HCG18/miR-34a-5p/HMMR axis accelerates the progression of lung adenocarcinoma

Development of a novel embryonic germline gene-related prognostic model of lung adenocarcinoma

BACKGROUND

Emerging evidence implicates the correlation of embryonic germline genes with the tumor progress and patient's outcome. However, the prognostic value of these genes in lung adenocarcinoma (LUAD) has not been fully studied. Here we systematically evaluated this issue, and constructed a novel signature and a nomogram associated with embryonic germline genes for predicting the outcomes of lung adenocarcinoma.

METHODS

The LUAD cohorts retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used as training set and testing set, respectively. The embryonic germline genes were downloaded from the website https://venn.lodder.dev. Then, the differentially expressed embryonic germline genes (DEGGs) between the tumor and normal samples were identified by limma package. The functional enrichment and pathway analyses were also performed by clusterProfiler package. The prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO)-Cox regression method. Survival and Receiver Operating Characteristic (ROC) analyses were performed to validate the model using training set and four testing GEO datasets. Finally, a prognostic nomogram based on the signature genes was constructed using multivariate regression method.

RESULTS

Among the identified 269 DEGGs, 249 were up-regulated and 20 were down-regulated. GO and KEGG analyses revealed that these DEGGs were mainly enriched in the process of cell proliferation and DNA damage repair. Then, 103 DEGGs with prognostic value were identified by univariate Cox regression and further filtered by LASSO method. The resulting sixteen DEGGs were included in step multivariate Cox regression and an eleven embryonic germline gene related signature (EGRS) was constructed. The model could robustly stratify the LUAD patients into high-risk and low-risk groups in both training and testing sets, and low-risk patients had much better outcomes. The multi-ROC analysis also showed that the EGRS model had the best predictive efficacy compared with other common clinicopathological factors. The EGRS model also showed robust predictive ability in four independent external datasets, and the area under curve (AUC) was 0.726 (GSE30219), 0.764 (GSE50081), 0.657 (GSE37745) and 0.668 (GSE72094). More importantly, the expression level of some genes in EGRS has a significant correlation with the progression of LUAD clinicopathology, suggesting these genes might play an important role in the progression of LUAD. Finally, based on EGRS genes, we built and calibrated a nomogram for conveniently evaluating patients' outcomes.

Glycolysis-related gene expression profiling serves as a novel prognosis risk predictor for human hepatocellular carcinoma

Metabolic pattern reconstruction is an important factor in tumor progression. Metabolism of tumor cells is characterized by abnormal increase in anaerobic glycolysis, regardless of high oxygen concentration, resulting in a significant accumulation of energy from glucose sources. These changes promotes rapid cell proliferation and tumor growth, which is further referenced a process known as the Warburg effect. The current study reconstructed the metabolic pattern in progression of cancer to identify genetic changes specific in cancer cells. A total of 12 common types of solid tumors were included in the current study. Gene set enrichment analysis (GSEA) was performed to analyze 9 glycolysis-related gene sets, which are implicated in the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes showed high area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). The findings of the current study showed that 8 genes (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were correlated with overall survival (OS) and recurrence-free survival (RFS). Further analysis showed that the prediction model accurately distinguished between high- and low-risk cancer patients among patients in different clusters in LIHC. A nomogram with a well-fitted calibration curve based on gene expression profiles and clinical characteristics showed good discrimination based on internal and external cohorts. These findings indicate that changes in expression level of metabolic genes implicated in glycolysis can contribute to reconstruction of tumor-related microenvironment.

NEAT1 silencing alleviates pulmonary arterial smooth muscle cell migration and proliferation under hypoxia through regulation of miR‑34a‑5p/KLF4 in vitro

Pulmonary arterial hypertension (PAH) is a severe vascular disease that adversely affects patient health and can be life threatening. The present study aimed to investigate the detailed role of nuclear paraspeckle assembly transcript 1 (NEAT1) in PAH. Using RT‑qPCR, the expression levels of NEAT1, microRNA (miR)‑34a‑5p, and Krüppel‑like factor 4 (KLF4) were detected in both hypoxia‑treated pulmonary arterial smooth muscle cells (PASMCs) and serum from PAH patients. Then, the interactions among miR‑34a‑5p, NEAT1, and KLF4 were evaluated by dual‑luciferase reporter assay. The detailed role of the NEAT1/miR‑34a‑5p/KLF4 axis in PAH pathogenesis was further explored using MTT, Transwell, and western blot assays. The results revealed that NEAT1 targeted miR‑34a‑5p and miR‑34a‑5p targeted KLF4. In hypoxia‑treated PASMCs and serum from PAH patients, high NEAT1 and KLF4 expression levels and low miR‑34a‑5p expression were observed. The proliferation and migration of hypoxia‑treated PASMCs were reduced by transfection with sh‑NEAT1 or miR‑34a‑5p mimics. The suppressive effects of NEAT1 knockdown on the proliferation and migration of hypoxia‑treated PASMCs were reversed by knock down of miR‑34a‑5p expression and increased KLF4 expression. NEAT1 was not only highly expressed in the serum of PAH patients but its silencing also alleviated PAH by regulating miR‑34a‑5p/KLF4 in vitro. The present study highlighted a potential new therapeutic target and diagnostic biomarker for PAH.

Development of a gene signature associated with iron metabolism in lung adenocarcinoma

There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Therefore, our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1), and patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P < 0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P < 0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. In addition, immune-related pathways were highlighted in the functional enrichment analysis. In conclusion, we developed and verified a 12-gene prognostic signature, which may be help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.

Knockdown of HCG18 Inhibits Cell Viability, Migration and Invasion in Pediatric Osteosarcoma by Targeting miR-188-5p/FOXC1 Axis

Understanding the underlying mechanisms of pediatric osteosarcoma (OS) migration and invasion is important for prognosis and treatment. We tried to measure the expression of long non-coding RNA HLA complex group 18 (HCG18) in OS and reveal its function in the malignant behaviors of OS cells. This study detected the expression of HCG18, miR-188-5p and forkhead box C1 (FOXC1) in OS tissues and cell lines by quantitative real-time PCR (qRT-PCR). The relevance between miR-188-5p and HCG18 or FOXC1 was affirmed by dual-luciferase reporter (DLR) assay. Cell viability was analyzed by MTT assay. Transwell assay was utilized to test cell invasion and migration. FOXC1 protein expression was detected by western blot. HCG18 expression was elevated in OS tissues, and enhanced HCG18 expression was related to metastasis. HCG18 silencing repressed the viability, migration and invasion of OS cells. Moreover, HCG18 interacted with miR-188-5p. MiR-188-5p up-regulation repressed cell viability, invasion and migration in OS cells. FOXC1, a known target of miR-188-5p, was negatively modulated by miR-188-5p. Furthermore, miR-188-5p inhibition or FOXC1 over-expression partially abolished the reduced of cell viability, invasion and migration mediated by HCG18 silencing in OS cell lines. This study revealed that HCG18 knockdown repressed the viability, invasion and migration of OS cells by targeting miR-188-5p and regulating FOXC1 expression. Thus, HCG18/ miR-188-5p/FOX may be a hopeful target for OS therapy.

LncRNA HCG18 Promotes Clear Cell Renal Cell Carcinoma Progression by Targeting miR-152-3p to Upregulate RAB14

Background

Long noncoding RNAs (lncRNAs) have been regarded as crucial regulators in many cancers, including clear cell renal cell carcinoma (ccRCC). This research aimed to explore the biological role and molecular mechanism of lncRNA HCG18 in ccRCC.

Materials and Methods

The expression levels of HCG18, miR-152-3p and RAB14 were examined by RT-qPCR. Cell viability, migration and invasion were examined by CCK-8 and transwell assays. Luciferase reporter and RIP assays were adopted to verify the interaction between miR-152-3p and HCG18 or RAB14.

Results

It was found that HCG18 expression was highly expressed in ccRCC tissues and cells, and patients with high expression of HCG18 had a short overall survival time. Moreover, HCG18 depletion attenuated ccRCC cell viability, migration and invasion. In addition, miR-152-3p was confirmed as a downstream target of HCG18 and was inversely regulated by HCG18, and RAB14 was a target of miR-152-3p. Functional assays demonstrated that miR-152-3p silencing or RAB14 addition abolished the inhibitory effects of HCG18 knockdown on ccRCC progression.

Conclusion

The results of the present study indicated that HCG18 accelerated the development and progression of ccRCC by upregulating RAB14 via sponging miR-152-3p, suggesting a potential therapeutic target for patients with ccRCC.

Identification of HCG18 and MCM3AP-AS1 That Associate With Bone Metastasis, Poor Prognosis and Increased Abundance of M2 Macrophage Infiltration in Prostate Cancer

Background:

Bone metastasis is a leading cause of the high mortality rate of prostate cancer (PCa), but curative strategies remain lacking. Recent studies suggest long non-coding RNAs (lncRNAs) may be potential targets to develop drugs. However, PCa bone metastasis-specifically-related lncRNAs were rarely reported. This study aimed to identify crucial lncRNAs and reveal their function mechanisms.

Methods:

GSE32269 and GSE26964 microarray datasets, downloaded from the Gene Expression Omnibus database, were used to analyze differentially expressed genes (DEGs)/lncRNAs (DELs) and miRNAs (DEMs), respectively. Weighted gene co-expression network analysis was performed to screen PCa bone metastasis-associated modules. The co-expression and competing endogenous RNAs (ceRNAs) networks were constructed to identify hub lncRNAs. Univariate Cox regression analysis was conducted to determine their prognostic values. The correlation of lncRNAs with immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource. Therapeutic drugs were predicted by querying the Connectivity Map (CMap) and the Comparative Toxicogenomics Database (CTD).

Results:

A total of 18 DELs, 2,614 DEGs and 86 DEMs were screened between bone metastatic and primary PCa samples. Four modules enriched by DEGs were shown to be bone metastasis-associated. LncRNA HCG18 and MCM3AP-AS1 were identified to be important because they existed in both of the co-expression and ceRNA networks (forming the relationship pairs: HCG18/MCM3AP-AS1-KNTC1, MCM3AP-AS1-hsa-miR-508-3p-DTL and HCG18/MCM3AP-AS1-hsa-miR-127-3p-CDKN3). All the genes in these interaction pairs were significantly associated with overall survival of PCa patients. Also, HCG18, MCM3AP-AS1 and their target mRNAs were positively correlated with various tumor-infiltrated immune cells, especially increased M2 macrophages. Valproic acid and trichostatin A may be effective to treat PCa bone metastasis by targeting HCG18 and MCM3AP-AS1.

Conclusion:

HCG18 and MCM3AP-AS1 that regulate M2 macrophage infiltration may be important targets to treat PCa bone metastasis and improve prognosis.