ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2018
[Differences in expression profiles of circular RNA between luminal breast cancer cells and normal breast cells].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- Hsa_circ_0011385 accelerates the progression of thyroid cancer by targeting miR-361-3p.Cancer cell international · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate the differences in the expression profiles of circular RNA (circRNA) between luminal breast cancer cells and normal breast cells.
methodsTotal RNA extracted from luminal breast cancer cells MCF7 and normal breast cells MCF10A was digested with Rnase R to remove linear RNAs and enrich circRNAs. The enriched circRNAs were amplified and transcribed into fluorescent cRNAs using a random priming method, and were hybridized onto the circRNA hybridization array. The circRNA expression profiles of MCF7 and MCF10A cells were analyzed using Agilent Feature Extraction software. Quantile normalization and subsequent data processing were performed, and volcano plot filtering and hierarchical clustering were utilized to analyze the circRNA expression patterns. The expressions of 3 circRNAs with significant log fold changes were validated using qPCR.
resultsThe hybridization array data revealed significant differences in the circRNA expression profiles between MCF7 and MCF10A cells. Compared with those of MCF10A cells, the 12910 circRNAs expressed in MCF7 cells showed 5964 up-regulated, 81 consistently regulated, and 6865 down-regulated circRNAs; 343 circRNAs showed a log fold change by more than 2 folds, among which 213 circRNAs were up-regulated and 130 were down-regulated. Nine circRNAs showed differential expressions by more than 2 folds, including 8 up-regulated ones, namely hsa_circRNA_061260 (6.02 folds), hsa_circRNA_103933 (5.96 folds), hsa_circRNA_005239 (5.84 folds), hsa_circRNA_100689 (5.69 folds), hsa_circRNA_004087 (5.60 folds), hsa_circRNA_104420 (5.25 folds), hsa_circRNA_104421 (5.13 folds) and hsa_circRNA_101222 (5.03 folds); only one circRNA was down-regulated, namely hsa_circRNA_104864 (5.09 folds). The expressions of hsa_circRNA_100689, hsa_circRNA_005239 and hsa_circRNA_104864 were further validated by qPCR, which yielded consistent results with the microarray data.
conclusionsThe circRNA expression profiles differ significantly between luminal breast cancer cells and normal breast cells. These differentially expressed circRNAs may serve as potential novel targets for the diagnosis of luminal breast cancer.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.