Identification of the consistently differential expressed hub mRNAs and proteins in lung adenocarcinoma and construction of the prognostic signature: a multidimensional analysis

Abstract

Background: This study aimed to elucidate the consistency of differentially expressed hub mRNAs and proteins in lung adenocarcinoma (LUAD) across populations and to construct a comprehensive LUAD prognostic signature.

Methods: The transcriptomic and proteomics data from different populations were standardized and analyzed using the same criteria to identify the consistently differential expressed mRNAs and proteins across genders and races. We then integrated prognosis-related mRNAs with clinical, pathological, and EGFR (epidermal growth factor receptor) mutation data to construct a survival model, subsequently validating it across populations. Through plasma proteomics, plasma proteins that consistently differential expressed with LUAD tissues were screened and validated, with their associations discerned by measuring expressions in tumor tissues and tumor vascular normalization.

Results: The consistency rate of differentially expressed mRNAs and proteins was ~20-40%, with ethnic factors leading to about 40-60% consistency of differentially expressed mRNA or protein across populations. The survival model based on the identified eight hub mRNAs as well as stage, smoking status, and EGFR mutations, demonstrated good prognostic prediction capabilities in both Western and East Asian populations, with a higher number of unfavorable variables indicating poorer LUAD prognosis. Notably, GPI expression in tumor tissues was inversely correlated with vascular normalization and positively correlated with plasma GPI expression.

Conclusion: Our study underscores the significance of integrating transcriptomics and proteomics data, emphasizing the need to account for genetic diversity among ethnic groups. The developed survival model may offer a holistic perspective on LUAD progression, enhancing prognosis and therapeutic strategies.

Figure 1

Differentially expressed mRNAs (proteins) in each database.

Figure 2

Consistency rate of differential mRNA and protein expression in the Western population. (A) Differentially upregulated and downregulated mRNAs and proteins in the Western population and consistency rates of upregulated and downregulated mRNAs and proteins. (B) Differentially upregulated and downregulated mRNAs and proteins in the male Western population and consistency rates of upregulated and downregulated mRNAs and proteins in males. (C) Differentially upregulated and downregulated mRNAs and proteins in the female Western population and consistency rates of upregulated and downregulated mRNAs and proteins in females.

Figure 3

Consistency rate of differential mRNA expression and differential protein expression between genders in the Western population. (A) Differentially upregulated mRNAs in the Western population between genders. (B) Differentially downregulated mRNAs in the Western population between genders. (C) Differentially upregulated proteins in the Western population between genders. (D) Differentially downregulated proteins in the Western population between genders.

Figure 4

Consistency rate of differential mRNA and protein expression in the Chinese population. (A) Differentially upregulated and downregulated mRNAs and proteins in the Chinese population and consistency rates of upregulated and downregulated mRNAs and proteins. (B) Differentially upregulated and downregulated mRNAs and proteins in the male Chinese population and consistency rates of upregulated and downregulated mRNAs and proteins in males. (C) Differentially upregulated and downregulated mRNAs and proteins in the female Chinese population and consistency rates of upregulated and downregulated mRNAs and proteins in females.

Figure 5

Correlation between mRNA and protein expression in the Chinese population.

Figure 6

Consistency rate of differential mRNA expression and differential protein expression between genders in the Chinese population. (A) Consistency rate of differential mRNA expression between genders in the Chinese population. (B) Consistency rate of differential protein expression between genders in the Chinese population.

Figure 7

Consistently differential expressed mRNAs and proteins between races. (A) Consistently differential expressed mRNAs between races. (B) Consistently differential expressed proteins between races.

Figure 8

Forest plot of hazard ratio (HR) of prognosis-related mRNA expression in the different populations. (A) Survival results of eight hub mRNAs in Chinese and Western populations. (B) Survival results of eight hub mRNAs in Japanese and the U.S. population.

Figure 9

Screening flowchart for prognostic-related hub mRNAs.

Figure 10

Survival model results. (A) Survival model from eight hub mRNAs in Chinese and Western populations. (B) Survival model from eight hub mRNAs, stage, and smoking status in Chinese and Western population. (C) Validation of survival model from eight hub mRNAs, stage, and smoking status in Japanese and the U.S. population. (D) Validation of survival model from eight hub mRNAs, stage, smoking status, and EGFR mutation in Chinese and Japanese population.

Figure 11

GO and KEGG pathway analysis of the prognosis-related mRNAs. (A) GO chord plot the eight most significant GO terms of the prognosis-related mRNAs. (B) Scatter plot of enriched KEGG pathways statistics. The color and size of the dots represent the range of the P-value and gene ratios mapped to the indicated pathways, respectively.

Figure 12

Correlation between vascular normalization and GPI expression in LUAD plasma and tissues. (A) GPI expression in ‘Blood+’ screening and validation phases. (B) Immunochemical staining results of GPI in tumor tissues. Scale: 200 µM. (C) Immunofluorescence photographs of LUAD tumor tissue with CD31 and α-SMA staining. Scale: 200 µM. (D) Correlation analysis between the GPI expression in tumor tissues and plasma; Correlation analysis between the GPI expression in tumor tissues and vascular normalization; Correlation analysis between the GPI expression in plasma and vascular normalization.