Relaxin 2/RXFP1 Signaling Induces Cell Invasion via the β-Catenin Pathway in Endometrial Cancer

Fue, Misaki; Miki, Yasuhiro; Takagi, Kiyoshi; Hashimoto, Chiaki; Yaegashi, Nobuo; Suzuki, Takashi; Itō, Kiyoshi

doi:10.3390/ijms19082438

Cited by 23 publications

(11 citation statements)

References 53 publications

(60 reference statements)

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“…Suppression of RXFP1 inhibits prostate cancer tumorigenesis, growth, and metastasis [41–43]. Relaxin 2/RXFP1 Signalling induces cell invasion via the beta-catenin pathway in endometrial cancer [44]. Expression of RXFP1 is decreased in idiopathic pulmonary fibrosis [45] and mediates the effects of miR-144-3p in lung fibroblasts from patients with idiopathic pulmonary fibrosis [46].…”

Section: Discussionmentioning

confidence: 99%

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Xie

2019

BioMed Research International

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Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (−0.2491 × EXPRRAGB) + (−0.0679 × EXPRSPH9) + (−0.2317 × EXPRPS6KL1) + (−0.1035 × EXPRXFP1) + 0.1571 × EXPRRM2 + 0.1104 × EXPRTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.

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Section: Discussionmentioning

confidence: 99%

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Xie

2019

BioMed Research International

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“…It is suggested that relaxin induces aggressive cell growth and invasiveness in several types of cancer, including endometrial cancer. Adherens junctions in cancer cells are weakened by the breakdown of the cadherin/catenin complex, which is induced by β‐catenin phosphorylation via RLN2/RXFP1 signaling . RXFP3 has not been reported and further studies are warranted to clarify the role of RXFP3 in cancer metastasis.…”

Section: Discussionmentioning

confidence: 99%

Genomic profiles of colorectal carcinoma with liver metastases and newly identified fusion genes

et al. 2019

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Every year, approximately 1.2 million cases of colorectal carcinoma (CRC) are newly diagnosed worldwide. Although metastases to distant organs are often fatal complications of CRC, little information is known as to how such metastatic lesions are formed. To reveal the genetic profiles for CRC metastasis, we conducted whole‐exome RNA sequencing on CRC tumors with liver metastasis (LM) (group A, n = 12) and clinical stage‐matched larger tumors without LM (group B, n = 16). While the somatic mutation profiles were similar among the primary tumors and LM lesions in group A and the tumors in group B, the A‐to‐C nucleotide change in the context of “AAG” was only enriched in the LM regions in group A, suggesting the presence of a DNA damage process specific to metastasis. Genes already known to be associated with CRC were mutated in all groups at a similar frequency, but we detected somatic nonsynonymous mutations in a total of 707 genes in the LM regions, but not in the tumors without LM. Signaling pathways linked to such “LM‐associated” genes were overrepresented for extracellular matrix‐receptor interaction or focal adhesion. Further, fusions of the ADAP1 (ArfGAP with dual PH domain 1) were newly identified in our cohort (3 out of 28 patients), which activated ARF6, an ADAP1‐substrate. Infrequently, mutated genes may play an important role in metastasis formation of CRC. Additionally, recurrent ADAP1 fusion genes were unexpectedly discovered. As these fusions activate small GTPase, further experiments are warranted to examine their contribution to CRC carcinogenesis.

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“…In our study, the gene RXFP2 that is predicted only in Subtype-2 is found to be involved in neuroactive ligand-receptor interaction. RXFP2 is also found to be involved in Relaxin signaling which induces cell invasion and is reported in several cancers 30 . The modulators of ABC transporters is reported to have the potential to augment the efficacy of anticancer drugs 31 .…”

Section: Discussionmentioning

confidence: 95%

Gene Prediction in Heterogeneous Cancer Tissues and Establishment of Least Absolute Shrinkage and Selection Operator Model for Lung Squamous Cell Carcinoma

Khaliq¹,

Sharathchandra²,

Meenakshi³

2019

Int J Life Sci Pharm Res

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This study is aimed to establish a Least Absolute Shrinkage and Selection Operator (LASSO) model based on tumor heterogeneity to predict the best features of LUSC in various cancer subtypes. The RNASeq data of 505 LUSC cancer samples were downloaded from the TCGA database. Subsequent to the identification of differentially expressed genes (DEGs), the samples were divided into two subtypes based on the consensus clustering method. The subtypes were estimated with the abundance of immune and non-immune stromal cell populations which infiltrated the tissue. LASSO model was established to predict each subtype's best genes. Enrichment pathway analysis was then carried out. Finally, the validity of the LUSC model for identifying features was established by the survival analysis. 240 and 262 samples were clustered in Subtype-1 and Subtype-2 groups respectively. DEG analysis was performed on each subtype. A standard cutoff was applied and in total, 4586 genes were up regulated and 1495 were down regulated in case of subtype-1 and 5016 genes were up regulated and 3224 were down regulated in case of subtype-2. LASSO model was established to predict the best features from each subtype, 49 and 34 most relevant genes were selected in subtype-1 and subtype-2. The abundance of tissue-infiltrates analysis distinguished the subtypes based on the expression pattern of immune infiltrates. Survival analysis showed that this model could effectively predict the best and distinct features in cancer subtypes. This study suggests that unsupervised clustering and LASSO model-based feature selection can be effectively used to predict relevant genes which might play an important role in cancer diagnosis.

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Relaxin 2/RXFP1 Signaling Induces Cell Invasion via the β-Catenin Pathway in Endometrial Cancer

Cited by 23 publications

References 53 publications

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Genomic profiles of colorectal carcinoma with liver metastases and newly identified fusion genes

Gene Prediction in Heterogeneous Cancer Tissues and Establishment of Least Absolute Shrinkage and Selection Operator Model for Lung Squamous Cell Carcinoma

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