Expression analysis of vimentin and the related lncRNA network in breast cancer

Mohebi, Mehdi; Ghafouri‐Fard, Soudeh; Modarressi, Mohammad Hossein; Dashti, Sepideh; Zekri, Ali; Kholghi-Oskooei, Vahid; Taheri, Mohammad

doi:10.1016/j.yexmp.2020.104439

Cited by 29 publications

(16 citation statements)

References 20 publications

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“…Due to unknown pathogenesis, the mortality of LUAD patients is still high and the treatment outcome is also unsatisfactory ( 27 ). In recent years, lncRNAs have played a more significant role in the development of tumors and the disorder of lncRNA may be considered as a crucial factor in the activities of tumors ( 28 , 29 ). We have found that lncRNA participated in 33 kinds of cancers.…”

Section: Discussionmentioning

confidence: 99%

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Liu

et al. 2020

Front. Oncol.

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Background Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD). Methods RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method. Results A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance ( P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis. Conclusions We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

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Section: Discussionmentioning

confidence: 99%

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Liu

et al. 2020

Front. Oncol.

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“…The same cohort of patients has been used in our previous study ( 23 ). Demographic data of study participants is summarized in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

Expression Analysis of Long Non-Coding RNAs Related With FOXM1, GATA3, FOXA1 and ESR1 in Breast Tissues

et al. 2021

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Breast cancer is the most common neoplasm among females. Estrogen receptor (ESR) signaling has a prominent impact in the pathogenesis of breast cancer. Among the transcription factors associated with ESR signaling, FOXM1, GATA3, FOXA1 and ESR1 have been suggested as a candidate in the pathogenesis of this neoplasm. In the current project, we have designed an in silico approach to find long non-coding RNAs (lncRNAs) that regulate these transcription factors. Then, we used clinical samples to carry out validation of our in silico findings. Our systems biology method led to the identification of APTR, AC144450.1, linc00663, ZNF337.AS1, and RAMP2.AS1 lncRNAs. Subsequently, we assessed the expression of these genes in breast cancer tissues compared with the adjacent non-cancerous tissues (ANCTs). Expression of GATA3 was significantly higher in breast cancer tissues compared with ANCTs (Ratio of mean expressions (RME) = 4.99, P value = 3.12E−04). Moreover, expression levels of APTR, AC144450.1, and ZNF337.AS1 were elevated in breast cancer tissues compared with control tissues (RME = 2.27, P value = 5.40E−03; Ratio of mean expressions = 615.95, P value = 7.39E−19 and RME = 1.78, P value = 3.40E−02, respectively). On the other hand, the expression of RAMP2.AS1 was lower in breast cancer tissues than controls (RME = 0.31, P value = 1.87E−03). Expression levels of FOXA1, ESR1, and FOXM1 and linc00663 were not significantly different between the two sets of samples. Expression of GATA3 was significantly associated with stage (P value = 4.77E−02). Moreover, expressions of FOXA1 and RAMP2.AS1 were associated with the mitotic rate (P values = 2.18E−02 and 1.77E−02, respectively). Finally, expressions of FOXM1 and ZNF337.AS1 were associated with breastfeeding duration (P values = 3.88E−02 and 4.33E−02, respectively). Based on the area under receiver operating characteristics curves, AC144450.1 had the optimal diagnostic power in differentiating between cancerous and non-cancerous tissues (AUC = 0.95, Sensitivity = 0.90, Specificity = 0.96). The combination of expression levels of all genes slightly increased the diagnostic power (AUC = 0.96). While there were several significant pairwise correlations between expression levels of genes in non-tumoral tissues, the most robust correlation was identified between linc00663 and RAMP2.AS1 (r = 0.61, P value = 3.08E−8). In the breast cancer tissues, the strongest correlations were reported between FOXM1/ZNF337.AS1 and FOXM1/RAMP2.AS1 pairs (r = 0.51, P value = 4.79E−5 and r = 0.51, P value = 6.39E−5, respectively). The current investigation suggests future assessment of the functional role of APTR, AC144450.1 and ZNF337.AS1 in the development of breast neoplasms.

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“…To the best of our knowledge, the approach of the present study comparing differences between obese and non-obese patients with BC has so far not been applied. Previous studies investigated healthy non-obese versus obese patients ( 15 , 16 , 44 , 63 , 64 ) as well as healthy control cases versus patients with BC ( 65 – 68 ). Therefore, in the present study, lncRNA expression levels were evaluated in whole blood taken from BC patients by liquid biopsy, with obese patients being compared with non-obese patients, aiming to determine the expression status of lncRNAs in obese patients with BC and their associations with the general and clinicopathological attributes of the patients.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Suppression of circulating AP001429.1 long non‑coding RNA in obese patients with breast cancer

et al. 2021

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Long non-coding RNAs (lncRNAs), a type of cellular RNA, play a critical regulatory role in several physiological developments and pathological processes, such as tumorigenesis and tumor progression. Obesity is a risk factor for a number of serious health conditions, including breast cancer (BC). However, the underlying mechanisms behind the association between obesity and increased BC incidence and mortality remain unclear. Several studies have reported changes in lncRNA expression due to obesity and BC, independently encouraging further investigation of the relationship between the two in connection with lncRNAs. The present study was designed to first screen for the expression of 29 selected lncRNAs that showed a link to cancer or obesity in the blood of a selected cohort of 6 obese and 6 non-obese patients with BC. The expression levels of significantly expressed lncRNAs, AP001429.1, PCAT6, P5549, P19461 and P3134 , were further investigated in a larger cohort of 69 patients with BC (36 obese and 33 non-obese), using reverse transcription-quantitative polymerase chain reaction. Results showed not only that AP001429.1 remained significantly downregulated in the larger cohort (P=0.002), but also that it was associated with several clinicopathological characteristics, such as negative HER2 status, negative E-cadherin expression, negative vascular invasion, negative margin invasion and LCIS. These findings suggest that obesity may have a role in inhibiting AP001429.1 expression, which may serve as a novel potential biomarker and therapeutic target for BC.

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Expression analysis of vimentin and the related lncRNA network in breast cancer

Cited by 29 publications

References 20 publications

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Expression Analysis of Long Non-Coding RNAs Related With FOXM1, GATA3, FOXA1 and ESR1 in Breast Tissues

Suppression of circulating AP001429.1 long non‑coding RNA in obese patients with breast cancer

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