MEOX1 Promotes Tumor Progression and Predicts Poor Prognosis in Human Non-Small-Cell Lung Cancer

MEOX1 triggers myofibroblast apoptosis resistance, contributing to pulmonary fibrosis in mice

The apoptosis resistance of myofibroblasts is a hallmark in the irreversible progression of pulmonary fibrosis (PF). While the underlying molecular mechanism remains elusive. In this study, we unveiled a previously unrecognized mechanism underlying myofibroblast apoptosis resistance during PF. Our investigation revealed heightened expression of mesenchyme homeobox 1 (MEOX1) in the lungs of idiopathic pulmonary fibrosis (IPF) patients and bleomycin-induced PF mice. Silencing MEOX1 significantly attenuated PF progression in mice. In vitro, we found a notable increase in MEOX1 expression in transforming growth factor-β1 (TGF-β1)-induced myofibroblasts. Silencing MEOX1 enhanced apoptosis of myofibroblasts. Mechanistically, we identified G-protein signaling pathway regulatory factor 4 (RGS4) as a critical downstream target of MEOX1, as predicted by bioinformatics analysis. MEOX1 enhanced apoptosis resistance by upregulating RGS4 expression in myofibroblasts. In conclusion, our study highlights MEOX1 as a promising therapeutic target for protecting against PF by modulating myofibroblast apoptosis resistance.

Establishment of glioma prognosis nomogram based on the function of meox1 in promoting the progression of cancer

Background

Gliomas stand out as highly predominant malignant nervous tumors and are linked to adverse treatment outcomes and short survival periods. Current treatment options are limited, emphasizing the need to identify effective therapeutic targets. The heterogeneity of tumors necessitates a personalized treatment approach with an effective grouping system. Meox1 has been implicated in promoting tumor progression in diverse cancers; nonetheless, its role in gliomas remains unelucidated.

Material/methods

Utilized immunohistochemistry to assess the expression of Meox1 protein in glioma tissues. Proliferation and invasion assays were conducted on wild-type and meox1-overexpressed glioma cells using the CCK8 and Transwell assays, respectively. The expression levels of meox1 and its related genes in gliomas were obtained from Chinese Glioma Genome Atlas (CGGA), along with the corresponding patient survival periods. LASSO regression modeling was employed to construct a scoring system for patients with gliomas, categorizing them into high-/low-risk groups. Additionally, a nomogram for predicting the survival period of patients with glioma was developed using multivariate logistic analysis.

Results

We attempted, for the first time, to demonstrate heightened expression of Meox1 in glioma tumor tissues, correlating with significantly increased invasion and proliferation abilities of glioma cells following meox1 overexpression. The scoring system effectively stratified patients with glioma into high-/low-risk groups, revealing differences in the survival period and immunotherapy efficacy between the two groups. The integration of this scoring system with other clinical indicators yielded a nomogram capable of effectively predicting the survival period of individuals with gliomas.

Conclusions

Our study established a stratified investigation system based on the levels of meox1 and its related genes, providing a novel, cost-effective model for facilitating the prognosis prediction of individuals with glioma.

Unraveling the molecular mechanisms of lymph node metastasis in ovarian cancer: focus on MEOX1

BACKGROUND

Lymph node metastasis (LNM) is a major factor contributing to the high mortality rate of ovarian cancer, making the treatment of this disease challenging. However, the molecular mechanism underlying LNM in ovarian cancer is still not well understood, posing a significant obstacle to overcome.

RESULTS

Through data mining from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we have identified MEOX1 as a specific gene associated with LNM in ovarian cancer. The expression of MEOX1 was found to be relatively high in serous ovarian adenocarcinoma, and its higher expression were associated with increased tumor grade and poorer clinical prognosis for ovarian cancer patients. Bioinformatics analysis revealed that MEOX1 exhibited the highest mRNA levels among all cancer types in ovarian cancer tissues and cell lines. Furthermore, gene set enrichment analysis (GSEA) and pathway analysis demonstrated that MEOX1 was involved in various LNM-related biological activities, such as lymphangiogenesis, lymphatic vessel formation during metastasis, epithelial-mesenchymal transition (EMT), G2/M checkpoint, degradation of extracellular matrix, and collagen formation. Additionally, the expression of MEOX1 was positively correlated with the expression of numerous prolymphangiogenic factors in ovarian cancer. To validate our findings, we conducted experiments using clinical tissue specimens and cell lines, which confirmed that MEOX1 was highly expressed in high-grade serous ovarian cancer (HGSOC) tissues and various ovarian cancer cell lines (A2780, SKOV3, HO8910, and OVCAR5) compared to normal ovarian tissues and normal ovarian epithelial cell line IOSE-80, respectively. Notably, we observed a higher protein level of MEOX1 in tumor tissues of LNM-positive HGSOC compared to LNM-negative HGSOC. Moreover, our fundamental experiments demonstrated that suppression of MEOX1 led to inhibitory effects on ovarian cancer cell proliferation and EMT, while overexpression of MEOX1 enhanced the proliferation and EMT capacities of ovarian cancer cells.

CONCLUSIONS

The results of our study indicate that MEOX1 plays a role in the lymph node metastasis of ovarian cancer by regulating multiple biological activities, including the proliferation and EMT of ovarian cancer, lymphangiogenesis, and ECM remodeling. Our findings suggest that MEOX1 could serve as a potential biomarker for the diagnosis and treatment of ovarian cancer with LNM.

Identification of the novel FOXP3-dependent Treg cell transcription factor MEOX1 by high-dimensional analysis of human CD4+ T cells

CD4+ T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4+ T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4+ T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4+ T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in Treg cells. Expression of MEOX1 was comparable to FOXP3 in Treg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in Treg cells. Knockdown of MEOX1 in Treg cells revealed a profound impact on downstream gene expression programs and Treg cell suppressive capacity. These findings in the context of CD4+ T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4+ T cell functionality, which opens new avenues for future therapeutic strategies.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

OBJECTIVES

Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m¹A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m¹A RNA methylation-related genes in BRCA is indistinct.

METHODS

The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m¹A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m¹A-related genes were selected through overlapping m¹A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m¹A score subgroups. The m¹A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).

RESULTS

Eighty-five differentially expressed m¹A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.

CONCLUSIONS

An m¹A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

Identification and Validation of Immune-Related Long Non-Coding RNA Signature for Predicting Immunotherapeutic Response and Prognosis in NSCLC Patients Treated With Immunotherapy

Background

Numerous studies have reported that long non-coding RNAs (lncRNAs) play important roles in immune-related pathways in cancer. However, immune-related lncRNAs and their roles in predicting immunotherapeutic response and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy remain largely unexplored.

Methods

Transcriptomic data from NSCLC patients were used to identify novel lncRNAs by a custom pipeline. ImmuCellAI was utilized to calculate the infiltration score of immune cells. The marker genes of immunotherapeutic response-related (ITR)-immune cells were used to identify immune-related (IR)-lncRNAs. A co-expression network was constructed to determine their functions. LASSO and multivariate Cox analyses were performed on the training set to construct an immunotherapeutic response and immune-related (ITIR)-lncRNA signature for predicting the immunotherapeutic response and prognosis of NSCLC. Four independent datasets involving NSCLC and melanoma patients were used to validate the ITIR-lncRNA signature.

Results

In total, 7,693 novel lncRNAs were identified for NSCLC. By comparing responders with non-responders, 154 ITR-lncRNAs were identified. Based on the correlation between the marker genes of ITR-immune cells and lncRNAs, 39 ITIR-lncRNAs were identified. A co-expression network was constructed and the potential functions of 38 ITIR-lncRNAs were annotated, most of which were related to immune/inflammatory-related pathways. Single-cell RNA-seq analysis was performed to confirm the functional prediction results of an ITIR-lncRNA, LINC01272. Four-ITIR-lncRNA signature was identified and verified for predicting the immunotherapeutic response and prognosis of NSCLC. Compared with non-responders, responders had a lower risk score in both NSCLC datasets (P<0.05). NSCLC patients in the high-risk group had significantly shorter PFS/OS time than those in the low-risk group in the training and testing sets (P<0.05). The AUC value was 1 of responsiveness in the training set. In melanoma validation datasets, patients in the high-risk group also had significantly shorter OS/PFS time than those in the low-risk group (P<0.05). The ITIR-lncRNA signature was an independent prognostic factor (P<0.001).

Conclusion

Thousands of novel lncRNAs in NSCLC were identified and characterized. In total, 39 ITIR-lncRNAs were identified, 38 of which were functionally annotated. Four ITIR-lncRNAs were identified as a novel ITIR-lncRNA signature for predicting the immunotherapeutic response and prognosis in NSCLC patients treated with immunotherapy.

MEOX1 suppresses the progression of lung cancer cells by inhibiting the cell-cycle checkpoint gene CCNB1

The previous study has shown that transcriptional factor MEOX1 could promote proliferation and sphere formation ability of non-small cell lung cancer (NSCLC) cells, however, we found that MEOX1 mRNA was lowly expressed in lung cancer tissues compared to that in normal adjacent tissues, and MEOX1 mRNA expression was positively correlated with the survival of lung cancer patients, especially in lung adenocarcinoma patients. Functional experiments using in vitro and in vivo experiments revealed that stable overexpression of MEOX1 significantly suppressed the proliferation ability, promoted cell cycle arrest in G2 phase, and apoptotic ability of NSCLC cells. Additionally, it was identified that MEOX1 and CCNB1 mRNA expression exhibited a negative correlation in different lung cancer tissues. Mechanistically, we indicated that MEOX1 bound to the transcriptional initiation site of CCNB1 and thus suppressed CCNB1 expression. Notably, CCNB1 overexpression rescued the inhibition of MEOX1 overexpression on NSCLC progression. This study deciphers a novel MEOX1/CCNB1 axis suppressing NSCLC progression.

Mesenchyme homeobox 1 mediated-promotion of osteoblastic differentiation is negatively regulated by mir-3064-5p

Human mesenchymal stem cells (hMSCs) are multipotent cells that can be differentiated into different cell types including osteoblasts. Herein we aimed to assess the regulation of transcription factor mesenchyme homeobox 1 (Meox1) in the osteogenic differentiation of hMSCs and to determine the microRNA which targets on Meox1. Total RNA was extracted from the isolated ligamentum flavum tissue samples and cultured hMSCs, and the expression of Meox1 was assessed by RT-PCR and Western blot assays. Cultured hMSCs were induced towards osteoblastic differentiation, and the osteoblast phenotype was determined by alkaline phosphatase activity and alizarin red staining. The microRNA targeting on the 3'-UTR of Meox1was predicted using bioinformatics tool, and the binding was validated by luciferase and RNA pulldown assays. The osteoblastic differentiation of hMSCs was checked with the knockdown of Meox1 and microRNA inhibitors. Higher expression of Meox1, and lower expression of miR-3064-5p in ossified ligamentum flavum (OLF) tissues were identified. In addition, increased expression along with the osteoblastic differentiation of hMSCs was found. Further research revealed that Meox was a direct target of miR-3064-5p, when the former promoted the differentiation of hMSCs into osteoblasts, the latter significantly suppressed the osteogenesis. The expression of Meox1 increased gradually with the osteoblastic differentiation of hMSCs, during which miR-3064-5p decreased. Meox1 is a direct target of miR-3064-5p, and they both play important roles in the osteogenesis. These findings provide potential target for the development of therapeutic drugs for skeletal system diseases.

Pentraxin 3 is a diagnostic and prognostic marker for ovarian epithelial cancer patients based on comprehensive bioinformatics and experiments

BACKGROUND

Ovarian epithelial cancer is one of the leading malignant tumors in gynecology and lacks effective diagnostic and prognostic markers. Our study aims to screen and verify ovarian epithelial cancer biomarkers.

METHODS

GSE18520 and GSE26712 were downloaded from the GEO database. The "limma" and "WGCNA" packages were used to explore hub genes. The Kaplan-Meier Plotter database was used for survival analysis of the hub genes. Immunohistochemical analysis was used to identify the expression level of Pentraxin 3 in ovarian epithelial cancer samples.

RESULTS

In this study, we integrated and analyzed two datasets, GSE18520 and GSE26712, and a total of 238 differentially expressed genes (DEGs) were screened out. Enrichment analysis showed that these DEGs were related to collagen-containing extracellular matrix and other pathways. Further application of WGCNA (weighted gene coexpression network analysis) identified 15 gene modules, with the purple module showing the highest correlation with ovarian epithelial cancer. Twenty-five genes were shared between the purple module and DEGs, 13 genes were related to the prognosis of ovarian epithelial cancer patients, and the PTX3 gene had the highest hazardous risk (HR) value. We performed immunohistochemical analyses on the 255 Pentraxin-3 (PTX3)-based clinical samples. PTX3 was found to be overexpressed in ovarian epithelial cancer and related to the degree of differentiation. The Cox proportional hazard model indicates that high PTX3 expression is an independent risk factor for the prognosis of ovarian epithelial cancer patients.

CONCLUSIONS

In conclusion, through WGCNA and a series of comprehensive bioinformatics analyses, PTX3 was first identified as a novel diagnostic and prognostic biomarker for ovarian epithelial cancer.

TIM-3 Expression Is Downregulated on Human NK Cells in Response to Cancer Targets in Synergy with Activation

Among natural killer (NK) cell receptors, the T-cell immunoglobulin and mucin-containing domain (TIM-3) has been associated with both inhibitory and activating functions, depending on context and activation pathway. Ex vivo and in vitro, expression of TIM-3 is inducible and depends on activation stimulus. Here, we report that TIM-3 expression can be downregulated on NK cells under specific conditions. When NK cells are exposed to cancer targets, they synergize with stimulation conditions to induce a substantial decrease in TIM-3 expression on their surface. We found that such downregulation occurs following prior NK activation. Downregulated TIM-3 expression correlated to lower cytotoxicity and lower interferon gamma (IFN-γ) expression, fueling the notion that TIM-3 might function as a benchmark for human NK cell dysfunction.

Molecular Mechanism of Mesenchyme Homeobox 1 in Transforming Growth Factor β1-Induced P311 Gene Transcription in Fibrosis

Organ fibrosis is characterized by excessive fibroblast, and extracellular matrix and the molecular basis are not fully elucidated. Recent studies have proven that P311, an 8-kDa conserved protein, could promote various organ fibrosis, such as skin, kidney, liver, and lung, partially through upregulating transforming growth factor β1 (TGF-β1) translation. However, the upstream regulators and mechanism of P311 gene regulation remain unclear, although we previously found that cytokines, hypoxia, and TGF-β1 could upregulate P311 transcription. Here, we aimed to elucidate the molecular mechanism of TGF-β1–induced P311 transcriptional regulation, focusing on mesenchyme homeobox 1 (Meox1). In this article, we identified the core promoter of P311 through bioinformatics analysis and luciferase reporter assays. Moreover, we demonstrated that Meox1, induced by TGF-β1, could bind to the promoter of P311 and promote its transcriptional activity. Furthermore, the effect of Meox1 on P311 transcriptional expression contributed to altered migration and proliferation in human dermal fibroblast cells. In conclusion, we identified Meox1 as a novel transcription factor of P311 gene, providing a new clue of the pathogenesis in fibrosis.

Stoichioproteomics reveal oxygen usage bias, key proteins and pathways in glioma

Background

The five-year survival rate and therapeutic effect of malignant glioma is low. Identification of key/associated proteins and pathways in glioma is necessary for developing effective diagnosis and targeted therapy of glioma. In addition, Glioma involves hypoxia-specific microenvironment, whether hypoxia restriction influences the stoichioproteomic characteristics of expressed proteins is unknown.

Methods

In this study, we analyzed the most comprehensive immunohistochemical data from 12 human glioma samples and 4 normal cell types of cerebral cortex, identified differentially expressed proteins (DEPs), and researched the oxygen contents of DEPs, highly and lowly expressed proteins. Further we located key genes on human genome to determine their locations and enriched them for key functional pathways.

Results

Our results showed that although no difference was detected on whole proteome, the average oxygen content of highly expressed proteins is 6.65% higher than that of lowly expressed proteins in glioma. A total of 1480 differentially expressed proteins were identified in glioma, including 226 up regulated proteins and 1254 down regulated proteins. The average oxygen content of up regulated proteins is 2.56% higher than that of down regulated proteins in glioma. The localization of differentially expressed genes on human genome showed that most genes were on chromosome 1 and least on Y. The up regulated proteins were significantly enriched in pathways including cell cycle, pathways in cancer, oocyte meiosis, DNA replication etc. Functional dissection of the up regulated proteins with high oxygen contents showed that 51.28% of the proteins were involved in cell cycle and cyclins.

Conclusions

Element signature of oxygen limitation could not be detected in glioma, just as what happened in plants and microbes. Unsaved use of oxygen by the highly expressed proteins and DEPs were adapted to the fast division of glioma cells. This study can help to reveal the molecular mechanism of glioma, and provide a new approach for studies of cancer-related biomacromolecules. In addition, this study lays a foundation for application of stoichioproteomics in precision medicine.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0571-y) contains supplementary material, which is available to authorized users.