Up‐regulation of plasma lncRNA CACS15 distinguished early‐stage oral squamous cell carcinoma patient

Abstract Background Oral squamous cell carcinoma (OSCC) is a capricious cancer with poor survival rates, even for early-stage patients. There is a pressing need to develop more precise risk assessment methods to appropriately tailor clinical treatment. Genome-wide association studies have not produced a viable biomarker. However, these studies are limited by using heterogeneous cohorts, not focusing on methylation although OSCC is a heavily epigenetically-regulated cancer, and not combining molecular data with clinicopathologic data for risk prediction. In this study we focused on early-stage (I/II) OSCC and created a risk score called the REASON score, which combines clinicopathologic characteristics with a 12-gene methylation signature, to predict the risk of 5-year mortality. Methods We combined data from an internal cohort (n = 515) and The Cancer Genome Atlas (TCGA) cohort (n = 58). We collected clinicopathologic data from both cohorts to derive the non-molecular portion of the REASON score. We then analyzed the TCGA cohort DNA methylation data to derive the molecular portion of the risk score. Results 5-year disease specific survival was 63% for the internal cohort and 86% for the TCGA cohort. The clinicopathologic features with the highest predictive ability among the two the cohorts were age, race, sex, tobacco use, alcohol use, histologic grade, stage, perineural invasion (PNI), lymphovascular invasion (LVI), and margin status. This panel of 10 non-molecular features predicted 5-year mortality risk with a concordance (c)-index = 0.67. Our molecular panel consisted of a 12-gene methylation signature (i.e., HORMAD2, MYLK, GPR133, SOX8, TRPA1, ABCA2, HGFAC, MCPH1, WDR86, CACNA1H, RNF216, CCNJL), which had the most significant differential methylation between patients who survived vs. died by 5 years. All 12 genes have already been linked to survival in other cancers. Of the genes, only SOX8 was previously associated with OSCC; our study was the first to link the remaining 11 genes to OSCC survival. The combined molecular and non-molecular panel formed the REASON score, which predicted risk of death with a c-index = 0.915. Conclusions The REASON score is a promising biomarker to predict risk of mortality in early-stage OSCC patients. Validation of the REASON score in a larger independent cohort is warranted.

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Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Frontiers in Oral Health ◽

10.3389/froh.2021.794248 ◽

2022 ◽

Vol 2 ◽

Author(s):

Rasheed Omobolaji Alabi ◽

Alhadi Almangush ◽

Mohammed Elmusrati ◽

Antti A. Mäkitie

Keyword(s):

Machine Learning ◽

Squamous Cell Carcinoma ◽

Deep Learning ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Precision Medicine ◽

Treatment Planning ◽

Squamous Cell ◽

Early Stage ◽

Learning Technology

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

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Robust expression of LINE-1 retrotransposon encoded proteins in oral squamous cell carcinoma

10.1101/2020.11.24.395285 ◽

2020 ◽

Author(s):

Koel Mukherjee ◽

Debpali Sur ◽

Abhijeet Singh ◽

Sandhya Rai ◽

Neeladrisingha Das ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Oral Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Early Stage ◽

Antibody Detection ◽

Normal Brain ◽

Unknown Mechanism ◽

Stage Of Development ◽

Amino Acid Stretch

AbstractRetrotransposons are sequences which transpose within genomes using RNA as an intermediate. Long INterpersed Element-1 (LINE1 or L1) is the only active retrotransposon occupying around 17% of the human genome with an estimated 500,000 copies. An active L1 encodes two proteins (L1ORF1p and L1ORF2p); both of which are critical in the process of retrotransposition. In-order to propagate to the nextgeneration, L1s remain active in germ tissues and at an early stage of development. Surprisingly, by some unknown mechanism, L1 also shows activity in certain parts of the normal brain and many cancers. L1 activity is generally determined by assaying L1ORF1p because of its high expression and availability of the antibody. However, due to its lowerexpression and the unavailability of a robust antibody, detection of L1ORF2p has been limited. L1ORF2p is the crucial protein in the process of retrotransposition as it provides endonuclease and reverse transcriptase (RT) activity. Here, we report a novel human L1ORF2p antibody generated using an 80-amino-acid stretch from the RT domain, which is highly conserved among different species. The antibody detects significant L1ORF2p expression in murine germ tissues and human oral squamous cell carcinoma (OSCC) samples. This particular cancer is prevalent in India due to excessive use of tobacco. Here, using our in-house antibodies against L1 proteins, we show that more than fifty percent of samples are positive for L1 proteins. Overall, we reported a novel L1ORF2p antibody that detects L1 activity in germ tissues and OSCC

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