A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma

Multimodal AI for prediction of distant metastasis in carcinoma patients

Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.

The Concordant Disruption of B7/CD28 Immune Regulators Predicts the Prognosis of Oral Carcinomas

Immune modulation is a critical factor in determining the survival of patients with malignancies, including those with oral squamous cell carcinoma (OSCC) and head and neck SCC (HNSCC). Immune escape or stimulation may be driven by the B7/CD28 family and other checkpoint molecules, forming ligand-receptor complexes with immune cells in the tumor microenvironment. Since the members of B7/CD28 can functionally compensate for or counteract each other, the concomitant disruption of multiple members of B7/CD28 in OSCC or HNSCC pathogenesis remains elusive. Transcriptome analysis was performed on 54 OSCC tumors and 28 paired normal oral tissue samples. Upregulation of CD80, CD86, PD-L1, PD-L2, CD276, VTCN1, and CTLA4 and downregulation of L-ICOS in OSCC relative to the control were noted. Concordance in the expression of CD80, CD86, PD-L1, PD-L2, and L-ICOS with CD28 members was observed across tumors. Lower ICOS expression indicated a worse prognosis in late-stage tumors. Moreover, tumors harboring higher PD-L1/ICOS, PD-L2/ICOS, or CD276/ICOS expression ratios had a worse prognosis. The survival of node-positive patients was further worsened in tumors exhibiting higher ratios between PD-L1, PD-L2, or CD276 and ICOS. Alterations in T cell, macrophage, myeloid dendritic cell, and mast cell populations in tumors relative to controls were found. Decreased memory B cells, CD8⁺ T cells, and Tregs, together with increased resting NK cells and M0 macrophages, occurred in tumors with a worse prognosis. This study confirmed frequent upregulation and eminent co-disruption of B7/CD28 members in OSCC tumors. The ratio between PD-L2 and ICOS is a promising survival predictor in node-positive HNSCC patients.

LncRNA DNAJC3-AS1 promotes the biological functions of papillary thyroid carcinoma via regulating the microRNA-27a-3p/CCBE1 axis

Long noncoding RNA DNAJC3-AS1 (lncRNA DNAJC3-AS1) has been probed in many studies, while the regulatory mechanism of DNAJC3-AS1 on papillary thyroid carcinoma (PTC) via regulating microRNA (miR)-27a-3p remains inadequate. This research aims to depict the role of DNAJC3-AS1, miR-27a-3p, collagen, and calcium-binding EGF domain-containing protein 1 (CCBE1) on PTC development. DNAJC3-AS1, miR-27a-3p, and CCBE1 expression levels in PTC tissues and adjacent normal tissues were tested. The relation of DNAJC3-AS1, miR-27a-3p, and CCBE1 was analyzed. DNAJC3-AS1 and miR-27a-3p and CCBE1-related oligonucleotides were transfected into IHH-4 cells to investigate their role in PTC development. Cell tumorigenicity was detected by in vivo assay. DNAJC3-AS1 and CCBE1 expressed highly and miR-27a-3p expressed lowly in PTC. Downregulation of DNAJC3-AS1, upregulating miR-27a-3p or downregulating CCBE1 impaired the malignant behaviors of IHH-4 cells. Depletion of miR-27a-3p reversed the DNAJC3-AS1 suppression-induced phenotypic inhibition of IHH-4 cells. DNAJC3-AS1 bound to miR-27a-3p and CCBE1 as a target of miR-27a-3p. Our study highlights that DNAJC3-AS1 inhibits miR-27a-3p to promote CCBE1 expression, thereby facilitating PTC development. This study affords distinguished therapeutic strategies and novel research directions for PTC treatment.

Development and validation of a nomogram for the prediction of lymph node metastasis within 2-year postoperatively in cT1-T2N0 oral squamous cell carcinoma

BACKGROUND

The current neck management for early oral squamous cell carcinoma (OSCC) has always been a controversial issue. A comprehensive model is necessary for predicting an individual's metastasis risk and appropriate patient counseling.

METHODS

A nomogram for predicting 2-year LNM in patients with cT1-2N0 OSCC was developed and validated using clinicopathological data from 642 patients from 2000 to 2018 in four hospitals, China.

RESULTS

Three variables (pathology grade, depth of invasion, tumor-infiltrating lymphocytes) were included in nomogram. C-indices were 0.826 (95% CI: 0.786-0.866) and 0.726 (95% CI: 0.653-0.780) in the internal and external validation. Kaplan-Meier method found the 2-year LNM rate of high-risk group (35.8%) was much higher than that of the low-risk group (14.5%). The nomogram model has an advantage over the 8th AJCC TNM stage in predicting the individual 2-year LNM probability for early OSCC.

CONCLUSION

Patients with low-risk nomogram score may receive neck observation; those with high-risk score should receive END.

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19

Background

The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.

Methods

Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.

Results

The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.

Conclusions

Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

The neuropeptide calcitonin gene-related peptide links perineural invasion with lymph node metastasis in oral squamous cell carcinoma

Objective

Although perineural invasion (PNI) is well-known to be correlated with and able to predict lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC), the clinical and molecular correlation between PNI and LNM has not been elucidated, and preoperative biomarkers for LNM prediction in OSCC are urgently needed.

Materials and methods

The correlation between PNI and LNM was retrospectively evaluated using a cohort of 218 patients diagnosed with OSCC. Candidate neuropeptides were screened based on TCGA database and verified via immunohistochemistry and Western blot analyses. ELISA was used to detect calcitonin gene-related peptide (CGRP) in patient plasma. In vitro assays were used to explore the effects of CGRP on OSCC cells.

Results

OSCC patients with PNI had a higher incidence of LNM (69.86% vs. 26.2%, P < 0.0001, n = 218). CGRP expression was upregulated in the PNI niche and in metastatic lymph nodes, and was correlated with poor overall survival of OSCC patients. Preoperative plasma CGRP levels were higher in OSCC patients (n = 70) compared to healthy donors (n = 60) (48.59 vs. 14.58 pg/ml, P < 0.0001), and were correlated with LNM (P < 0.0001) and PNI (P = 0.0002). Preoperative plasma CGRP levels alone yielded an AUC value of 0.8088 to predict LNM, and CGRP levels combined with preoperative T stage reached an AUC value of 0.8590. CGRP promoted proliferation and migration abilities of OSCC cells, which could be antagonized by either pharmacological or genetic blockade of the CGRP receptor.

Conclusions

The neuropeptide CGRP links PNI and LNM in OSCC, and preoperative plasma CGRP levels can be used to predict LNM in OSCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08998-9.

A Combined Prediction Model for Lymph Node Metastasis Based on a Molecular Panel and Clinicopathological Factors in Oral Squamous Cell Carcinoma

Objective

Lymph node metastasis is the most important factor influencing the prognosis of oral squamous cell carcinoma (OSCC) patients. However, there is no proper method for predicting lymph node metastasis. This study aimed to construct and validate a preoperative prediction model for lymph node metastasis and guide personalized neck management based on the gene expression profile and clinicopathological parameters of OSCC.

Methods

Based on a previous study of related genes in OSCC, the mRNA expression of candidate genes was evaluated by real-time PCR in OSCC specimens. In this retrospective study, the gene expression profile and clinicopathological parameters of 112 OSCC patients were combined to construct the best prediction model for lymph node metastasis of OSCC. The model was validated with 95 OSCC samples in this study. Logistic regression analysis was used. The area under the curve (AUC) ultimately determined the diagnostic value of the prediction model.

Results

The two genes CDKN2A + PLAU were closely related to lymph node metastasis of oral squamous cell carcinoma. The model with the combination of CDKN2A, PLAU, T stage and pathological grade was the best in predicting lymph node metastasis (AUC = 0.807, 95% CI: 0.713-0.881, P=0.0001). The prediction model had a specificity of 96% and sensitivity of 72.73% for stage T1 and T2 OSCC (AUC = 0.855, 95% CI: 0.697-0.949, P=0.0001).

Conclusions

High expression of CDKN2A and PLAU was associated with lymph node metastasis in OSCC. The prediction model including CDKN2A, PLAU, T stage and pathological grade can be used as the best diagnostic model for lymph node metastasis in OSCC.