Long Noncoding RNA GMAN, Up-regulated in Gastric Cancer Tissues, Is Associated With Metastasis in Patients and Promotes Translation of Ephrin A1 by Competitively Binding GMAN-AS
Bibliographic record
Abstract
BACKGROUND & AIMS: We aimed to identify long noncoding RNAs (lncRNAs) that are up-regulated in gastric cancer tissues from patients and study their function in gastric tumor metastasis. METHODS: We collected gastric tumor and nontumor tissues from patients in China and analyzed levels of lncRNAs by microarray analysis, proteins by immunohistochemistry, and RNAs by quantitative reverse-transcription polymerase chain reaction; we compared these with survival times of patients and tumor progression. RNA levels were knocked down or knocked out in BGC-823, SGC-7901, and MKN45 cell lines using small interfering or short hairpin RNAs or clustered regularly interspaced short palindromic repeats (ie, CRISPR)/CRISPR associated protein 9 (ie, Cas9) vectors. Genes were overexpressed from transfected plasmids in HGC-27 cells. Cells were analyzed by Northern blot and immunoblot, polysome profiling assay, and cell invasion assay. Cells were injected into the tail veins or spleens of nude mice or SCID mice; lung and liver tissues were collected, and metastases were counted. lncRNAs were cloned by using rapid amplification of complementary DNA ends. Their interactions with other genes were determined by RNA pulldown and mapping assays. RESULTS: In microarray analyses, we identified 151 lncRNAs expressed at significantly higher levels in gastric tumor vs nontumor tissues. Levels of an lncRNA that we called gastric cancer metastasis associated long noncoding RNA (GMAN) were increased in gastric tumor tissues, compared with nontumor tissues; its up-regulation was associated with tumor metastasis and shorter survival times of patients. The GMAN gene overlaps with the ephrin A1 gene (EFNA1) and was highly expressed in BGC-823 and MKN45 cells. Knockdown of GMAN in these cells did not affect proliferation, colony formation, or adhesion but did reduce their invasive activity in Transwell assays. Ectopic expression of GMAN increased the invasive activity of HGC-27 cells. BGC-823 and MKN45 cells with knockdown of GMAN formed fewer metastases after injection into tail veins of nude mice. Knockdown or knockout of GMAN also reduced levels of ephrin A1 protein in cells. We found that GMAN promoted translation of ephrin A1 messenger RNA into protein by binding to the antisense GMAN RNA (GMAN-AS)-this antisense sequence is also complementary to that of ephrin A1 mRNA. Levels of ephrin A1 protein were also increased in gastric tumors from patients with metastases than in those without metastases. Knockout of ephrin A1 in BGC-823 cells reduced their invasive activity in Transwell assays and ability to form metastases after injection into SCID mice. Ectopic expression of ephrin A1 in BGC-823 cells with knockdown or knockout of GMAN restored their invasive activities and ability form metastases in nude or SCID mice. A CRISPR/Cas9-based strategy to disrupt the GMAN gene significantly reduced the numbers of metastases formed from SGC-7901 cells in mice. CONCLUSIONS: We identified an lncRNA, which we call GMAN, that is increased in gastric tumors from patients and associated with survival and formation of metastases. It regulates translation of ephrin A1 mRNA by binding competitively to GMAN-AS. Knockdown or knockout of GMAN or ephrin A1 in gastric cancer cell lines reduces their invasive activity and ability to form metastases after injection into mice. These genes might be targeted to prevent or reduce gastric cancer metastasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".