Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 May 15;12(5):2397-2418.
eCollection 2022.

Bioinformatics analysis of potential Key lncRNA-miRNA-mRNA molecules as prognostic markers and important ceRNA axes in gastric cancer

Siqi Tang  1   2 , Keyong Liao  1   2 , Yongpeng Shi  1   2 , Tingting Tang  1 , Beibei Cui  1 , Zunnan Huang  1   3
Affiliations

Bioinformatics analysis of potential Key lncRNA-miRNA-mRNA molecules as prognostic markers and important ceRNA axes in gastric cancer

Siqi Tang et al. Am J Cancer Res. .

Abstract

Gastric cancer (GC), the fifth most common malignancy worldwide, has an extremely poor prognosis at the advanced stage or the early stage if inadequately treated. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs all function as competing endogenous RNAs (ceRNAs) that target and regulate each other. Changes in their expression and their regulatory bioprocesses play important roles in GC. However, the roles of key RNAs and their regulatory networks remain unclear. In this study, RNA profiles were extracted from The Cancer Genome Atlas database, and R language was used to discover the differentially expressed (DE) lncRNAs, miRNAs and mRNAs in GC. Then, the DERNAs were paired by miRcode, miRDB, TargetScan and DIANA, and the ceRNA network was further constructed and visualized using Cytoscape. Moreover, a functional enrichment analysis was performed using Metascape. Afterward, the "survival" package was employed to identify candidate prognostic targets (DERNA-os) in the ceRNA network. Ultimately, the ceRNA network was analyzed to identify crucial lncRNA/miRNA/mRNA axes. Based on 374 gastric adenocarcinoma and gastric adenoma samples, 283 DEceRNAs (69 lncRNAs, 10 miRNAs, and 204 mRNAs) were identified. The 204 mRNAs were significantly enriched in some interesting functional clusters, such as the trans-synaptic signaling cluster and the protein digestion and absorption cluster. The ceRNA network consisted of 43 ceRNAs (13 lncRNAs, 2 miRNAs, and 28 mRNAs) that were related to prognosis. Among them, 2 lncRNAs (LNC00469 and AC010145.1) and 1 mRNA (PRRT4) were potential new biomarkers. In addition, according to the lncRNA/miRNA/mRNA regulatory relationships among the 43 ceRNAs, we identified four axes that might play important roles in the progression of GC and investigated the potential mechanism of the most promising axis (POU6F2-AS2/hsa-mir-137/OPCML) in promoting the proliferation and invasiveness of GC.

Keywords: Gastric cancer; ceRNA; lncRNA; mRNA; miRNA.

PubMed Disclaimer

Conflict of interest statement

None.

Figures

Figure 1
Figure 1
Analysis of prognostic biomarkers and key axes. The analysis includes specific bioinformatics methods, data processing tools, and partial research results.
Figure 2
Figure 2
Heatmap and volcano plots of RNAs. With |log2FC| > 2.0 and FDR < 0.05 as the standards, heatmaps and volcano plots were drawn to illustrate the outcome of the differential expression analysis. The red dots denote significantly upregulated RNAs, and the green dots denote significantly downregulated RNAs. Ai. Heatmap of lncRNAs; Bi. heatmap of miRNAs; Ci. heatmap of mRNAs. The abscissa represents the log2 transformation value of the fold change in differential expression between GC samples and normal samples. The larger the |log2FC| value, the greater the fold change. The ordinate represents the -log10 transformation of the FDR value. The larger the -log10 transformation value, the more significant the difference. Aii. Volcano plot of lncRNAs; Bii. volcano plot of miRNAs; Cii. volcano plot of mRNAs. The abscissa represents logFC, where the farther the point deviates from the center, the greater the fold change; the negative half represents downregulation, and the positive half represents upregulation. The ordinate represents -log (FDR), where the closer the point is to the top, the more significant the difference in expression between GC samples and normal samples.
Figure 3
Figure 3
The ceRNA network diagram. Red indicates upregulated RNA expression, green indicates downregulated RNA expression, diamonds represent lncRNAs, squares represent miRNAs, and circles represent mRNAs. 69 DElncRNAs, 10 DEmiRNAs and 204 DEmRNAs were included in the network. They targeted each other and paired to form 154 DElncRNA-DEmiRNA pairs and 288 DEmiRNA-DEmRNA pairs.
Figure 4
Figure 4
Functional enrichment analysis of 204 mRNAs. A. Bar graph of enriched terms across input gene lists colored by P values. B. Network of enriched terms colored by P value, where terms containing more genes tend to have a more significant P value. C. Network of enriched terms colored by cluster ID, where nodes that share the same cluster ID are typically close to each other.
Figure 5
Figure 5
Survival curves of lncRNAs and miRNAs in the ceRNA network. (A-M) show the survival curves of 13 lncRNAs, while (N and O) show the survival curves of 2 miRNAs. The x-axis represents overall survival time, and the y-axis represents the overall survival rate. P < 0.05 is considered statistically significant.
Figure 6
Figure 6
Establishment of 4 lncRNA/miRNA/mRNA axes. The blank rectangles represent the screening process, the blue rectangles represent the components of the ceRNA network, the green rectangles represent candidate prognostic targets, and the purple rectangles represent the regulatory axes.
Figure 7
Figure 7
Mechanism plot (A) and evidence map (B) of the POU6F2-AS2/hsa-mir-137/OPCML axis and YBX1 in gastric cancer.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–249. - PubMed
    1. Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl) 2021;134:783–791. - PMC - PubMed
    1. Chen JG, Chen HZ, Zhu J, Yang YL, Zhang YH, Huang PX, Chen YS, Zhu CY, Yang LP, Shen K, Qiang FL, Wang GR. Cancer survival in patients from a hospital-based cancer registry, China. J Cancer. 2018;9:851–860. - PMC - PubMed
    1. Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C, Kodzius R, Shimokawa K, Bajic VB, Brenner SE, Batalov S, Forrest AR, Zavolan M, Davis MJ, Wilming LG, Aidinis V, Allen JE, Ambesi-Impiombato A, Apweiler R, Aturaliya RN, Bailey TL, Bansal M, Baxter L, Beisel KW, Bersano T, Bono H, Chalk AM, Chiu KP, Choudhary V, Christoffels A, Clutterbuck DR, Crowe ML, Dalla E, Dalrymple BP, de Bono B, Della Gatta G, di Bernardo D, Down T, Engstrom P, Fagiolini M, Faulkner G, Fletcher CF, Fukushima T, Furuno M, Futaki S, Gariboldi M, Georgii-Hemming P, Gingeras TR, Gojobori T, Green RE, Gustincich S, Harbers M, Hayashi Y, Hensch TK, Hirokawa N, Hill D, Huminiecki L, Iacono M, Ikeo K, Iwama A, Ishikawa T, Jakt M, Kanapin A, Katoh M, Kawasawa Y, Kelso J, Kitamura H, Kitano H, Kollias G, Krishnan SP, Kruger A, Kummerfeld SK, Kurochkin IV, Lareau LF, Lazarevic D, Lipovich L, Liu J, Liuni S, McWilliam S, Madan Babu M, Madera M, Marchionni L, Matsuda H, Matsuzawa S, Miki H, Mignone F, Miyake S, Morris K, Mottagui-Tabar S, Mulder N, Nakano N, Nakauchi H, Ng P, Nilsson R, Nishiguchi S, Nishikawa S, Nori F, Ohara O, Okazaki Y, Orlando V, Pang KC, Pavan WJ, Pavesi G, Pesole G, Petrovsky N, Piazza S, Reed J, Reid JF, Ring BZ, Ringwald M, Rost B, Ruan Y, Salzberg SL, Sandelin A, Schneider C, Schönbach C, Sekiguchi K, Semple CA, Seno S, Sessa L, Sheng Y, Shibata Y, Shimada H, Shimada K, Silva D, Sinclair B, Sperling S, Stupka E, Sugiura K, Sultana R, Takenaka Y, Taki K, Tammoja K, Tan SL, Tang S, Taylor MS, Tegner J, Teichmann SA, Ueda HR, van Nimwegen E, Verardo R, Wei CL, Yagi K, Yamanishi H, Zabarovsky E, Zhu S, Zimmer A, Hide W, Bult C, Grimmond SM, Teasdale RD, Liu ET, Brusic V, Quackenbush J, Wahlestedt C, Mattick JS, Hume DA, Kai C, Sasaki D, Tomaru Y, Fukuda S, Kanamori-Katayama M, Suzuki M, Aoki J, Arakawa T, Iida J, Imamura K, Itoh M, Kato T, Kawaji H, Kawagashira N, Kawashima T, Kojima M, Kondo S, Konno H, Nakano K, Ninomiya N, Nishio T, Okada M, Plessy C, Shibata K, Shiraki T, Suzuki S, Tagami M, Waki K, Watahiki A, Okamura-Oho Y, Suzuki H, Kawai J, Hayashizaki Y. The transcriptional landscape of the mammalian genome. Science. 2005;309:1559–1563. - PubMed
    1. Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, Xue C, Marinov GK, Khatun J, Williams BA, Zaleski C, Rozowsky J, Röder M, Kokocinski F, Abdelhamid RF, Alioto T, Antoshechkin I, Baer MT, Bar NS, Batut P, Bell K, Bell I, Chakrabortty S, Chen X, Chrast J, Curado J, Derrien T, Drenkow J, Dumais E, Dumais J, Duttagupta R, Falconnet E, Fastuca M, Fejes-Toth K, Ferreira P, Foissac S, Fullwood MJ, Gao H, Gonzalez D, Gordon A, Gunawardena H, Howald C, Jha S, Johnson R, Kapranov P, King B, Kingswood C, Luo OJ, Park E, Persaud K, Preall JB, Ribeca P, Risk B, Robyr D, Sammeth M, Schaffer L, See LH, Shahab A, Skancke J, Suzuki AM, Takahashi H, Tilgner H, Trout D, Walters N, Wang H, Wrobel J, Yu Y, Ruan X, Hayashizaki Y, Harrow J, Gerstein M, Hubbard T, Reymond A, Antonarakis SE, Hannon G, Giddings MC, Ruan Y, Wold B, Carninci P, Guigó R, Gingeras TR. Landscape of transcription in human cells. Nature. 2012;489:101–108. - PMC - PubMed

LinkOut - more resources