Total RNA sequencing reveals gene expression and microbial alterations shared by oral pre-malignant lesions and cancer

Abstract

Head and neck cancers are a complex malignancy comprising multiple anatomical sites, with cancer of the oral cavity ranking among the deadliest and most disfiguring cancers globally. Oral cancer (OC) constitutes a subset of head and neck cancer cases, presenting primarily as tobacco-and alcohol-associated oral squamous cell carcinoma (OSCC), with a 5-year survival rate of ∼65%, partly due to the lack of early detection and effective treatments. OSCC arises from premalignant lesions (PMLs) in the oral cavity through a multi-step series of clinical and histopathological stages, including varying degrees of epithelial dysplasia. To gain insights into the molecular mechanisms associated with the progression of PMLs to OSCC, we profiled the whole transcriptome of 66 human PMLs comprising leukoplakia with dysplasia and hyperkeratosis non-reactive (HkNR) pathologies, alongside healthy controls and OSCC. Our data revealed that PMLs were enriched in gene signatures associated with cellular plasticity, such as partial EMT (p-EMT) phenotypes, and with immune response. Integrated analyses of the host transcriptome and microbiome further highlighted a significant association between differential microbial abundance and PML pathway activity, suggesting a contribution of the oral microbiome towards PML evolution to OSCC. Collectively, this study reveals molecular processes associated with PML progression that may help early diagnosis and disease interception at an early stage.

Author summary: Patients harboring oral premalignant lesions (PMLs) have an increased risk of developing oral squamous cell carcinoma (OSCC), but the underlying mechanisms driving transformation of PMLs to OSCC remain poorly understood. In this study, Khan et al., analyzed a newly generated dataset of gene expression and microbial profiles of oral tissues from patients diagnosed with PMLs from differing histopathological groups, including hyperkeratosis not reactive ( HkNR ) and dysplasia, comparing these profiles with OSCC and normal oral mucosa. Significant similarities between PMLs and OSCC were observed, with PMLs manifesting several cancer hallmarks, including oncogenic and immune pathways. The study also demonstrates associations between the abundance of multiple microbial species and PML groups, suggesting a potential contribution of the oral microbiome to the early stages of OSCC development. The study offers insights into the nature of the molecular, cellular and microbial heterogeneity of oral PMLs and suggests that molecular and clinical refinement of PMLs may provide opportunities for early disease detection and interception.

Figure 1:. PMLs show transcriptional heterogeneity.

Figure 1A: Heatmap of top 2000 highly varying genes, with rows (genes) and columns (patients) sorted by hierarchical clustering. The columns are split into three groups as determined by a 3-way cut of the dendrogram. Row annotation highlights epithelium-related genes in red and immune-related genes in green. Figure 1B (top): UMAP analysis of gene expression profiles from all samples colored by histopathological group. The progression status of 27 patients is denoted with black circles (stable), blue squares (progressed to dysplasia), and red squares (progressed to SCC). Figure 1B (bottom): Clustering analysis using K-means, with k=3. Within each cluster, the group positively enriched by chi-square test is highlighted. Figure 1C: Boxplots of GSVA-based enrichment scores. Displayed from left to right are: score differences of up- and down-regulated OPMD signatures (n=37 genes; up=9, down=27); score differences of up- and down-regulated TCGA HNSCC signatures (n=2647; up=1317, down=1330); and scores from a unidirectional p-EMT signature (n=100).

Figure 2:. Pathway enrichment of the PML and OSCC signatures.

Figure 2A: Over-representation-based enrichment results based on the Hallmarks compendium of differentially expressed genes from pairwise analysis with control, OSCC, and within PMLs. The size of the dot is proportional to the number of overlapping genes and the color coding represents the FDR-corrected q-val. Figure 2B: Over-representation-based enrichment results based on the Reactome compendium. The hierarchical organization of related pathways is shown on the right.

Figure 3:. Relative abundance of immune cell-types stratified by histopathology.

Immune cell-type deconvolution scores obtained from CIBERSORT are stratified by histopathological groups. (Top) Cell type proportions of the innate (left) and adaptive (right) compartments. (Bottom) Specific cell sub-type proportions within the two compartments.

Figure 4:. Differential microbial abundance and their association with PML and OSCC.

Figure 4A: Log-CPM normalized relative proportions of microbial profiles of Phylum, Genus and Species level stratified by histopathological grouping. Figure 4B: Associations of differentially abundant microbes with human genes and host-response pathways are outlined. The node size denotes the total number of associations for a microbe or a gene, and the edges’ thickness is proportional to the enrichment score between a microbe and a gene. The node color indicates whether the gene was up- (red) or down-regulated (blue) in the corresponding host transcriptome differential analysis. The gene-pathway edges denote enrichment by over-representation analysis.