Bone metastasis is one of the most serious complications in lung cancer patients. MicroRNAs (miRNAs) play important roles in tumour development, progression and metastasis. A previous study showed that miR-106a is highly expressed in the tissues of lung adenocarcinoma with bone metastasis, but its mechanism remains unclear. In this study, we showed that miR-106a expression is dramatically increased in lung cancer patients with bone metastasis (BM) by immunohistochemical analysis. MiR-106a promoted A549 and SPC-A1 cell proliferation, migration and invasion in vitro. The results of bioluminescence imaging (BLI), micro-CT and X-ray demonstrated that miR-106a promoted bone metastasis of lung adenocarcinoma in vivo. Mechanistic investigations revealed that miR-106a upregulation promoted metastasis by targeting tumour protein 53-induced nuclear protein 1 (TP53INP1)-mediated metastatic progression, including cell migration, autophagy-dependent death and epithelial–mesenchymal transition (EMT). Notably, autophagy partially attenuated the effects of miR-106a on promoting bone metastasis in lung adenocarcinoma. These findings demonstrated that restoring the expression of TP53INP1 by silencing miR-106a may be a novel therapeutic strategy for bone metastatic in lung adenocarcinoma.
This study aimed to construct a widely accepted prognostic nomogram in Chinese high-grade osteosarcoma (HOS) patients aged ≤ 30 years to provide insight into predicting 5-year overall survival (OS). Data from 503 consecutive HOS patients at our centre between 12/2012 and 05/2019 were retrospectively collected. Eighty-four clinical features and routine laboratory haematological and biochemical testing indicators of each patient at the time of diagnosis were collected. A prognostic nomogram model for predicting OS was constructed based on the Cox proportional hazards model. The performance was assessed by the concordance index (C-index), receiver operating characteristic curve and calibration curve. The utility was evaluated by decision curve analysis. The 5-year OS was 52.1% and 2.6% for the nonmetastatic and metastatic patients, respectively. The nomogram included nine important variables based on a multivariate analysis: tumour stage, surgical type, metastasis, preoperative neoadjuvant chemotherapy cycle, postoperative metastasis time, mean corpuscular volume, tumour-specific growth factor, gamma-glutamyl transferase and creatinine. The calibration curve showed that the nomogram was able to predict 5-year OS accurately. The C-index of the nomogram for OS prediction was 0.795 (range, 0.703–0.887). Moreover, the decision curve analysis curve also demonstrated the clinical benefit of this model. The nomogram provides an individualized risk estimate of the 5-year OS in patients with HOS aged ≤ 30 years in a Chinese population-based cohort.
Background: The most ordinary subtype of lung cancer is lung adenocarcinoma (LuAC), which is characterized by strong metastatic ability. And LuAC rates in Xuanwei leads to the poor prognosis and high death rate. In this study, we systematically explored the molecular mechanism of LuAC bone metastasis in Xuanwei by transcriptome sequencing.Methods: RNA Sequencing was conducted to explore the noncoding RNAs (ncRNAs) expression profiles in primary LuAC and LuAC bone metastasis. We identified differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), lncRNAs (DElncRNAs) and circRNAs (DEcircRNAs). Bioinformatics analyses the possible relationships and functions of the LuAC bone metastasis-related competing endogenous RNA (ceRNA). And qRT-PCR was performed to evaluate the expression of these differently expressed genes in serum.Results: A total of 2,141 DEmRNAs, 43 DEmiRNAs, 136 DElncRNAs and 706 DEcircRNAs were identified in the Xuanwei patients with primary LuAC vs. LuAC bone metastasis, respectively. The circRNA/miRNA/mRNA and lncRNA/miRNA/mRNA networks of LuAC in Xuanwei with bone metastasis were built, and the gene expression mechanisms regulated by ncRNAs were unveiled via the ceRNA regulatory networks. We observe that lncRNA (ADAMTS9-AS2, TEX41, DLEU2, LINC00152)-miR-223-3p-SCARB1 and hsa_circ_0000053-miR-196a-5p/miR-196b-5p-HOXA5 ceRNA networks might play an important role in bone metastasis of Xuanwei LuAC. Conclusions:We comprehensively identified ceRNA regulatory networks of LuAC in Xuanwei with bone metastasis as well as revealed the contribution of different ncRNAs expression profiles. Our data demonstrate the association between mRNAs and ncRNAs in the metastasis mechanism of LuAC in Xuanwei with bone metastasis.
This study purposed to construct a widely accepted prognostic nomogram in Chinese high-grade osteosarcoma (HOS) patients aged ≤ 30 years to provide insight into predicting 5-year overall survival (OS). Data of 503 consecutive HOS patients at our centre between 12/2012 and 05/2019 were retrospectively collected. The 84 clinical features, routine laboratory haematological and biochemical testing indicators of each patient at the time of diagnosis were collected. A prognostic nomogram model for predicting OS was constructed based on Cox proportional hazards model. The performance was assessed by concordance index (C-index), receiver operating characteristic curve and calibration curve. The utility was evaluated by decision curve analysis. The 5-year OS was 52.1% and 2.6% for the nonmetastatic and metastatic patients, respectively. The nomogram included nine important variables based on a multivariate analysis: tumour stage, surgical type, metastasis, preoperative neoadjuvant chemotherapy cycle, postoperative metastasis time, mean corpuscular volume, tumour-specific growth factor, gamma-glutamyl transferase and creatinine. The calibration curve showed that the nomogram was able to predict 5-year OS accurately. The C-index of the nomogram for OS prediction was 0.795 (range, 0.703-0.887). Moreover, the decision curve analysis curve also demonstrated the clinical benefit of this model. The nomogram provides an individualized risk estimate of the 5-year OS in patients with HOS aged ≤ 30 years in a Chinese population-based cohort.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.