The RE1 Silencing Transcription Factor (REST) is a major regulator of neurogenesis and brain development. Medulloblastoma (MB) is a pediatric brain cancer characterized by a blockade of neuronal specification. REST gene expression is aberrantly elevated in a subset of MBs that are driven by constitutive activation of sonic hedgehog (SHH) signaling in cerebellar granular progenitor cells (CGNPs), the cells of origin of this subgroup of tumors. To understand its transcriptional deregulation in MBs, we first studied control of Rest gene expression during neuronal differentiation of normal mouse CGNPs. Higher Rest expression was observed in proliferating CGNPs compared to differentiating neurons. Interestingly, two Rest isoforms were expressed in CGNPs, of which only one showed a significant reduction in expression during neurogenesis. In proliferating CGNPs, higher MLL4 and KDM7A activities opposed by the repressive polycomb repressive complex 2 (PRC2) and the G9A/G9A-like protein (GLP) complex function allowed Rest homeostasis. During differentiation, reduction in MLL4 enrichment on chromatin, in conjunction with an increase in PRC2/G9A/GLP/KDM7A activities promoted a decline in Rest expression. These findings suggest a lineage-context specific paradoxical role for KDM7A in the regulation of Rest expression in CGNPs. In human SHH-MBs (SHH-α and SHH-β) where elevated REST gene expression is associated with poor prognosis, up- or downregulation of KDM7A caused a significant worsening in patient survival. Our studies are the first to implicate KDM7A in REST regulation and in MB biology.

Tumor-specific protein 70 (SP70) was identified as a new biomarker associated with the proliferation and invasion of cancer cells. This study aimed to investigate the expression of SP70 in hepatocellular carcinoma (HCC) and assess its clinical value in the diagnosis and prediction of early HCC recurrence.

Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68-0.79] and 0.70 [95% CI 0.62-0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58-0.70] and 0.65 [95% CI 0.55-0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.

Cholangiocarcinoma (CCA), including intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA) CCA, is a highly aggressive malignancy originating from bile duct. The prognosis of CCA is very poor, and the biomarker study is unsatisfactory compared with other common cancers.

Long noncoding RNAs (lncRNAs) have recently received growing substantial attention in cancer research due to their important roles in various cancer types. However, the underlying mechanisms and functions of lncRNAs, especially in lung adenocarcinoma (LUAD), remain elusive. Based on pan-cancer screening analyses, we identified that the noncoding RNA LINC00665 was up-regulated in lung adenocarcinoma, which was subsequently confirmed in clinical samples and cell lines. Higher expression of LINC00665 was positively associated with poor prognosis and advanced T stage. Next, using gain- and loss- of function approaches, we revealed that LINC00665 promotes cell proliferation, cell migration, invasion, and suppresses cell apoptosis in LUAD through in vitro and in vivo experiments. Additionally, our findings showed that LINC00665 was predominately localized in the cytoplasm so as to interact with Ago2 protein, which could function as miRNA sponges. The results of bioinformatics prediction and RNA pull-down assay indicated that LINC00665 directly interacted with miR-195-5p. This was also confirmed by fluorescence colocalization. Furthermore, luciferase reporter assay demonstrated that Myc binding protein (MYCBP, also called AMY-1), which enhanced c-Myc transcriptional activity, was the target gene of LINC00665 dependent on miR-195-5p. Finally, rescue functional assay results uncovered that the oncogenic capability of LINC00665 was dependent on miR-195-5p and c-Myc transcriptional activity. In summary, this work elucidates that LINC00665 accelerates LUAD progression via the miR-195-5p/MYCBP axis by acting as a competing endogenous RNA (ceRNA), suggesting that LINC00665 may represent a potential therapeutic target for clinical intervention of LUAD.

Esophageal squamous cell carcinoma (ESCC) is one of the most frequently diagnosed cancers in the world with a high mortality rate. The mechanism about ESCC development and whether miRNAs play a critical role remains unclear and needs carefully elucidated.

Background: The immune landscape of hepatocellular carcinoma (HCC) is heterogeneous. This study aims to develop the immune type which could improve predictive value of HCC survival. Methods: A total of 208 HCC patients in the testing cohort, 112 patients in the validation cohort and 365 HCC patients in the TCGA database were included in this study. Immune features were assessed by immunohistochemical staining or CIBERSORT method. We constructed prognostic classifiers by LASSO COX analyses in the TCGA cohort, which identified five features out of the 22 types of immune cells. Results: The formulas based on the immunohistochemical staining are as follows: ISOS = 0.648* Macrophagestromal + 0.444*Neutrophilsstromal + 0.218*Tregsstromal - 0.703*Memory T cellsstromal; ISDFS = 0.285*B cellsstromal + 0.494*Neutrophilsstromal + 0.431*Tregsstromal - 0.736*Memory T cellsstromal. We classified HCC patients into immune type A subgroup (IS-A) and type B subgroup (IS-B) based on immune scores. The immune type was an independent prognostic indicator for disease-free survival (DFS) and overall survival (OS) in both testing and validation cohorts. Two nomograms (for OS and DFS) that integrated the immune type and clinicopathologic risk factors also showed good predictive accuracy and discriminatory power. IS-A group was correlated with higher immune checkpoint molecule expression. In addition, patients with IS-A and IS-B had distinct mutation signature. Conclusion: The immune types could predict survival and recurrence of HCC effectively. In addition, the immunosuppressive pathways and mutation signature are distinct between two immune types.

The treatment of melanoma has remained a difficult challenge. Targeting the tumor stroma has recently attracted attention for developing novel strategies for melanoma therapy. Activating transcription factor 3 (ATF3) plays a crucial role in regulating tumorigenesis and development, but whether the expression of ATF3 in human dermal fibroblasts (HDFs) can affect melanoma development hasn't been studied. Our results show that ATF3 expression is downregulated in stromal cells of human melanoma. HDFs expressing high levels of ATF3 suppressed the growth and migration of melanoma cells in association with downregulation of different cytokines including IL-6 in vitro. In vivo, HDFs with high ATF3 expression reduced tumor formation. Adding recombinant IL-6 to melanoma cells reversed those in vitro and in vivo effects, suggesting that ATF3 expression by HDFs regulates melanoma progression through the IL-6/STAT3 pathway. More importantly, HDFs pretreated with cyclosporine A or phenformin to induce ATF3 expression inhibited melanoma cell growth in vitro and in vivo. In summary, our study reveals that ATF3 suppresses human melanoma growth and that inducing the expression of ATF3 in HDFs can inhibit melanoma growth, a new potential melanoma therapeutic approach.

FAS-associated death structural domain (FADD) proteins are important proteins that regulate apoptosis and are also involved in many nonapoptotic pathways in tumors. However, how dysregulated FADD affects the development of lung adenocarcinoma (LUAD) remains unknown.

Motor neuron and pancreas homeobox 1 (MNX1) is a development-related genes and has been found to be highly expressed in several cancers. However, its biological function in cervical cancer remains largely unexplored. QRT-PCR, western blot, and IHC showed that MNX1 was abnormally overexpressed in cervical cancer tissues and cell lines. The high expression level of MNX1 correlated with poorer clinicopathologic characteristics in cervical cancer patients. Evaluated by RTCA (Real Time Cellular Analysis) proliferation assay, colony formation assay, EdU assay, transwell assay, and matrigel assay, we found that knockdown of MNX1 inhibited proliferation, migration and invasion of cervical cancer in vitro, while overexpression of MNX1 promoted malignant phenotype of cervical cancer. And subcutaneous xenograft model confirmed the malignant phenotype of MNX1 in vivo. Furthermore, flow cytometry, chromatin immunoprecipitation, and luciferase reporter assay indicated that MNX1 accelerated cell cycle transition by transcriptionally downregulating cyclin-dependent kinases p21cip1. In summary, our study revealed that MNX1 exerted an oncogenic role in cervical cancer via repressing the transcription of p21cip1 and thus accelerating cell cycle progression. Our results suggested that MNX1 was a potential diagnostic marker and therapeutic target for cervical cancer patients.

Collagens are major components of the ECM in various organs, including the lungs. Ectopic expression of collagens can regulate the tumor progression and disease outcome through remodeling of the extracellular matrix (ECM). However, it remains largely unexplored whether collagens are involved in the tumor progression of lung adenocarcinoma (LUAD). Analysis of three LUAD transcriptional expression profiles showed that COL10A1 mRNA expression was up-regulated and associated with poor prognosis. Gain- and loss-of-function studies were performed to observe that up-regulated COL10A1 promotes LUAD cell proliferation and invasion in vitro and in vivo. In molecular mechanism study, we found that COL10A1 interacts with DDR2 and affects the downstream FAK signaling pathway to regulate LUAD cell progression. The expression of COL10A1 on tissue microarray (TMA) was also measured to explore the association between COL10A1 expression and patient outcome. The results addressed that COL10A1 is up-regulated and positively correlated with lymph node metastasis in lung adenocarcinoma, and the COL10A1 expression is also an independent prognostic factor. In summary, the up-regulated COL10A1 remodels the ECM and the COL10A1/DDR2/FAK axis regulates the proliferation and metastasis of LUAD cells, implying that COL10A1 is a promising therapeutic target and prognostic marker for LUAD patients.

Objective: Identification of tumor invasiveness of pulmonary adenocarcinomas before surgery is one of the most important guides to surgical planning. Additionally, preoperative diagnosis of lung adenocarcinoma with micropapillary patterns is also critical for clinical decision making. We aimed to evaluate the accuracy of deep learning models on classifying invasiveness degree and attempted to predict the micropapillary pattern in lung adenocarcinoma. Methods: The records of 291 histopathologically confirmed lung adenocarcinoma patients were retrospectively analyzed and consisted of 61 adenocarcinoma in situ, 80 minimally invasive adenocarcinoma, 117 invasive adenocarcinoma, and 33 invasive adenocarcinoma with micropapillary components (>5%). We constructed two diagnostic models, the Lung-DL model and the Dense model, based on the LeNet and the DenseNet architecture, respectively. Results: For distinguishing the nodule invasiveness degree, the area under the curve (AUC) value of the diagnosis with the Lung-DL model is 0.88 and that with the Dense model is 0.86. In the prediction of the micropapillary pattern, overall accuracies of 92 and 72.91% were obtained for the Lung-DL model and the Dense model, respectively. Conclusion: Deep learning was successfully used for the invasiveness classification of pulmonary adenocarcinomas. This is also the first time that deep learning techniques have been used to predict micropapillary patterns. Both tasks can increase efficiency and assist in the creation of precise individualized treatment plans.

Despite the implementation of the 8th American Joint Committee on Cancer (AJCC) TNM staging system for gastric cancer (GC) in 2017, it still holds a significant level of stage migration which affects patients' proper classification and accurate prognosis. Here, to reduce this effect, we evaluated the prognostic value of a lymph node ratio (LNR) and established a novel tumor-ratio-metastasis (TRM) staging system.

To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.

Objectives: To evaluate whether incorporating the radiomics, genomics, and clinical features allows prediction of metastasis in colorectal cancer (CRC) and to develop a preoperative nomogram for predicting metastasis. Methods: We retrospectively analyzed radiomics features of computed tomography (CT) images in 134 patients (62 in the primary cohort, 28 in the validation cohort, and 44 in the independent-test cohort) clinicopathologically diagnosed with CRC at Dazhou Central Hospital from February 2018 to October 2019. Tumor tissues were collected from all patients for RNA sequencing, and clinical data were obtained from medical records. A total of 854 radiomics features were extracted from enhanced venous-phase CT of CRC. Least absolute shrinkage and selection operator regression analysis was utilized for data dimension reduction, feature screen, and radiomics signature development. Multivariable logistic regression analysis was performed to build a multiscale predicting model incorporating the radiomics, genomics, and clinical features. The receiver operating characteristic curve, calibration curve, and decision curve were conducted to evaluate the performance of the nomogram. Results: The radiomics signature based on 16 selected radiomics features showed good performance in metastasis assessment in both primary [area under the curve (AUC) = 0.945, 95% confidence interval (CI) 0.892-0.998] and validation cohorts (AUC = 0.754, 95% CI 0.570-0.938). The multiscale nomogram model contained radiomics features signatures, four-gene expression related to cell cycle pathway, and CA 19-9 level. The multiscale model showed good discrimination performance in the primary cohort (AUC = 0.981, 95% CI 0.953-1.000), the validation cohort (AUC = 0.822, 95% CI 0.635-1.000), and the independent-test cohort (AUC = 0.752, 95% CI 0.608-0.896) and good calibration. Decision curve analysis confirmed the clinical application value of the multiscale model. Conclusion: This study presented a multiscale model that incorporated the radiological eigenvalues, genomics features, and CA 19-9, which could be conveniently utilized to facilitate the individualized preoperatively assessing metastasis in CRC patients.

In this study, we retrieved the data available in the Surveillance, Epidemiology, and End Results database to identify the prognostic factors for patients with pancreatic head cancer who had undergone pancreaticoduodenectomy and developed a prediction model for clinical reference.