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. 2023 Mar 15;13(3):727-757.
eCollection 2023.

An in-silico approach leads to explore six genes as a molecular signatures of lung adenocarcinoma

Mostafa A Abdel-Maksoud  1 , Fatima Hassan  2 , Uzma Mubarik  3 , Ayman Mubarak  1 , Mohamed A Farrag  1 , Sumaiah Alghamdi  1 , Sampson Agyapong Atuahene  4 , Sally Almekhlafi  1 , Mohammed Aufy  5
Affiliations

An in-silico approach leads to explore six genes as a molecular signatures of lung adenocarcinoma

Mostafa A Abdel-Maksoud et al. Am J Cancer Res. .

Abstract

Due to heterogenetic-specific nature of the available biomarkers, the incidence of lung adenocarcinoma (LUAD) is on the rise worldwide. Previously reported LUAD-related hub genes were searched from the medical literature via literature mining and were processed to identify few top genes via degree method. Later, a comprehensive in silico methodology was applied on the selected real hub genes to identify their tumor driving, diagnostic, and prognostic roles in LUAD patients with divers clinicopathological variables. Out of total 145 extracted hub genes, six genes including CDC6, PBK, AURKA, KIF2C, OIP5, and PRC1 were identified as real hub genes. The expression analysis showed that all these genes were significantly up-regulated across LUAD samples of different clinicopathological variables. In addition, a variety of unique correlations among the expression and of real hub genes and some other parameters including promoter methylation status, overall survival (OS), genetic changes, tumor purity, and immune cell infiltration have also been explored in the present study. Moreover, via TFS-miRNA-mRNA regulatory network, one important TF (E2F1) and one important miRNAs (hsa-mir-34a-5p) that targeted all the real hub genes were also identified. Finally, a variety of drugs also predicted to be very useful in treating LUAD. The discovery of the real hub genes, TFS-miRNA-mRNA network, and chemotherapeutic drugs associated with LUAD provides new insights into underlying mechanisms and treatment of LUAD overcoming heterogeneity barriers.

Keywords: Lung adenocarcinoma; PubMed; biomarker; hub gene; miRNA.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
(A) A PPI network showing LUAD-related extracted hub genes from the selected studies. (B) A PPI network showing one significant module, (C) A PPI network of the hub genes identified in the significant module, and (D) Six real hub genes identified via degree method.
Figure 2
Figure 2
GO and KEGG enrichment analysis of real hub genes. (A) BF, (B) CC, (C) MF, and (D) KEGG analysis. A P-value <0.05 was consider as significant.
Figure 3
Figure 3
mRNA expression analysis results of real hub genes in LUAD and normal controls via UALCAN. (A) CDC6, (B) PBK, (C) AURKA, (D), KIF2C, (E) OIP5, and (F) PRC1.
Figure 4
Figure 4
mRNA and protein expression level validations of the real hub genes in LUAD patients paired with controls via different online expression databases. (A) mRNA expression level validation of real hub genes via TIMER, (B) mRNA expression level validation of real hub genes via GENT2, (C) mRNA expression level validation of real hub genes via GEPIA, (D) mRNA expression level validation of real hub genes via DriverDBV3, and (E) Protein expression level validation of real hub genes via UALCAN.
Figure 5
Figure 5
Relative mRNA expression analysis results of real hub genes in LUAD patients stratified by different clinicopathological features. (A) Cancer stage (B) Patient’s race, (C) Patient’s gender, (D) Patient’s age, and (E) Nodal metastasis status.
Figure 6
Figure 6
Correlation of promoter methylation with mRNA expression of real hub genes in LUAD paired with controls. (A) CDC6, (B) PBK, (C) AURKA, (D), KIF2C, (E) OIP5, and (F) PRC1.
Figure 7
Figure 7
Association between OS and real hub genes expression in LUAD patients. (A) via KM plotter and (B) via GEPIA tool.
Figure 8
Figure 8
Frequency and distribution of genomic alterations associated with real hub genes in LUAD. (A) Frequency of genomic alteration, (B) distribution of mutations in protein domains of real hub genes, and (C) types of CNVs in LUAD samples.
Figure 9
Figure 9
Correlation analysis of real hub genes expression with tumor purity, CD4+ T, and CD8+ T cells in LUAD. (A) CDC6, (B) PBK, (C) AURKA, (D) KIF2C, (E) OIP5, and (F) PRC1.
Figure 10
Figure 10
TF-miRNA-mRNA network analysis of real hub genes in LUAD. (A) miRNAs targeting real hub genes, (B) has-mir-34a-5p miRNA targeting real hub genes, (C) relative expression of has-mir-34a-5p in LUAD and normal controls, (D) TFS targeting real hub genes, (E) E2F1 targeting real hub genes, and (F) relative expression analysis of E2F1 in LUAD samples paired with controls. The pink and grey nodes represent the miRNA, red nodes represent the hub gene, while green node represent the TFS.
Figure 11
Figure 11
Real hub genes and different divers states association in LUAD. (A) Correlation analysis of real hub genes expression with 14 different states in LUAD, and (B) Correlation analysis of real hub genes expression only significant states in LUAD.
Figure 12
Figure 12
CDC6, PBK, AURKA, KIF2C, OIP5, and PRC1 genes positively correlated mutant genes in LUAD from MuTarget. (A) with CDC6, (B) with PBK, (C) with AURKA, (D) with KIF2C, (E) with OIP5, and (F) with PRC1.
Figure 13
Figure 13
Screening of real hub genes-associated chemotherapeutic drugs. (A-F) indicates chemotherapeutic drugs that can decrease or increase the expression of the real hub genes. (A) CDC6-associated chemotherapeutic drugs, (B) PBK-associated chemotherapeutic drugs, (C) AURKA-associated chemotherapeutic drugs, (D) KIF2C-associated chemotherapeutic drugs, (E) OIP5-associated chemotherapeutic drugs, and (F) PRC1-associated chemotherapeutic drugs. Red arrows: drugs that increase the real hub genes expression, Green arrows: drug that decrease the real hub genes expression while the numbers of arrows represent the supported numbers of studies in literature.
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