Transcriptome Analysis in Patients With Muscle-invasive Bladder Cancer

Abstract

Background/Aim

Bladder cancer (BC) is the most prevalent malignant tumor in the urinary tract, classified mainly into muscle-invasive BC (MIBC) and non-MIBC (NMIBC). Recent studies highlight the important role of changes in transcriptome activity in carcinogenesis, aiding in the identification of additional differentially regulated candidate genes, improving our understanding of the molecular basis of gene regulation in BC. This study aimed to evaluate the transcriptome of MIBC patients compared with normal subjects.

Materials and Methods

mRNA sequencing was conducted using the Illumina NovaSeq 6000 Dx system in a case series comprising 11 subjects with MIBC and 19 healthy controls matched for age and sex. For functional analysis, the pathfindR package was utilized to comprehensively identify pathways enriched in omics data within active subnetworks.

Results

Our results demonstrated the presence of differentiated pathways, including spliceosome activity, oxidative phosphorylation, and chemical carcinogenesis due to reactive oxygen species, in MIBC patients compared with controls.

Conclusion

The identification of novel molecular pathways in MIBC patients could prove useful in defining cancer predisposition factors and exploring potential therapeutic options.

Urothelial carcinoma of the bladder (BC) appears to be the most frequent malignant tumor affecting the urinary tract and is classified into two main categories: Non-muscle-invasive BC (NMIBC) and muscle-invasive BC (MIBC). NMIBCs account for 75% of all BC cases and typically exhibit better treatment response, as well as lower mortality rates compared to MIBCs (1). In fact, most MIBCs have a poor prognosis due to rapid metastasis. Although chemo-radiotherapy is an alternative, half of patients with MIBC experience recurrence and metastasis within two years after diagnosis. Therefore, radical cystectomy (RC) should be considered the gold standard therapy for MIBC. Hence, it is crucial to understand the biological processes underlying cancer progression and to discover new prognostic biomarkers for MIBC (1).

In recent years, there has been a significant focus on understanding the fundamental mechanisms behind the development of MIBC, as well as identifying molecular biomarkers and potential therapeutic targets. In fact, both genetic alterations (at the level of DNA and subsequent RNA expression) and epigenetic modifications (for example, DNA methylation) underlie the different phenotypes of BC, leading to diagnostic, prognostic, predictive, and therapeutic implications (2-4). Indeed, BC is a multifaceted disease with various molecular mechanisms involved in its development and progression. To explore the different genetic alterations involved in this pathological process, researchers have utilized the Cancer Genome Atlas (TCGA) project, which has been pivotal in advancing our understanding of BC by shedding light on its molecular background. It has allowed the creation of a rich genomic, epigenomic, transcriptomic, and proteomic analysis dataset regarding BC. In particular, it has revealed 64 significantly mutated genes associated with this disease. Altered signaling and protein expression pathways identified include: p53/cell cycle, DNA repair, PI3K/AKT, and chromatin modifications. Nonetheless, further studies have shown that specific genomic alterations, including point DNA mutations rearrangements (5) and copy number variations (6), play a critically important function in the development of BC. Similarly, transcriptome modifications may also lead to abnormal cellular behavior, thus directly contributing to carcinogenesis (7). The transcriptome encompasses all RNA transcripts generated by the genome in a specific cell or tissue at a particular time and offers insights into gene expression levels, alternative splicing, and post-transcriptional modifications. Thus, it reflects gene activity within a cell. Therefore, the study of the MIBC-related transcriptome may help in the identification of additional related candidate genes and improve the understanding of gene regulation in MIBC.

This study aimed to evaluate the transcriptome of MIBC patients compared with normal subjects. Then, based on the dysregulated genes, we performed gene enrichment and pathway analysis to explore the potential mechanisms of these dysregulated genes in MIBC patients compared to controls.

Materials and Methods

This study involves an mRNA analysis, following the methodology described in Salemi et al. (8), performed in 30 participants of Sicilian ancestry. The cohort comprised 11 patients with MIBC (11 males; mean age 63.00±9.88 years) and 19 healthy controls matched for age and sex (19 males; mean age 58.84±20.36 years). All MIBCs were classified as high-grade urothelial carcinomas. Informed consent was obtained from all participants in the study and the principles of the 1964 Declaration of Helsinki were followed.

RNA extraction and RNA sequencing and data analysis. RNA extraction, sequencing, and data analysis were performed according to Salemi et al. (2022) (8). Low-quality reads (≤25 bp) and adaptor sequences were trimmed with cutadapt (9) (v.3.4) and the fastq files were mapped on a reference genome using the bioinformatics tool STAR (version 2.7.5c) (10). The reference genome used was the Human assembly obtained from GenCode (HG38-Release 37 (GRCh38.p13). The quantification of regulated genes for each sequenced sample was computed using feature Counts algorithm (v.2.0), considering all genes expressed in at least 25% of samples, as determined by the Bioconductor DESeq2 package (11). Transcripts showing a fold-change ≥2 or ≤−2 (|fold-change| ≥2), with adjusted p-values ≤0.001 (p_adj), were considered as differentially regulated.

Functional and pathways analysis of differentially regulated genes. The R package pathfindR was used to perform the functional analysis. Specifically, pathfindR analysis was based on the KEGG pathway database and a set of selected genes with p_adj ≤0.001 and ≥2. Only enriched terms with adjusted p-value ≤0.05 were used for the analysis. In addition, the score_terms function of pathfindR was used to calculate aggregate term scores per sample based on gene expression patterns (12,13). The raw data (.fastq files) of the identified mRNAs are available from ArrayExpress under accession number E-MTAB-12828.

Results

The main analysis revealed good clustering and showed complete separation between the two groups of samples. The different expression levels for each gene were calculated using feature counts with common gene annotations (GenCode_Version37) encompassing the entire transcript annotation. Based on this analysis, we identified 20,852 differentially expressed genes.

The heat map showed statistically significant differences between patients and controls (Figure 1A). Among the differentially expressed genes, 3,942 were significantly (p_adj ≤0.001 and |fold-change| ≥2) up-regulated and 3,989 (p_adj ≤0.001 and |fold-change| ≤2) significantly down-regulated in patients compared with controls. As shown in the volcano plots, the differentially regulated genes in patients differed significantly from those found in controls (Figure 1B).

Figure 1. mRNA profiling. (A) Heatmap of the supervised hierarchical clustering analysis on the expression profiles of the differential mRNAs (p_adj ≤0.001 and |fold-change| ≥2) in patients with bladder cancer (BC) compared to healthy controls. Expression values lower or higher than the median are shown in blue or yellow, respectively. (B) Volcano plot of deregulated mRNAs (p_adj ≤0.001 and |fold-change| ≥2). The gray arrows indicate points-of-interest that display both large log2 fold-changes (x axis) and high statistical significance (−log10 of p_adj, y axis). The green, red, grey, and blue nodes represent down-regulated, up-regulated, not significant, and filtered only p_adj genes, respectively. BC indicates muscle-invasive BC.

The results of our analysis indicate that the majority of the dysregulated genes are associated with hemoglobin formation in MIBC; in detail, the hemoglobin alpha locus 1 (HBA1) gene, with a fold-change of 7,759,428, and the hemoglobin alpha locus 2 (HBA2) gene, with a fold-change of 3,103,175, showed significant up-regulation. Another gene that was significantly over-expressed in the MIBC case series was the SNCA gene (Fold Change 50.561; adjusted p-value <0.001).

Statistically significant signaling pathways were identified through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, applying the adjusted p-value threshold of 0.05. In particular, spliceosome and oxidative phosphorylation were among the most enriched categories (Figure 2).

Figure 2. Bubble plot indicating top enrichment KEGG pathways. p-Values are represented by different colors, and the size of bubbles indicates the gene count of each pathway. The heatmap adjacent to it shows average aggregated term scores per sample for patients with muscle-invasive bladder cancer (MIBC) and healthy controls (CTRL). BC denotes MIBC.

Discussion

Our findings reveal the over-expression of the HBA1 and HBA2 genes in individuals with MIBC. A compensatory mechanism could be postulated, triggered in response to hematuria, potentially activating the hematopoietic system. Indeed, these genes contribute to the structure of the polypeptide chains in adult tetrameric hemoglobin (Gene/Locus MIM number 141800 HBA1; Gene/Locus MIM number 141800 HBA2).

Furthermore, our results highlight the over-expression of the SNCA gene in the MIBC case series. SNCA encodes a protein that is highly regulated in the brain and moderately regulated in the bone marrow. SNCA is involved in several functions, including the induction of apoptosis, elevation of oxidative stress, regulation of mitochondrial homeostasis and calcium, although it has also been implicated in cellular dysfunction. Recently, Wu et al. (14) showed a down-regulation of the SNCA mRNA in tissues from MIBC subjects compared to controls. Additionally, the same authors performed further analyses showing that low expression correlated with favorable overall survival and that tumor-infiltrating immune cells were regulators of tumor growth and progression. Furthermore, a possible biological role of gene expression in disease was evaluated by observing its association with cell migration, which is a fundamental feature of metastasis (14).

Of note, we observed a variation in mRNA expression of certain genes, as illustrated in Figure 2, which are also implicated in spliceosome activity. The spliceosome catalyzes the splicing of nuclear pre-mRNA and is a multi-megadalton ribonucleoprotein (RNP) complex (15). The role of alternative splicing (AS) in tumorigenesis was studied, and in terms of survival, most of the AS events were also found to be favorable factors in patients with MIBC (16-18). In this context, with reference to BC, the interaction between oxidative phosphorylation was demonstrated in disease progression (19). Researchers have revealed the importance of oxidative phosphorylation in the maintenance and growth of several BC cell lines. Specifically, cell lines associated with a high risk showed a non-oxidative state, while those at low risk of progression showed an activated oxidative metabolic state (20). Even more recently, considering the involvement of macrophages in the occurrence and progression of BC, targeted macrophage control has been proposed as a treatment method for BC immunotherapy, based on macrophage phagocytosis-mediated oxidative phosphorylation (MPOP), has been proposed. The results showed MPOP-feature genes and developed a predictive nomogram capable of accurately predicting BC overall survival. Similarly, using a BC model of non-genetic plasticity (21).

Although our casuistry is not particularly extensive, our preliminary data have identified new molecular pathways in patients with MIBC that could prove useful in defining cancer predisposition and enabling early detection of individuals at higher risk of developing BC. Such pathways could represent therapeutic targets to attenuate disease progression and potentially mitigate therapeutic contraindications for BC patients (1,22-24).

Conflicts of Interest

The Authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Authors’ Contributions

The Authors contributed equally to all aspects of this study. Conceptualization: P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P.; Methodology, P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P.; Software: P.P., M.S., G.M., M.G.S., L.P; Validation: P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P.; Formal analysis: P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P.; Investigation: P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P; Resources: P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P; Data curation: P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P; Writing – Original draft preparation: P.P., M.S., M.G.S., G.L., S.M., F.S., A.T., L.P; Writing – Review & editing: P.P., M.S., G.M., M.G.S., G.L., S.M., F.S., A.T., L.P; Visualization: P.P., M.S., G.M., M.G.S., G.L., F.S., A.T., L.P; Supervision: P.P., M.S; Project administration: P.P., M.S., G.M., M.G.S., S.M., F.S., A.T., L.P.