COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation

Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831–3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis

MYCBP2 expression correlated with inflammatory cell infiltration and prognosis immunotherapy in thyroid cancer patients

IntroductionImmune checkpoint inhibitors (ICIs) have shown promising results for the treatment of multiple cancers. ICIs and related therapies may also be useful for the treatment of thyroid cancer (TC). In TC, Myc binding protein 2 (MYCBP2) is correlated with inflammatory cell infiltration and cancer prognosis. However, the relationship between MYCBP2 expression and ICI efficacy in TC patients is unclear.MethodsWe downloaded data from two TC cohorts, including transcriptomic data and clinical prognosis data. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict the efficacy of ICIs in TC patients. MCPcounter, xCell, and quanTIseq were used to calculate immune cell infiltration scores. Gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA) were used to evaluate signaling pathway scores. Immunohistochemical (IHC) analysis and clinical follow up was used to identify the MYCBP2 protein expression status in patients and associated with clinical outcome.ResultsA higher proportion of MYCBP2-high TC patients were predicted ICI responders than MYCBP2-low patients. MYCBP2-high patients also had significantly increased infiltration of CD8+ T cells, cytotoxic lymphocytes (CTLs), B cells, natural killer (NK) cells and dendritic cells (DC)s. Compared with MYCBP2-low patients, MYCBP2-high patients had higher expression of genes associated with B cells, CD8+ T cells, macrophages, plasmacytoid dendritic cells (pDCs), antigen processing and presentation, inflammatory stimulation, and interferon (IFN) responses. GSEA and ssGSEA also showed that MYCBP2-high patients had significantly increased activity of inflammatory factors and signaling pathways associated with immune responses.In addiation, Patients in our local cohort with high MYCBP2 expression always had a better prognosis and greater sensitivity to therapy while compared to patients with low MYCBP2 expression after six months clinic follow up.ConclusionsIn this study, we found that MYCBP2 may be a predictive biomarker for ICI efficacy in TC patients. High MYCBP2 expression was associated with significantly enriched immune cell infiltration. MYCBP2 may also be involved in the regulation of signaling pathways associated with anti-tumor immune responses or the production of inflammatory factors

Diagnostic segregation of human breast tumours using Fourier-transform infrared spectroscopy coupled with multivariate analysis: Classifying cancer subtypes

The present study aimed to investigate whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with multivariate analysis could be applied to discriminate and classify among breast tumour molecular subtypes based on the unique spectral "fingerprints" of their biochemical composition. The different breast cancer tissues and normal breast tissues were collected and identified by pathology and ATR-FTIR spectroscopy respectively. The study indicates that the levels of the lipid-to-protein, nucleic acid-to-lipid, phosphate-to-carbohydrate and their secondary structure ratio, including RNA-to-DNA, Amide I-to-Amide II, and RNA-to-lipid ratios were significantly altered among the molecular subtype of breast tumour compared with normal breast tissues, which helps explain the changes in the biochemical structure of different molecular phenotypes of breast cancer. Tentatively-assigned characteristic peak ratios of infrared (IR) spectra reflect the changes of the macromolecule structure in different issues to a great extent and can be used as a potential biomarker to predict the molecular subtype of breast tumour. The present study acts as the first case study to show the successful application of IR spectroscopy in classifying subtypes of breast cancer with biochemical alterations. Therefore, the present study is likely to help to provide a new diagnostic approach for the accurate diagnosis of breast tumours and differential molecular subtypes and has the potential to be used for further intraoperative management. [Abstract copyright: Copyright © 2021 Elsevier B.V. All rights reserved.