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. 2023 Mar 1;20(1):8.
doi: 10.1186/s12014-023-09399-9.

Proteomic analysis identifies dysregulated proteins and associated molecular pathways in a cohort of gallbladder cancer patients of African ancestry

Pavan Baichan  1 , Previn Naicker  2 , Tanya Nadine Augustine  3 , Martin Smith  1   4 , Geoffrey Candy  1 , John Devar  1   4 , Ekene Emmanuel Nweke  5
Affiliations

Proteomic analysis identifies dysregulated proteins and associated molecular pathways in a cohort of gallbladder cancer patients of African ancestry

Pavan Baichan et al. Clin Proteomics. .

Abstract

Background: Gallbladder cancer (GBC) is a lethal cancer with a poor prognosis. The lack of specific and sensitive biomarkers results in delayed diagnosis with most patients presenting at late stages of the disease. Furthermore, there is little known about the molecular mechanisms associated with GBC, especially in patients of African ancestry. This study aimed to determine dysregulated proteins in South African GBC patients to identify potential mechanisms of the disease progression and plausible biomarkers.

Methods: Tissues (27 GBC, 13 Gallstone disease, and 5 normal tissues) and blood plasma (54 GBC and 73 Benign biliary pathology) were obtained from consenting patients. Protein extraction was performed on all tissues and liquid chromatography-mass spectrometry was used for proteomic profiling. A project-specific spectral library was built using the Pulsar search algorithm. Principal component and Spearman's rank correlation analyses were performed using PAST (V4.07b). Pathway and Network analyses were conducted using REACTOME (v3.7) and stringAPP (v1.7.0), respectively.

Results: In the tissue sample group, there were 62 and 194 dysregulated proteins in GBC compared to normal and gallstone groups, respectively. In the plasma group, there were 33 altered proteins in GBC compared to the benign biliary pathology group. We found 9 proteins (APOA1, APOA2, RET4, TTR, HEMO, HBB, HBA, PIGR, and APOE) to be commonly dysregulated in both tissue and plasma. Furthermore, a subset analysis demonstrated that 2 proteins, S100A8 and S100A9, were downregulated in GBC patients with GD history compared to those without. Pathway analysis showed that the dysregulated proteins in GBC patients were enriched in pathways involved in smooth muscle contraction, metabolism, ECM organization, and integrin cell surface interactions.

Conclusion: The identified dysregulated proteins help in understanding GBC molecular mechanisms in our patient group. Furthermore, the alteration of specific proteins in both tissue and plasma samples suggests their potential utility as biomarkers of GBC in this sample cohort.

Keywords: Gallbladder cancer; Gallstone disease; Molecular pathways; Proteins; SWATH-MS.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Flow chart of the summary of methods used. SWATH-MS analysis was used to profile biosamples from patients with gallbladder cancer and benign biliary pathologies, such as gallstone disease. Proteins dysregulated in gallbladder cancer patients were identified. Subsequently, pathway and statistical analyses were conducted
Fig. 2
Fig. 2
Venn diagram for common dysregulated proteins among sample group comparisons. There is a total of 62, 194, and 33 identified dysregulated proteins across GBC/Normal tissue, GBC/GD tissue, and GBC/BBP plasma groups, respectively. There are commonly dysregulated proteins as indicated by the overlaps. The Venn diagram was generated using Venny (v2.1.0)
Fig. 3
Fig. 3
Pathway and network analyses of dysregulated proteins. Network and dysregulated pathways of A Gallbladder cancer tissue compared to normal gallbladders. B Gallbladder cancer tissues compared to gallstone tissues. C Gallbladder cancer plasma compared to benign biliary pathologies plasma samples. The colour change is determined by the average Log2 fold change of the protein. D Network analysis shows the intricate relationships between the CDPs. Red and blue indicate downregulated and upregulated proteins in GBC across the groups, respectively
Fig. 4
Fig. 4
Principal component analysis and Spearman’s Rank Correlation Test for "Commonly dysregulated proteins (CDPs)". A The scatterplot indicates PC1 vs PC2 of the CDPs. Black dots indicate GBC tissue patients, red dots indicate GBC plasma patients, blue dots indicate GD patients and grey dots indicate BBP patients. B The loadings for the CDPs for each PC. Green highlighted values are significantly positive loadings and red highlighted values are significantly negative loadings. C The Spearman’s Correlation Rank Test indicates significant positive (blue dots) and negative (red dots) between the CDPs
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