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Identification of HOX signatures contributing to oral cancer phenotype.

ABSTRACT: The role of evolutionarily conserved homeobox-containing HOX genes as transcriptional regulators in the developmental specification of organisms is well known. The contribution of HOX genes involvement in oral cancer phenotype has yet to be fully ascertained. TCGA-HNSC HTSeq-counts and clinical data were retrieved from the GDC portal for oral cavity neoplasms. GEO datasets (GSE72627, GSE30784, GSE37991) were accessed and analyzed using GEO2R. Differential HOX gene expression was profiled using the DESeq2 R package with a log2 fold change cut-off (- 1 and + 1) and Benjamini-Hochberg p-adjusted value at ≤ 0.01. Gene set over-representation analysis and semantic analysis associated with the disease ontology was performed using the ClusterProfiler R package, and pathway over-representation analysis was performed using IMPaLa. HOX protein interaction network was constructed using the Pathfind R package. HOX phenotype associations were performed using Mammalian Phenotype Ontology, Human Phenotype Ontology, PhenGenI associations, Jensen tissues, and OMIM entries. Drug connectivity mapping was carried out with Dr. Insight R package. HOXA2 was upregulated in oral dysplasia but silenced during tumor progression. Loss of HOXB2 expression was consistent in the potentially malignant oral lesions as well as in the primary tumor. HOXA7, HOXA10, HOXB7, HOXC6, HOXC10, HOXD10, and HOXD11 were consistently upregulated from premalignancy to malignancy and were notably associated with risk factors. Overrepresentation analysis suggested HOXA10 was involved in the transcriptional misregulation contributing to the oral cancer phenotype. HOX genes subnetwork analysis showed crucial interactions with cell cycle regulators, growth responsive elements, and proto-oncogenes. Phenotype associations specific to the oral region involving HOX genes provide intrinsic cues to tumor development. The 5' HOX genes were aberrantly upregulated during oral carcinogenesis reflecting their posterior prevalence.

SUBMITTER: Padam KSR

PROVIDER: S-EPMC9203786 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Identification of HOX signatures contributing to oral cancer phenotype.

Padam Kanaka Sai Ram KSR Morgan Richard R Hunter Keith K Chakrabarty Sanjiban S Kumar Naveena A N NAN Radhakrishnan Raghu R

Scientific reports 20220616 1

The role of evolutionarily conserved homeobox-containing HOX genes as transcriptional regulators in the developmental specification of organisms is well known. The contribution of HOX genes involvement in oral cancer phenotype has yet to be fully ascertained. TCGA-HNSC HTSeq-counts and clinical data were retrieved from the GDC portal for oral cavity neoplasms. GEO datasets (GSE72627, GSE30784, GSE37991) were accessed and analyzed using GEO2R. Differential HOX gene expression was profiled using t ...[more]

PMID: 35710803

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Project description:BackgroundEarly-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.Methods and findingsTumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.ConclusionsOur findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.

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Identification of HOX signatures contributing to oral cancer phenotype.

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Identification of HOX signatures contributing to oral cancer phenotype.

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