE signature for robustly predicting the recurrence of stage II/III CRC and help treatment decision by identifying patients who will not benefit from current standard treatment.

CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study.

BACKGROUND: This study aimed to develop a radiogenomic prognostic prediction model for colorectal cancer (CRC) by investigating the biological and clinical relevance of intratumoural heterogeneity. METHODS: This retrospective multi-cohort study was conducted in three steps. First, we identified genomic subclones using unsupervised deconvolution analysis. Second, we established radiogenomic signatures to link radiomic features with prognostic subclone compositions in an independent radiogenomic dataset containing matched imaging and gene expression data. Finally, the prognostic value of the identified radiogenomic signatures was validated using two testing datasets containing imaging and survival information collected from separate medical centres. RESULTS: This multi-institutional retrospective study included 1601 patients (714 females and 887 males; mean age, 65 years ± 14 [standard deviation]) with CRC from 5 datasets. Molecular heterogeneity was identified using unsupervised deconvolution analysis of gene expression data. The relative prevalence of the two subclones associated with cell cycle and extracellular matrix pathways identified patients with significantly different survival outcomes. A radiogenomic signature-based predictive model significantly stratified patients into high- and low-risk groups with disparate disease-free survival (HR = 1.74, P = 0.003). Radiogenomic signatures were revealed as an independent predictive factor for CRC by multivariable analysis (HR = 1.59, 95% CI:1.03-2.45, P = 0.034). Functional analysis demonstrated that the 11 radiogenomic signatures were predominantly associated with extracellular matrix and immune-related pathways. CONCLUSIONS: The identified radiogenomic signatures might be a surrogate for genomic signatures and could complement the current prognostic strategies.

Predicting prognosis and immunotherapy response among colorectal cancer patients based on a tumor immune microenvironment-related lncRNA signature.

Long non-coding RNAs (lncRNAs) remodel the tumor immune microenvironment (TIME) by regulating the functions of tumor-infiltrating immune cells. It remains uncertain the way that TIME-related lncRNAs (TRLs) influence the prognosis and immunotherapy response of colorectal cancer (CRC). Aiming at providing survival and immunotherapy response predictions, a CRC TIME-related lncRNA signature (TRLs signature) was developed and the related potential regulatory mechanisms were explored with a comprehensive analysis on gene expression profiles from 97 immune cell lines, 61 CRC cell lines and 1807 CRC patients. Stratifying CRC patients with the TRLs signature, prolonged survival was observed in the low-risk group, while the patients in the high-risk group had significantly higher pro-tumor immune cells infiltration and higher immunotherapy response rate. Through the complex TRLs-mRNA regulation network, immunoregulation pathways and immunotherapy response pathways were found to be differently activated between the groups. In conclusion, the CRC TRLs signature is capable of making prognosis and immunotherapy response predictions, which may find application in stratifying patients for immunotherapy in the bedside.

A Transcription Factor Signature Can Identify the CMS4 Subtype and Stratify the Prognostic Risk of Colorectal Cancer.

Background: Colorectal cancer (CRC) is a heterogeneous disease, and current classification systems are insufficient for stratifying patients with different risks. This study aims to develop a generalized, individualized prognostic consensus molecular subtype (CMS)-transcription factors (TFs)-based signature that can predict the prognosis of CRC. Methods: We obtained differentially expressed TF signature and target genes between the CMS4 and other CMS subtypes of CRC from The Cancer Genome Atlas (TCGA) database. A multi-dimensional network inference integrative analysis was conducted to identify the master genes and establish a CMS4-TFs-based signature. For validation, an in-house clinical cohort (n = 351) and another independent public CRC cohort (n = 565) were applied. Gene set enrichment analysis (GSEA) and prediction of immune cell infiltration were performed to interpret the biological significance of the model. Results: A CMS4-TFs-based signature termed TF-9 that includes nine TF master genes was developed. Patients in the TF-9 high-risk group have significantly worse survival, regardless of clinical characteristics. The TF-9 achieved the highest mean C-index (0.65) compared to all other signatures reported (0.51 to 0.57). Immune infiltration revealed that the microenvironment in the high-risk group was highly immune suppressed, as evidenced by the overexpression of TIM3, CD39, and CD40, suggesting that high-risk patients may not directly benefit from the immune checkpoint inhibitors. Conclusions: The TF-9 signature allows a more precise categorization of patients with relevant clinical and biological implications, which may be a valuable tool for improving the tailoring of therapeutic interventions in CRC patients.

Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images.

Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised framework named SOUSA (Segmentation Only Uses Sparse Annotations), aiming at learning from a small set of sparse annotated data and a large amount of unlabeled data. The proposed framework contains a teacher model and a student model. The student model is weakly supervised by scribbles and a Geodesic distance map derived from scribbles. Meanwhile, a large amount of unlabeled data with various perturbations are fed to student and teacher models. The consistency of their output predictions is imposed by Mean Square Error (MSE) loss and a carefully designed Multi-angle Projection Reconstruction (MPR) loss. Extensive experiments are conducted to demonstrate the robustness and generalization ability of our proposed method. Results show that our method outperforms weakly- and semi-supervised state-of-the-art methods on multiple datasets. Furthermore, our method achieves a competitive performance with some fully supervised methods with dense annotation when the size of the dataset is limited.

Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer.

Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations. Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging. Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4-90.6, p < 0.001; validation cohort: HR: 2.04, 95% CI: 1.44-2.89, p < 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8-69.3, p < 0.001; validation cohort: HR: 1.83, 95% CI: 1.19-2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways. Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

DNA Repair-Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients.

Background: Increasing evidence have depicted that DNA repair-related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair-related gene signature (DRGS) in predicting the prognosis of CRC patients. Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set. Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67-3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38-3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40-3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best. Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy.

The growth pattern of liver metastases on MRI predicts early recurrence in patients with colorectal cancer: a multicenter study.

OBJECTIVES: The multicenter study aimed to explore the relationship between the growth pattern of liver metastases on preoperative MRI and early recurrence in patients with colorectal cancer liver metastases (CRCLM) after surgery. METHODS: A total of 348 CRCLM patients from 3 independent centers were enrolled, including 130 patients with 339 liver metastases in the primary cohort and 218 patients in validation cohorts. Referring to the gross classification of hepatocellular carcinoma (HCC), the growth pattern of each liver metastasis on MRI was classified into four types: rough, smooth, focal extranodular protuberant (FEP), and nodular confluent (NC). Disease-free survival (DFS) curve was constructed using the Kaplan-Meier method. RESULTS: In primary cohort, 42 (12.4%) of the 339 liver metastases were rough type, 237 (69.9%) were smooth type, 29 (8.6%) were FEP type, and 31 (9.1%) were NC type. Those patients with FEP- and/or NC-type liver metastases had shorter DFS than those without such metastases (p < 0.05). However, there were no significant differences in DFS between patients with rough- and smooth-type liver metastases and those without such metastases. The patients with FEP- and/or NC-type liver metastases also had shorter DFS than those without such metastases in two external validation cohorts. In addition, 40.5% of high-risk-type (FEP and NC) liver metastases converted to low-risk types (rough and smooth) after neoadjuvant chemotherapy. CONCLUSION: The FEP- and NC-type liver metastases were associated with early recurrence, which may facilitate the clinical treatment of CRCLM patients. KEY POINTS: • In the primary cohort, patients with FEP- and NC-type metastases had shorter disease-free survival (DFS) and a higher intrahepatic recurrence rate than patients without such metastases in the liver. • In the primary cohort, there were no significant differences in DFS or intrahepatic recurrence rate between patients with rough- and smooth-type metastases and those without such metastases in the liver. • High-risk patients had shorter DFS and a higher intrahepatic recurrence rate than low-risk patients in primary and external validation cohorts.

Comprehensive Proteomic Analysis of Colon Cancer Tissue Revealed the Reason for the Worse Prognosis of Right-Sided Colon Cancer and Mucinous Colon Cancer at the Protein Level.

To clarify the molecular mechanisms underlying the poor prognosis of right-sided and mucinous colon cancer at the proteomic level. A tandem mass tag-proteomics approach was used to identify differentially expressed proteins (DEPs) in colon carcinoma tissues from different locations and with different histological types to reveal the underlying mechanisms of these differences at the protein level. In additional, the DEPs were analyzed using bioinformatics methods. The proteomics profiles among colon cancers with different tumor locations and histological types were dramatically distinguished. In terms of tumor locations, the right-sided carcinoma specific DEPs may promote the tumor progression via activating inflammation, metastasis associated pathways. When referring to histological types, the mucinous colon cancers perhaps increased the invasion and metastasis through distinct mechanisms in different tumor locations. For mucinous cancer located in right-sided colon, the mucinous specific DEPs were mainly associated with ECM-related remodeling and the IL-17 signal pathway. For mucinous cancer located in left-sided colon, the mucinous specific DEPs showed a strong relationship with ACE2/Ang-(1-7)/MasR axis. The proteomics profiles of colon cancers showed distinct differences related to locations and histological types. These results suggested a distinct mechanism underlying the diverse subtypes of colon cancers.

[Recent Advances on the Differences between Left- and Right-sided Colorectal Cancer].

Right-sided colon cancer and left-sided colorectal cancer have significant differences in epidemiology,clinical features,tumor differentiation,response to treatment,prognosis,and molecular characteristics.The former has lower prevalence than the latter and is mainly associated with female and elderly patients,with poor tumor differentiation,strong invasion,poor prognosis,and weak response to epidermal growth factor receptor inhibitors.Thus,it is generally believed that the primary location of colorectal cancer is closely associated with prognosis,acting as an independent prognostic factor for therapeutic efficacy.Recent studies have revealed the genetic differences between right-sided colon cancer and left-sided colorectal cancer,providing explanations for the biological differences.This review summarizes the recent advances on the differences between left-and right-sided colorectal cancer.

A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer.

Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC). Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator algorithm (LASSO). A nomogram was constructed by combining the radiomics score with clinicopathological risk factors for predicting the prognosis of CRC patients. The performance of the nomogram was evaluated by the calibration curve, receiver operating characteristic (ROC) curve and C-index statistics. Functional analysis and correlation analysis were used to explore the underlying association between radiomic feature and the gene-expression patterns. Results: Five radiomic features were selected to calculate the radiomics score by using the LASSO regression model. The Kaplan-Meier analysis showed that radiomics score was significantly associated with disease-free survival (DFS) [primary cohort: hazard ratio (HR): 5.65, 95% CI: 2.26-14.13, P < 0.001; validation cohort: HR: 8.49, 95% CI: 2.05-35.17, P < 0.001]. Multivariable analysis confirmed the independent prognostic value of radiomics score (primary cohort: HR: 5.35, 95% CI: 2.14-13.39, P < 0.001; validation cohort: HR: 5.19, 95% CI: 1.22-22.00, P = 0.026). We incorporated radiomics signature with the TNM stage to build a nomogram, which performed better than TNM stage alone. The C-index of the nomogram achieved 0.74 (0.69-0.80) in the primary cohort and 0.82 (0.77-0.87) in the validation cohort. Functional analysis and correlation analysis found that the radiomic signatures were mainly associated with metabolism related pathways. Conclusions: The radiomics score derived from the preoperative CT image was an independent prognostic factor and could be a complement to the current staging strategies of colorectal cancer.

Serum extracellular vesicles contain SPARC and LRG1 as biomarkers of colon cancer and differ by tumour primary location.

BACKGROUND: Recently, the distinction between left- and right-sided colon cancer (LCC and RCC) has been brought into focus. RCC is associated with an inferior overall survival and progression-free survival. We aimed to perform a detailed analysis of the diversity of extracellular vesicles (EV) between LCC and RCC using quantitative proteomics and to identify for new diagnostic and prognostic biomarkers. METHODS: We isolated EVs from patients with LCC, RCC and healthy volunteers, and treated colorectal cancer cell line with serum-derived EVs. We then performed a quantitative proteomics analysis of the serum-derived EVs and cell line treated with EVs. Proteomic data are available via ProteomeXchange with the identifiers PXD012283 and PXD012304. In addition, we assessed the performance of EV SPARC and LRG1 as diagnosis and prognosis biomarkers in colon cancer. FINDINGS: The expression profile of the serum EV proteome in patients with RCC was different from that of patients with LCC. Serum-derived EVs in RCC promoted cellular mobility more significantly than EVs derived from LCC. EV SPARC and LRG1 expression levels demonstrated area under the receiver-operating characteristic curve values of 0.95 and 0.93 for discriminating patients with colon cancer from healthy controls. Moreover, the expression levels of SPARC and LRG1 correlated with tumour sidedness and were predictive of tumour recurrence. INTERPRETATION: We identified differences in EV protein profiles between LCC and RCC. Serum-derived EVs of RCC may promote metastasis via upregulation of extracellular matrix (ECM)-related proteins, especially SPARC and LRG1, which may serve as diagnosis and prognosis biomarkers in colon cancer.

LncRNA H19 regulates PI3K-Akt signal pathway by functioning as a ceRNA and predicts poor prognosis in colorectal cancer: integrative analysis of dysregulated ncRNA-associated ceRNA network.

BACKGROUND: It is becoming increasingly clear that cancers can rarely be ascribed to just one or a few genomic variations. Genes generally do not function alone, but in groups that function as "networks". This study aimed to develop a competing endogenous RNA (ceRNA) network to elucidate the role of long non-coding RNA H19 in colorectal cancer. METHODS: Large-scale RNA-seq data was obtained from The Cancer Genome Atlas database. Differentially expressed RNAs were identified by bioinformatics analysis, and a competing endogenous RNA network was constructed. Functional enrichment analysis and correlation analysis between competing endogenous RNAs and clinical features were performed to reveal their roles in the tumorigenesis of colorectal cancer. To verify the conclusions derived from bioinformatics analysis, we investigated the effect of lncRNA H19 knockdown in human colorectal cancer cell lines HT-29 and HCT116. RESULTS: The present study successfully identify various cancer-specific lncRNAs and pseudogenes in CRC. The lncRNA/pseudogene-miRNA-mRNA ceRNA network was constructed using 10 lncRNAs, 5 pseudogenes, 122 mRNAs and 39 miRNAs. In the ceRNA network of CRC, H19 up-regulates various cancer-related mRNA by competitively sponging various miRNA, and participates in PI3K-Akt signaling pathway in this manner. Cox regression and correlation analysis showed that H19 and some other competing endogenous RNAs in the network are associated with poor prognosis and clinical parameters such as tumor grade and metastasis. Knockdown of H19 reduces the protein level of MET, ZEB1, and COL1A1 in vitro. CONCLUSIONS: H19 regulates PI3K-Akt signal pathway through a competing endogenous RNA network and predicts poor prognosis in colorectal cancer. The pseudogene RPLP0P2 may be an important oncogene like H19 and needs to be studied further.

Kyphoscoliosis with Klippel-Trenaunay syndrome: a case report and literature review.

BACKGROUND: Klippel-Trenaunay syndrome (KTS) is a rare congenital syndrome characterized by the triad of venous varicosities, capillary malformations and limb hypertrophy. However, KTS may rarely occur in combination with kyphoscoliosis. CASE PRESENTATION: We presented an 18-year-old female with KTS and kyphoscoliosis. Hypertrophy of bone and soft tissue affected her left face, trunk and lower limb. Moreover, the patient is associated with subacute thyroiditis, vitamin D deficiency and iron deficiency anemia, high level of D-dimer, swollen tonsil, kyphoscoliosis and Chiari-I-malformation without syringomyelia. A posterior correction and spinal fusion from T10 to L5 levels were performed for this patient. The lumbar curve was corrected from 105° to 60° and the kyphosis improved from 58° to 26°. The distance of trunk shift decreased from 10 cm to 1.4 cm. There were no thrombotic events occurred. At the 8th month follow-up, there was no significantly change of the curve in the coronal and sagittal radiographs. During the 31-month follow-up, the patient did not experience any discomfort. And her general appearance did not have any change until the last follow-up. However, she refused to take radiograph for worrying about radiation. CONCLUSIONS: KTS is a rare disease with classic clinical triad. However, it can also have other different features, including kyphoscoliosis, elevated D-Dimer, vitamin D deficiency and iron-deficiency anemia. These issues should be taken into consideration when planning treatment for kyphoscoliosis in KTS patients.

Loop ostomy following laparoscopic low anterior resection for rectal cancer after neoadjuvant chemoradiotherapy.

BACKGROUND: Both loop ileostomy (LI) and loop transverse colostomy (LTC) could achieve absolute fecal diversion and have several advantages. This study compared LI and LTC following laparoscopic low anterior resection for rectal cancer after neoadjuvant chemoradiotherapy. METHODS: Between January 2009 and December 2016, 186 patients who underwent laparoscopic low anterior resection for rectal cancer and loop ostomy were included. All patients received preoperative neoadjuvant chemoradiotherapy. Of these, 77 underwent LI and 109 underwent LTC. Demographic characteristics, operative details, and complications were analyzed. RESULTS: In the fecal diversion period, the LTC group showed significantly less dermatitis (p = 0.001) and electrolyte disturbance (p = 0.002), while LI group showed significantly shorter time to first defecation (p = 0.006) and lower incidence of parastomal hernia (p = 0.014). In the stoma closure period, a significantly higher incidence of wound infection was found in LTC group (p = 0.001). CONCLUSIONS: Both LI and LTC have advantages and disadvantages. For its lower wound infection rate, lower incidence of parastomal hernia, and shorter time to first defecation, LI is recommended for all patients except those with potential electrolyte disturbance and sensitive skin.

A novel heterozygous germline deletion in MSH2 gene in a five generation Chinese family with Lynch syndrome.

Lynch syndrome (LS) is one of the most common familial forms of colorectal cancer predisposing syndrome with an autosomal dominant mode of inheritance. LS is caused by the germline mutations in DNA mismatch repair (MMR) genes including MSH2, MLH1, MSH6 and PMS2. Clinically, LS is characterized by high incidence of early-onset colorectal cancer as well as endometrial, small intestinal and urinary tract cancers, usually occur in the third to fourth decade of the life. Here we describe a five generation Chinese family with LS clinically diagnosed according to the Amsterdam II criteria. Immuno-histochemical staining of MSH2 and MSH6 shows only foci nuclear positive on the surface of the tumor with strong expression of MLH1 and PMS2 with diffuse immunoreactivity. In order to dig into the molecular basis of this LS pedigree, we collected the proband's blood sample, extracted the genomic DNA and applied the genetic screening. As a result, we identified a novel heterozygous deletion in MSH2 gene by targeted next generation sequencing, which is also proved to be co-segregated among other affected family members by following validation. To our knowledge, this novel heterozygous deletion (c.1676_1679 delTAAA) in MSH2 gene causes frameshift mutation (p.Asn560Lysfs*29) and leads to the formation of a truncated MSH2 protein which is confirmed to be a deleterious mutation according to the variant interpretation guidelines of American College of Medical Genetics and Genomics (ACMG). Identification of novel DNA mismatch repair (MMR) gene mutations can definitely benefit to the clinical diagnosis and management.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.