P-Cadherin (CDH3) is overexpressed in colorectal tumors and has potential as a serum marker for colorectal cancer monitoring

Hmcs, Kumara; Ga, Bellini; Ol, Caballero; Sac, Herath; Su, Tong; Ahmed, Aqeel; Njoh, Linda; Cekic,; Rl, Whelan

doi:10.18632/oncoscience.370

Cited by 30 publications

(37 citation statements)

References 15 publications

(17 reference statements)

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“…The top five proteins predicted by two models, NKX2-1, CAV1, CDH3, FN1 and YBX1, were exactly the same. The relationship between the expression of these five proteins and the prognosis of the cancer patients was almost consistent with that described in previous reports [ 23 , 24 , 25 , 26 , 27 , 28 ]. CAV-1 and YBX-1 are generally negative predictors of NSCLC outcomes [ 24 , 25 ].…”

Section: Resultssupporting

confidence: 90%

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

et al. 2020

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Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

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Section: Resultssupporting

confidence: 90%

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

et al. 2020

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“…For example, CDH3 exerts upregulated expressions in high‐grade breast cancer, serving as an effective indicator for tumor progression, and poor prognosis . In addition, CDH3 has also been found to be upregulated in colorectal tumors and carries potential for use in colorectal cancer monitoring …”

Section: Discussionmentioning

confidence: 99%

Long noncoding RNA ADAMTS9‐AS2 suppresses the progression of esophageal cancer by mediating CDH3 promoter methylation

Yang

Zhu

et al. 2019

Molecular Carcinogenesis

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Long noncoding RNAs (lncRNAs) have been implicated in the biology of esophageal cancer via mRNA degradation or translational inhibition. CDH3 is also aberrantly expressed in numerous cancers. This study was conducted with the hypothesis that ADAMTS9-AS2 or CDH3 methylation plays a role in esophageal cancer cell activity and in vivo development. Firstly, mRNA levels of ADAMTS9-AS2 and CDH3 in esophageal cancer tissues and cells were detected by reverse-transcription quantitative polymerase chain reaction. Afterward, esophageal cancer OE21 cells were treated with overexpression of ADAMTS9-AS2, siRNA against ADAMTS9-AS2, overexpression of CDH3 and demethylating agent 5-aza-dc. The biological functions of esophageal cancer OE21 cells were assayed to define the regulatory mechanisms of ADAMTS9-AS2 in esophageal cancer. The interactions among ADAMTS9-AS2, DNMT1/DNMT3 (A/B) and CDH3 were detected by MSP, RNA pull-down, RIP, and ChIP assays. The in vitro findings were reproduced in nude mice to explore the role of ADAMTS9-AS2 in the development of esophageal cancer in vivo. Esophageal cancers expressed low levels of ADAMTS9-AS2 and high levels of CDH3. Methylation of CDH3 promoter was induced by ADAMTS9-AS2 via DNMT1/DNMT3 (A/B). Furthermore, proliferation, invasion, and migration of esophageal cancer cells were inhibited by ADAMTS9-AS2 via downregulation of CDH3. Suppressed esophageal cancer development in vivo was also detected after ADAMTS9-AS2 overexpression. Overexpressed ADAMTS9-AS2 aids in the suppression of esophageal cancer development, which is achieved via inducing CDH3 promoter methylation. K E Y W O R D S ADAMTS9-AS2, CDH3, esophageal cancer, invasion, methylation, migration, proliferation

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“…Additional examples of differentially expressed genes (log-fold change > 0.5 and q value < 0.05), such as CDH3, TP53INP2, E2F1, CCND2, and SERPINE1, were also previously shown to have direct roles in colorectal and digestive cancers [45][46][47]. While some of these genes participate in basic cancer-related cellular functions such as proliferation and invasion [45,[47][48][49][50], others, e.g., BEST2, play important roles in gut barrier function and anion transport [51]. To test signatures of inflammation in our data, we intersected our DEGs (q value < 0.05) with data from Hong et al [52], who compared gene regulation in Crohn's disease (CD) patients (with and without inflammation) and healthy controls.…”

Section: Differentially Expressed Host Genes Between Cf and Healthy Mmentioning

confidence: 99%

“…Examples include RRS1 (q value = 6.16E−09), which encodes for the ribosomal biogenesis protein homolog that promotes angiogenesis and cellular proliferation, but suppresses apoptosis [42]; KRTAP5-5 (q value = 4.89E−08), which encodes for keratin-associated protein 5-5, a protein that plays important roles in cytoskeletal function and facilitates various malignant behaviors that include cellular motility and vascular invasion [43]; and ALDOB (q value = 2.64E −07), which encodes for aldolase B, an enzyme that promotes metastatic cancer-associated metabolic reprogramming [44]. Additional examples of differentially expressed genes (log-fold change > 0.5 and q value < 0.05), such as CDH3, TP53INP2, E2F1, CCND2, and SERPINE1, were also previously shown to have direct roles in colorectal and digestive cancers [45][46][47]. While some of these genes participate in basic cancer-related cellular functions such as proliferation and invasion [45,[47][48][49][50], others, e.g., BEST2, play important roles in gut barrier function and anion transport [51].…”

Section: Differentially Expressed Host Genes Between Cf and Healthy Mmentioning

confidence: 99%

Interactions between the gut microbiome and host gene regulation in cystic fibrosis

et al. 2020

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Background: Cystic fibrosis is the most common autosomal recessive genetic disease in Caucasians. It is caused by mutations in the CFTR gene, leading to poor hydration of mucus and impairment of the respiratory, digestive, and reproductive organ functions. Advancements in medical care have led to markedly increased longevity of patients with cystic fibrosis, but new complications have emerged, such as early onset of colorectal cancer. Although the pathogenesis of colorectal cancer in cystic fibrosis remains unclear, altered host-microbe interactions might play a critical role. To investigate this, we characterized changes in the microbiome and host gene expression in the colonic mucosa of cystic fibrosis patients relative to healthy controls, and identified host gene-microbiome interactions in the colon of cystic fibrosis patients. Methods: We performed RNA-seq on colonic mucosa samples from cystic fibrosis patients and healthy controls to determine differentially expressed host genes. We also performed 16S rRNA sequencing to characterize the colonic mucosal microbiome and identify gut microbes that are differentially abundant between patients and healthy controls. Lastly, we modeled associations between relative abundances of specific bacterial taxa in the gut mucosa and host gene expression. Results: We find that 1543 genes, including CFTR, show differential expression in the colon of cystic fibrosis patients compared to healthy controls. These genes are enriched with functions related to gastrointestinal and colorectal cancer, such as metastasis of colorectal cancer, tumor suppression, p53, and mTOR signaling pathways. In addition, patients with cystic fibrosis show decreased gut microbial diversity, decreased abundance of butyrate producing bacteria, such as Ruminococcaceae and Butyricimonas, and increased abundance of other taxa, such as Actinobacteria and Clostridium. An integrative analysis identified colorectal cancer-related genes, including LCN2 and DUOX2, for which gene expression is correlated with the abundance of colorectal cancer-associated bacteria, such as Ruminococcaceae and Veillonella. Conclusions: In addition to characterizing host gene expression and mucosal microbiome in cystic fibrosis patients, our study explored the potential role of host-microbe interactions in the etiology of colorectal cancer in cystic fibrosis. Our results provide biomarkers that may potentially serve as targets for stratifying risk of colorectal cancer in patients with cystic fibrosis.

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P-Cadherin (CDH3) is overexpressed in colorectal tumors and has potential as a serum marker for colorectal cancer monitoring

Cited by 30 publications

References 15 publications

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Long noncoding RNA ADAMTS9‐AS2 suppresses the progression of esophageal cancer by mediating CDH3 promoter methylation

Interactions between the gut microbiome and host gene regulation in cystic fibrosis

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