Biomolecules, Vol. 12, Pages 1756: EIF4G1 Is a Potential Prognostic Biomarker of Breast Cancer

Conceptualization, G.T.; methodology, K.L, G.T., X.Z., W.L., J.R. and Y.S.; software, G.T. and B.X.; validation, formal analysis, data curation, visualization, and project administration, K.L, G.T. and X.Z.; investigation, X.Z.; resources, M.W.; writing—original draft preparation, K.L.; writing—review and editing, G.T., M.W., B.X., E.A.A.-G., F.L. and Y.W.; supervision and funding acquisition, M.W. and B.X. All authors have read and agreed to the published version of the manuscript.

Figure 1. The workflow chart of the study.

Figure 1. The workflow chart of the study.

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Figure 2. EIF4G1 is upregulated in pan-cancer and is involved in numerous processes. (a) Functional states of EIF4G1 and its association with 14 different types of cancers in the CancerSEA database; (b) The expression levels of EIF4G1 in different cancer types from the TCGA database, as analyzed with TIMER (* p < 0.05, *** p < 0.001).

Figure 2. EIF4G1 is upregulated in pan-cancer and is involved in numerous processes. (a) Functional states of EIF4G1 and its association with 14 different types of cancers in the CancerSEA database; (b) The expression levels of EIF4G1 in different cancer types from the TCGA database, as analyzed with TIMER (* p < 0.05, *** p < 0.001).

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Figure 3. Upregulated EIF4G1 in two cohorts, and the results from KM survival analyses. (a,b) Different expression of EIF4G1 between breast cancer tissues and adjacent normal tissues in TCGA cohort and GSE42568; (c) KM survival analysis of high- and low-expression groups in TCGA-training cohort; (d) KM survival curve of the high- and low-expression groups in the meta-validation dataset.

Figure 3. Upregulated EIF4G1 in two cohorts, and the results from KM survival analyses. (a,b) Different expression of EIF4G1 between breast cancer tissues and adjacent normal tissues in TCGA cohort and GSE42568; (c) KM survival analysis of high- and low-expression groups in TCGA-training cohort; (d) KM survival curve of the high- and low-expression groups in the meta-validation dataset.

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Figure 4. Functional annotations. (a) Top ten GO terms enrichment analysis of DEGs; (b) Top ten KEGG pathway enrichment analysis of DEGs. Gly, Glycine; Ser, serine; Thr, threonine.

Figure 4. Functional annotations. (a) Top ten GO terms enrichment analysis of DEGs; (b) Top ten KEGG pathway enrichment analysis of DEGs. Gly, Glycine; Ser, serine; Thr, threonine.

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Figure 5. ESTIMATE scores distribution and TIICs analysis by CIBERSORT. (a, b) Distribution of ISs and SSs between EIF4G1 high- and low-expression groups; (c) A bar chart showing the difference in the proportion of 22 TIICs in the TME of BRCA; (d) A boxplot comparing the proportion of the 22 TIICs in the TME of BRCA between the high- and low-expression groups (* p < 0.05, ** p < 0.01, *** p < 0.001; ns, not significant).

Figure 5. ESTIMATE scores distribution and TIICs analysis by CIBERSORT. (a, b) Distribution of ISs and SSs between EIF4G1 high- and low-expression groups; (c) A bar chart showing the difference in the proportion of 22 TIICs in the TME of BRCA; (d) A boxplot comparing the proportion of the 22 TIICs in the TME of BRCA between the high- and low-expression groups (* p < 0.05, ** p < 0.01, *** p < 0.001; ns, not significant).

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Figure 6. Correlation analysis of EIF4G1 to the immune checkpoint, TIDE score, and IPS. (a) CTLA4, (b) PD-1, (c) PDL1, (d) GZMB, and (e) LAG3 from GEPIA. (f) TIDE score between EIF4G1 high- and low-expression expression groups. (g) The IPS, (h) IPS-PD-1/PD-L1/PD-L2, (i) IPS-CTLA4, and (j) IPS-PD-1/PD-L1/PD-L2 + CTLA4.

Figure 6. Correlation analysis of EIF4G1 to the immune checkpoint, TIDE score, and IPS. (a) CTLA4, (b) PD-1, (c) PDL1, (d) GZMB, and (e) LAG3 from GEPIA. (f) TIDE score between EIF4G1 high- and low-expression expression groups. (g) The IPS, (h) IPS-PD-1/PD-L1/PD-L2, (i) IPS-CTLA4, and (j) IPS-PD-1/PD-L1/PD-L2 + CTLA4.

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Figure 7. IHC staining validating the expression level and prognostic value of EIF4G1 in BRCA. (a) Representative IHC images of EIF4G1 in tumors and non-cancerous breast tissues. Scale bars were 500 μm and 50 μm; (b) Expression level of EIF4G1 in BRCA tissues and non-cancerous breast tissues; (c) Differences in OS between EIF4G1 high- and low-expression groups, as determined with the KM curve.

Figure 7. IHC staining validating the expression level and prognostic value of EIF4G1 in BRCA. (a) Representative IHC images of EIF4G1 in tumors and non-cancerous breast tissues. Scale bars were 500 μm and 50 μm; (b) Expression level of EIF4G1 in BRCA tissues and non-cancerous breast tissues; (c) Differences in OS between EIF4G1 high- and low-expression groups, as determined with the KM curve.

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Table 1. Clinical features of BRCA patients in the TCGA and meta-validation dataset.

Table 1. Clinical features of BRCA patients in the TCGA and meta-validation dataset.

Clinical
FeaturesTCGA
(n = 1083)GSE42568
(n = 104)GSE88770
(n = 117)OS
Alive
Dead
933 (86.15%)
150 (13.85%)
69 (66.35%)
35 (33.65%)
89 (76.07%)
28 (23.93%)Age
≤58
>58
545 (50.32%)
538 (49.68%)
56 (53.85%)
48 (46.15%)
-
-Grade
G1
G2
G3
-
-
-
11 (10.58%)
40 (38.46%)
53 (50.96%)
13 (11.11%)
96 (82.05%)
7 (5.98%)PR status
Positive
Negative
Indeterminate
688 (63.53%)
342 (31.58%)
4 (0.37%)
-
-
-
79 (67.52%)
37 (31.62%)
ER status
Positive
Negative
Indeterminate
795 (73.41%)
238 (21.98%)
2 (0.18%)
-
-
-
106 (90.60%)
11 (9.40%)
-HER2 status
Positive
Negative
Indeterminate
161 (14.87%)
557 (51.43%)
12 (1.11%)
-
-
-
7 (5.98%)
108 (92.31%)
-Stage
Stage I
Stage II
Stage III
Stage IV
Stage X
182 (16.81%)
613 (56.60%)
247 (22.81%)
19 (1.75%)
14 (1.29%)
-
-
-
-
-
-
-
-
-
-T stage
T1
T2
T3
T4
279 (25.76%)
624 (57.62%)
138 (12.74%)
39 (3.60%)
-
-
-
-
-
-
-
-N stage
N0
N1
N2
N3
512 (47.28%)
356 (32.87%)
119 (10.99%)
76 (7.02%)
-
-
-
-
-
-
-
-M stage
M0
M1
901 (83.19%)
21 (1.94%)
-
-
-
-

Table 2. A list of screened compounds with highly negative enrichment scores.

Table 2. A list of screened compounds with highly negative enrichment scores.

RankScoreNameDescriptionTarget8549−94.63KIN001-220Aurora kinase inhibitorAURKA8534−79.24DigitoxigeninATPase inhibitorATP1A18530−71.17EpothiloneMicrotubule inhibitorTUBA1A, TUBA1B, TUBA1C, TUBA3C, TUBA4A, TUBA8, TUBB, TUBB1, TUBB3, TUBB4A, TUBB4B8529−69.93Dihydro-7-desacetyldeoxygeduninHSP inhibitorHSP90AA18528−67.25FludrocortisoneGlucocorticoid receptor agonistNR3C2, AR, NR3C1

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