Cell Adhesion Molecule 1 (CADM1) Is an Independent Prognostic Factor in Patients with Cutaneous Squamous Cell Carcinoma

Saito‐Sasaki, Natsuko; Sawada, Yu; Okada, Etsuko; Nakamura, Motonobu

doi:10.3390/diagnostics11050830

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…"Cell-cell adhesion" ranks fifth among all enriched pathways and processes. Cell Adhesion Molecule 1 (CADM1), which participates in this process, is an independent prognostic factor for patients with cSCC 55 .…”

Section: Pathway and Process Enrichment Analysis Based On Best-predic...mentioning

confidence: 99%

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Gao

Yuan

et al. 2023

Preprint

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Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.

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Section: Pathway and Process Enrichment Analysis Based On Best-predic...mentioning

confidence: 99%

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Gao

Yuan

et al. 2023

Preprint

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“…Reduced cell adhesion is often a biomarker of invasiveness and an indication of a poor prognosis in many cancers. , The invasive processes are also driven by αβ; however, the actions of Tgf-β on cancer cells also appear to require signals from the EGFR-family . Tgf-β enhances invasion by activating the expression integrins (e.g.ITGA2), extracellular matrix proteins (e.g., LAMB3), and wnt proteins (e.g., WNT7A/B); the increased expression of these proteins requires the activation of transcription regulators such as AP-1 and p63, and signaling from the EGFR-family enhances the activation of AP-1 and p63 .…”

Section: Egfr Signalingmentioning

confidence: 99%

Regulation of Signaling from the Epidermal Growth Factor Family

Burgess

2022

J. Phys. Chem. B

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The epidermal growth factor (EGF) system has allowed chemists, biologists, and clinicians to improve our understanding of cell production and cancer therapy. The discovery of EGF led to the recognition of cell surface receptors capable of controlling the proliferation and survival of cells. The detailed structures of the EGF-like ligand and the responses of their receptors (EGFR-family) has revealed the conformational and aggregation changes whereby ligands activate the intracellular kinase domains. Biophysical analysis has revealed the preformed clustering of different EGFR-family members and the processes which occur on ligand binding. Understanding these receptor activation processes and the consequential cytoplasmic signaling has allowed the development of inhibitors which are revolutionizing cancer therapy. This Review describes the recent progress in our understanding of the activation of the EGFR-family, the effects of signaling from the EGFR-family on cell proliferation, and the targeting of the EGFR-family in cancer treatment.

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“…Cutaneous squamous cell carcinoma is a skin cancer derived from epithelial keratinocytes, and its advanced form has an unfavorable clinical course because of the limited number of therapeutic options in the current treatment [ 56 , 57 , 58 ]. In addition, immune checkpoint inhibitor treatment is not widely used for treating metastatic cutaneous squamous cell carcinoma.…”

Section: Sting-involved Anti-tumor Immunitymentioning

confidence: 99%

STING Signaling and Skin Cancers

Sato

Sawada

Nakamura

2021

Cancers

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Recent developments in immunotherapy against malignancies overcome the disadvantages of traditional systemic treatments; however, this immune checkpoint treatment is not perfect and cannot obtain a satisfactory clinical outcome in all cases. Therefore, an additional therapeutic option for malignancy is needed in oncology. Stimulator of interferon genes (STING) has recently been highlighted as a strong type I interferon driver and shows anti-tumor immunity against various malignancies. STING-targeted anti-tumor immunotherapy is expected to enhance the anti-tumor effects and clinical outcomes of immunotherapy against malignancies. In this review, we focus on recent advancements in the knowledge gained from research on STING signaling in skin cancers. In addition to the limitations of STING-targeted immunotherapy, we also discuss the clinical application of STING agonists in the treatment of skin cancer.

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Cell Adhesion Molecule 1 (CADM1) Is an Independent Prognostic Factor in Patients with Cutaneous Squamous Cell Carcinoma

Cited by 8 publications

References 40 publications

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Regulation of Signaling from the Epidermal Growth Factor Family

STING Signaling and Skin Cancers

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