Loss of CADM1/TSLC1 Expression Is Associated with Poor Clinical Outcome in Patients with Esophageal Squamous Cell Carcinoma

CADM1 promotes adhesion to vascular endothelial cells and transendothelial migration in cultured GIST cells

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the human gastrointestinal tract. Small intestinal GISTs appear to be associated with poorer prognosis and higher metastasis rate than gastric GISTs of the same size and mitotic index. Recently, we reported that cell adhesion molecule 1 (CADM1) is expressed specifically in most small intestinal GISTs, but not in most gastric GISTs, suggesting that this difference in CADM1 expression between gastric GISTs and small intestinal GISTs might influence the difference in clinical behavior between them. The aim of the present study was to examine whether high CADM1 expression affected proliferation, migration, invasion, adhesion to endothelial cells and transendothelial migration of cultured GIST cells by comparing original GIST-T1 cells with very low CADM1 expression with GIST-T1 cells with high CADM1 expression induced by CADM1 cDNA transfection (GIST-T1-CAD cells). GIST-T1-CAD cells had reduced ability to proliferate, migrate and invade compared with the original GIST-T1 cells, but showed significantly higher ability to adhere to human umbilical vein endothelial cells and migrate through endothelial cell monolayers. Thus, CADM1 may contribute to higher metastasis rates in small intestinal GISTs facilitating tumor cell adhesion to vascular endothelial cell and transendothelial migration of tumor cells. CADM1 might serve as a potential target for inhibition of metastasis in small intestinal GISTs.

LncRNA DCST1-AS1 Promotes Endometrial Cancer Progression by Modulating the MiR-665/HOXB5 and MiR-873-5p/CADM1 Pathways

Dysregulation of long noncoding RNA (lncRNA) is implicated in the initiation and progression of various tumors, including endometrial cancer (EC). However, the mechanism of lncRNAs in EC tumorigenesis and progression remains largely unexplored. In this work, we identified a novel lncRNA DC-STAMP domain-containing 1-antisense 1 (DCST1-AS1), which is highly upregulated and correlated with poor survival in EC patients. Overexpression of DCST1-AS1 significantly enhanced EC cell proliferation, colony formation, migration, and invasion in vitro and promoted tumor growth of EC in vivo. Mechanistically, DCST1-AS1 mediated EC progression by inducing the expression of homeobox B5 (HOXB5) and cell adhesion molecule 1 (CADM1), via acting as a competing endogenous RNA for microRNA-665 (miR-665) and microRNA-873-5p (miR-873-5p), respectively. In addition, we found that the expression of miR-665 and miR-873-5p was significantly downregulated, while HOXB5 and CADM1 expression levels were increased in EC tissues. Taken together, our findings support the important role of DCST1-AS1 in EC progression, and DCST1-AS1 may be used as a prognostic biomarker as well as a potential therapeutic target for EC.

Functional and Clinical Characteristics of Cell Adhesion Molecule CADM1 in Cancer

The cell adhesion molecule CADM1, which participates in cell adhesion and signal transduction, has a regulatory effect on the development of tumors. CADM1 is often involved in malignant tumors of multiple organ systems, such as the respiratory and digestive systems. Upregulated CADM1 promotes tumor cell apoptosis and inhibits malignant proliferation. Along with cell cycle-related proteins, it participates in regulating signaling pathways, such as EMT, STAT3, and AKT, and plays an important role in inhibiting invasion and migration. Considering clinical characteristics, low CADM1 expression is associated with aggressive tumors and poor prognosis. In addition, some long non-coding RNAs (lncRNAs) or miRNAs directly or indirectly act on CADM1 to regulate tumor growth and motility. Interestingly, CADM1 function differs in adult T-cell leukemia/lymphoma (ATLL), and NF-κB is thought to be involved in this process. Taken together, CADM1 could be a potential biomarker for early diagnosis and a target for cancer treatment in future clinical practices.

An oncolytic adenovirus delivering TSLC1 inhibits Wnt signaling pathway and tumor growth in SMMC-7721 xenograft mice model

Tumor suppressor in lung cancer-1 (TSLC1) was first identified as a tumor suppressor for lung cancer, and frequently downregulated in various types of cancers including hepatocellular carcinoma (HCC). The Wnt pathway plays a critical role in tumorigenesis, migration, and invasion in HCC. However, the function of TSLC1 in modulating Wnt signaling in HCC is unclear. In this study, we evaluated the effect of TSLC1-armed oncolytic adenovirus (S24-TSLC1) on the Wnt/β-catenin pathway, cell viability, invasion and migration abilities of HCC in vitro and the growth of SMMC-7721-xenografted tumor in mice model. We detected the expression of TSLC1 in tumor samples and HCC cell lines. The results showed that TSLC1 expression was low in HCC, but high in pericarcinomatous tissue and normal cells, which implied that TSLC1 is a tumor suppressor of liver cancer. S24-TSLC1 exhibited an antitumor effect on HCC cell growth in vitro, but did little damage to normal liver cells. Overexpression of TSLC1 downregulated the transcriptional activity of TCF4/β-catenin and inhibited the mRNA or protein expression of Wnt target genes cyclinD1 and c-myc. S24-TSLC1 also inhibited the invasion and migration of HCC cells. Animal experiments further confirmed that S24-TSLC1 significantly inhibited tumor growth of the SMMC-7721-xenografted tumor. In conclusion, TSLC1 could downregulate the Wnt signal pathway and suppress HCC cell growth, migration and invasion, suggesting that S24-TSLC1 may be a potent antitumor agent for future clinical trials in liver cancer treatment.

Differential Expression of CADM1 in Gastrointestinal Stromal Tumors of Different Sites and with Different Gene Abnormalities

Gastrointestinal stromal tumor (GIST), the most common mesenchymal tumor of the human gastrointestinal tract, differentiating toward the interstitial cell of Cajal (ICC), arises predominantly in the stomach and small intestine. Small intestinal GISTs appear to have worse prognosis than gastric GISTs. In a pilot study of a cDNA expression chip using several GISTs, we found that Cell Adhesion Molecule 1 (CADM1), which could contribute to tumor growth and infiltration, is expressed more strongly in small intestinal GISTs than gastric GISTs. In the present study, we examined CADM1 expression in GISTs of different sites and with different gene abnormalities using a large number of gastric and small intestinal GISTs. First, immunoblotting confirmed significantly higher CADM1 expression in small intestinal GISTs with exon 11 c-kit mutation than gastric GISTs with exon 11 c-kit mutation. Real-time PCR also revealed that small intestinal GISTs with exon 11 c-kit mutation showed significantly higher CADM1 mRNA than gastric GISTs with exon 11 c-kit mutation. Although most small intestinal GISTs showed high CADM1 mRNA expression regardless of gene abnormality types, different CADM1 expression was detected between gastric GISTs with c-kit mutation and those with PDGFRA mutation. Immunohistochemistry showed that many small intestinal GISTs were CADM1-positive but most gastric GISTs CADM1-negative or -indefinite. In the normal gastric and small intestinal walls, immunoreactivity of CADM1 was detected only in nerves, but neither in gastric ICCs nor small intestinal ICCs, indicating that the high CADM1expression in small intestinal GISTs might be acquired during tumorigenesis. Different CADM1 expression between gastric and small intestinal GISTs might be related to different prognoses between them. Further functional experiments are needed to elucidate the role of CADM1 on GIST biology, and there is a possibility that targeting therapy against CADM1 has a preventive effect for tumor spreading in small intestinal GISTs.

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning

Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the overall survival of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker- and biomarker+ subgroups. Then, by using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene biomarkers. Finally, the combined 15 biomarkers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival status of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%). Using the capability of deep learning, we believe that our prediction can be a promising index that helps oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.

Reaching the limits of prognostication in non-small cell lung cancer: an optimized biomarker panel fails to outperform clinical parameters

Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer patients. One score was obtained for each tumor and each protein. The scores were combined, with or without the inclusion of clinical parameters, and the best prognostic model was defined according to the corresponding concordance index (C-index). The best-performing model was subsequently validated in an independent cohort consisting of tissue from 345 non-small cell lung cancer patients. The model based only on protein expression did not perform better compared to clinicopathological parameters, whereas combining protein expression with clinicopathological data resulted in a slightly better prognostic performance (C-index: all non-small cell lung cancer 0.63 vs 0.64; adenocarcinoma: 0.66 vs 0.70, squamous cell carcinoma: 0.57 vs 0.56). However, this modest effect did not translate into a significantly improved accuracy of survival prediction. The combination of a prognostic biomarker panel with clinicopathological parameters did not improve survival prediction in non-small cell lung cancer, questioning the potential of immunohistochemistry-based assessment of protein biomarkers for prognostication in clinical practice.