Head and neck squamous cell carcinoma (HNSCC) tumor phenotypes and clinical outcomes are significantly influenced by etiological agents, such as HPV infection, smoking, and alcohol consumption. Accordingly, the intratumor microbiome has been increasingly implicated in cancer progression and metastasis. However, few studies characterize the intratumor microbial landscape of HNSCC with respect to these etiological agents. In this study, we aimed to investigate the bacterial and fungal landscape of HNSCC in association with HPV infection, smoking, and alcohol consumption. RNA-sequencing data were extracted from The Cancer Genome Atlas (TCGA) regarding 449 tissue samples and 44 normal samples. Pathoscope 2.0 was used to extract the microbial reads. Microbe abundance was compared to clinical variables, oncogenic signatures, and immune-associated pathways. Our results demonstrated that a similar number of dysregulated microbes was overabundant in smokers and nonsmokers, while heavy drinkers were characterized by an underabundance of dysregulated microbes. Conversely, the majority of dysregulated microbes were overabundant in HPV+ tumor samples when compared to HPV- tumor samples. Moreover, we observed that many dysregulated microbes were associated with oncogenic and metastatic pathways, suggesting their roles in influencing carcinogenesis. These microbes provide insights regarding potential mechanisms for tumor pathogenesis and progression with respect to the three etiological agents.

Tobacco is the primary etiologic agent in worsened lung squamous cell carcinoma (LUSC) outcomes. Meanwhile, it has been shown that etiologic agents alter enhancer RNAs (eRNAs) expression. Therefore, we aimed to identify the effects of tobacco and electronic cigarette (e-cigarette) use on eRNA expression in relation to LUSC outcomes. We extracted eRNA counts from RNA-sequencing data of tumor/adjacent normal tissue and before/after e-cigarette tissue from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), respectively. Tobacco-mediated LUSC eRNAs were correlated to patient survival, clinical variables, and immune-associated elements. eRNA expression was also correlated to mutation rates through the Repeated Evaluation of Variables Conditional Entropy and Redundance (REVEALER) algorithm and methylated sites through methylationArrayAnalysis. Differential expression analysis was then completed for the e-cigarette data to compare with key tobacco-mediated eRNAs. We identified 684 downregulated eRNAs and 819 upregulated eRNAs associated with tobacco-mediated LUSC, specifically, with the cancer pathological stage. We also observed a decrease in immune cell abundance in tobacco-mediated LUSC. Yet, we found an increased association of eRNA expression with immune cell abundance in tobacco-mediated LUSC. We identified 16 key eRNAs with significant correlations to 8 clinical variables, implicating these eRNAs in LUSC malignancy. Furthermore, we observed that these 16 eRNAs were highly associated with chromosomal alterations and reduced CpG site methylation. Finally, we observed large eRNA expression upregulation with e-cigarette use, which corresponded to the upregulation of the 16 key eRNAs. Our findings provide a novel mechanism by which tobacco and e-cigarette smoke influences eRNA interactions to promote LUSC pathogenesis and provide insight regarding disease progression at a molecular level.

Lung squamous cell carcinoma (LUSC) is a highly heterogeneous cancer that is influenced by etiological agents such as tobacco smoke. Accordingly, transfer RNA-derived fragments (tRFs) are implicated in both cancer onset and development and demonstrate the potential to act as targets for cancer treatments and therapies. Therefore, we aimed to characterize tRF expression with respect to LUSC pathogenesis and clinical outcomes. Specifically, we analyzed the effect of tobacco smoke on tRF expression. In order to do so, we extracted tRF read counts from MINTbase v2.0 for 425 primary tumor samples and 36 adjacent normal samples. We analyzed the data in three primary cohorts: (1) all primary tumor samples (425 samples), (2) smoking-induced LUSC primary tumor samples (134 samples), and (3) non-smoking-induced LUSC primary tumor samples (18 samples). Differential expression analysis was performed to examine tRF expression in each of the three cohorts. tRF expression was correlated to clinical variables and patient survival outcomes. We identified unique tRFs in primary tumor samples, smoking-induced LUSC primary tumor samples, and non-smoking-induced LUSC primary tumor samples. In addition, many of these tRFs demonstrated correlations to worse patient survival outcomes. Notably, tRFs in the smoking-induced LUSC and non-smoking-induced LUSC primary tumor cohorts were significantly correlated to clinical variables pertaining to cancer stage and treatment efficacy. We hope that our results will be used to better inform future LUSC diagnostic and therapeutic modalities.

Transfer-RNA-derived fragments (tRFs) are a novel class of small non-coding RNAs that have been implicated in oncogenesis. tRFs may act as post-transcriptional regulators by recruiting AGO proteins and binding to highly complementary regions of mRNA at seed regions, resulting in the knockdown of the transcript. Therefore, tRFs may be critical to tumorigenesis and warrant investigation as potential biomarkers. Meanwhile, the incidence of papillary thyroid carcinoma (PTC) has increased in recent decades and current diagnostic technology stands to benefit from new detection methods. Although small non-coding RNAs have been studied for their role in oncogenesis, there is currently no standard for their use as PTC biomarkers, and tRFs are especially underexplored. Accordingly, we aim to identify dysregulated tRFs in PTC that may serve as biomarker candidates. We identified dysregulated tRFs and driver genes between PTC primary tumor samples (n = 511) and adjacent normal tissue samples (n = 59). Expression data were obtained from MINTbase v2.0 and The Cancer Genome Atlas. Dysregulated tRFs and genes were analyzed in tandem to find pairs with anticorrelated expression. Significantly anticorrelated tRF-gene pairs were then tested for potential binding affinity using RNA22—if a heteroduplex can form via complementary binding, this would support the hypothesized RNA silencing mechanism. Four tRFs were significantly dysregulated in PTC tissue (p < 0.05), with only AsnGTT 3′-tRF being upregulated. Binding affinity analysis revealed that tRF-30-RY73W0K5KKOV (AsnGTT 3′-tRF) exhibits sufficient complementarity to potentially bind to and regulate transcripts of SLC26A4, SLC5A8, DIO2, and TPO, which were all found to be downregulated in PTC tissue. In the present study, we identified dysregulated tRFs in PTC and found that AsnGTT 3′-tRF is a potential post-transcriptional regulator and biomarker.