Exploring heterogeneous expression of beta-actin (ACTB) in bladder cancer by producing a monoclonal antibody 6D6

Abstract

Background: To predict outcomes and identify potential therapeutic targets for cancers, it is critical to find novel specific biomarkers. The objective of this study was to search for and explore novel bladder cancer-associated protein biomarkers.

Methods: A library of monoclonal antibodies (mAbs) against the JAM-ICR cell line was first generated, and clones with high affinity were selected. Hybridomas were screened using bladder cancer (BLCA) cell lines and normal cells. The target of the selected mAb was then characterized through immunoaffinity purification, western blotting, and mass spectrometry analysis. Expression of the target antigen was assessed by flow cytometry and IHC methods. Several databases were also used to evaluate the target antigen in BLCA and other types of cancers.

Results: Based on screenings, a 6D6 clone was selected that recognized an isoform of beta-actin (ACTB). Our data showed that ACTB expression on different cell lines was heterogeneous and varied significantly from low to high intensity. 6D6 bound strongly to epithelial cells while showing weak to no reactivity to stromal, endothelial, and smooth muscle cells. There was no association between ACTB intensity and related prognostic factors in BLCA. In silico evaluations revealed a significant correlation between ACTB and overexpressed genes and biomarkers in BLCA. Additionally, the differential expression of ACTB in tumor and healthy tissue as well as its correlation with survival time in a number of cancers were shown.

Conclusions: The heterogeneous expression of ACTB may suggest the potential value of this marker in the diagnosis or prognosis of cancer.

Keywords: ACTB; Beta-actin; Biomarkers; Bladder cancer; Hybridoma; Monoclonal antibody.

Fig. 1

Workflow diagram. WB: western blot, IHC: Immunohistochemistry

Fig. 2

Reactivity of 6D6 mAb with JAM-ICR, MSCs, granulocytes, monocytes, and lymphocytes with flow cytometry. The main cells were selected based on their size and granularity (FSC/SSC), and then positive cells (positive FITC) were gated. Negative control cells were treated with only the anti-mouse Ig secondary antibody. MSC: Adipose-derived mesenchymal stem cells

Fig. 3

Characterization of the purified mAb and the target antigen. SDS‒PAGE electrophoresis of the purified monoclonal antibody 6D6 under reducing conditions showed two bands at 50 and 25 kDa (A). After purification with the immunoprecipitation strategy, the target antigen of 6D6 was run on SDS‒PAGE and then transferred to a PVDF membrane for western blotting (B). Full-length of the gel (Figure S1 with marker) and the original blot (Figure S2 with marker) are presented in supplementary files. Additionally, Fig. 3B with different contrast (using the Image Lab software setting) was also added to the supplementary file as Figure S3

Fig. 4

Evaluation of 6D6 reactivity with different cell lines by flow cytometry. The expression of ACTB on different cell lines was heterogeneous and varied from low (MCF-7, SW1116, Jurkat, Raji, and Heck293) to moderate (HT-29, AGS) and high (5637, MRC5, MDA-MB-231, U87, and EJ). Each test was performed three times separately, and the frequencies of each cells were compared with JAM-ICR. * = P-value < 0.05

Fig. 5

Cellular distribution of ACTB expression in bladder tissue. JAM-ICR cells were stained as a positive control (A), and a sample without 6D6 was considered a negative control (B). 6D6 showed no or low reactivity with stromal cells (C) and smooth muscle cells (D). Both cytoplasmic (E) and membranous (F) patterns of ACTB were observed. ACTB expression was compared between tumor tissue (red arrow) and normal adjacent tissue (green arrow) (G-H) and high stage (I) and low stage (J) BLCA

Fig. 6

The in silico (A, C, E) and in vitro (B, D, F) evaluation of ACTB expression in bladder cancer. ACTB expression was not significantly different between tumor tissue and adjacent normal tissue (A, B). However, an in silico study showed a significant increase in ACTB expression in stage IV (C), and our IHC results did not show a significant difference (D). The survival time in patients with high ACTB expression was not different from that in patients with low ACTB expression (E, F). In silico data were obtained from the GEPIA2 database. Although the GEPIA2 database is mainly concerned with the analysis of mRNA expression levels in different tissues, the employed mAb-based approach enables the detection of differential biomarkers at the protein level

Fig. 7

Differential expression of ACTB in various cancer tissues. There was a significant difference between the expression of ACTB in healthy and tumor tissue in 9 cancers (among 31 cancers), and the maximum difference was seen in pancreatic adenocarcinoma (PAAD; fold change = 12.9), testicular germ cell tumors (TGCT; fold change = 4.6), and glioblastoma multiform (GBM; fold change = 4.1). In silico data were obtained from the GEPIA2 database

Fig. 8

The overall survival time of patients with low or high expression. The expression of ACTB was associated with survival time in various cancers, such as GBM, head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), LGG (brain lower grade glioma), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), mesothelioma (MESO), SKCM (skin cutaneous melanoma), and uveal melanoma (UVM)

Fig. 9

Interaction of ACTB with other genes (A) and proteins (B). The interaction network of ACTB with other proteins and genes was obtained from STRING and UCSC servers, respectively. Additionally, ACTB mutations in patients with BLCA (C) and the tissue distribution of the prevalent mutation (p.G158R) are shown (D)