Discovery and Validation of New Potential Biomarkers for Early Detection of Colon Cancer

<div><p>Background</p><p>Accurate detection of characteristic proteins secreted by colon cancer tumor cells in biological fluids could serve as a biomarker for the disease. The aim of the present study was to identify and validate new serum biomarkers and demonstrate their potential usefulness for early diagnosis of colon cancer.</p><p>Methods</p><p>The study was organized in three sequential phases: 1) biomarker discovery, 2) technical and biological validation, and 3) proof of concept to test the potential clinical use of selected biomarkers. A prioritized subset of the differentially-expressed genes between tissue types (50 colon mucosa from cancer-free individuals and 100 normal-tumor pairs from colon cancer patients) was validated and further tested in a series of serum samples from 80 colon cancer cases, 23 patients with adenoma and 77 cancer-free controls.</p><p>Results</p><p>In the discovery phase, 505 unique candidate biomarkers were identified, with highly significant results and high capacity to discriminate between the different tissue types. After a subsequent prioritization, all tested genes (N = 23) were successfully validated in tissue, and one of them, COL10A1, showed relevant differences in serum protein levels between controls, patients with adenoma (p = 0.0083) and colon cancer cases (p = 3.2e-6).</p><p>Conclusion</p><p>We present a sequential process for the identification and further validation of biomarkers for early detection of colon cancer that identifies COL10A1 protein levels in serum as a potential diagnostic candidate to detect both adenoma lesions and tumor.</p><p>Impact</p><p>The use of a cheap serum test for colon cancer screening should improve its participation rates and contribute to decrease the burden of this disease.</p></div

Polymorphisms in Alcohol Metabolism Genes <i>ADH1B</i> and <i>ALDH2</i>, Alcohol Consumption and Colorectal Cancer

<div><p>Background</p><p>Colorectal cancer (CRC) is a leading cause of cancer death worldwide. Epidemiological risk factors for CRC included alcohol intake, which is mainly metabolized to acetaldehyde by alcohol dehydrogenase and further oxidized to acetate by aldehyde dehydrogenase; consequently, the role of genes in the alcohol metabolism pathways is of particular interest. The aim of this study is to analyze the association between SNPs in <i>ADH1B</i> and <i>ALDH2</i> genes and CRC risk, and also the main effect of alcohol consumption on CRC risk in the study population.</p><p>Methodology/Principal Findings</p><p>SNPs from <i>ADH1B</i> and <i>ALDH2</i> genes, included in alcohol metabolism pathway, were genotyped in 1694 CRC cases and 1851 matched controls from the Molecular Epidemiology of Colorectal Cancer study. Information on clinicopathological characteristics, lifestyle and dietary habits were also obtained. Logistic regression and association analysis were conducted. A positive association between alcohol consumption and CRC risk was observed in male participants from the Molecular Epidemiology of Colorectal Cancer study (MECC) study (OR = 1.47; 95%CI = 1.18-1.81). Moreover, the SNPs rs1229984 in <i>ADH1B</i> gene was found to be associated with CRC risk: under the recessive model, the OR was 1.75 for A/A genotype (95%CI = 1.21-2.52; p-value = 0.0025). A path analysis based on structural equation modeling showed a direct effect of <i>ADH1B</i> gene polymorphisms on colorectal carcinogenesis and also an indirect effect mediated through alcohol consumption.</p><p>Conclusions/Significance</p><p>Genetic polymorphisms in the alcohol metabolism pathways have a potential role in colorectal carcinogenesis, probably due to the differences in the ethanol metabolism and acetaldehyde oxidation of these enzyme variants.</p></div

Association between polymorphisms in alcohol metabolism genes and colorectal cancer risk (men only).

1<p>OR adjusted for age, ethnicity, religion, energy intake and physical activity.</p

Main characteristics of the subjects included in the study.

<p>Main characteristics of the subjects included in the study.</p

Differences in expression between tissue types in the biomarker discovery series.

<p><b>A.</b> Principal component analysis. <b>B.</b> Differentially expressed genes between adjacent normal and cancer-free samples. <b>C.</b> Differentially expressed genes between tumor and cancer-free samples.</p

Heatmap of threshold cycle values from technical (A) and biological validation (B).

<p>Samples are color-coded on top of the heatmaps based on the tissue type (i.e., cancer-free mucosa = green, adjacent normal tissue from colon cancer patients = blue, tumor tissue = red).</p

COL10A1 performance as a diagnostic biomarker.

<p><b>A.</b> Receiver operating characteristic curves for both adenomas and colon cancer together (purple) and colon cancer cases only (red). <b>B.</b> Different marker cutpoints against the sensitivity and specificity curves.</p

Selected genes to be technically and biologically validated.

<p>*T/F: expression in tumor > expression in cancer-free mucosa; A/F: expression in adjacent normal mucosa > expression in cancer-free mucosa.</p><p>The association for all genes are significant after Bonferroni correction, but p-values shown are unadjusted for multiple comparisons.</p><p>Selected genes to be technically and biologically validated.</p

sj-docx-1-tam-10.1177_17588359231225028 – Supplemental material for Impact of SARS-CoV-2 vaccines and recent chemotherapy on COVID-19 morbidity and mortality in patients with soft tissue sarcoma: an analysis from the OnCovid registry

Supplemental material, sj-docx-1-tam-10.1177_17588359231225028 for Impact of SARS-CoV-2 vaccines and recent chemotherapy on COVID-19 morbidity and mortality in patients with soft tissue sarcoma: an analysis from the OnCovid registry by Bruno Vincenzi, Alessio Cortellini, Alessandro Mazzocca, Sarah Orlando, Davide Romandini, Juan Aguilar-Company, Isabel Ruiz-Camps, Claudia Valverde Morales, Simeon Eremiev-Eremiev, Carlo Tondini, Joan Brunet, Rossella Bertulli, Salvatore Provenzano, Mark Bower, Daniele Generali, Ramon Salazar, Anna Sureda, Aleix Prat, Michalarea Vasiliki, Mieke Van Hemelrijck, Ailsa Sita-Lumsden, Alexia Bertuzzi, Sabrina Rossi, Amanda Jackson, Federica Grosso, Alvin J. X. Lee, Cian Murphy, Katherine Belessiotis, Uma Mukherjee, Fanny Pommeret, Angela Loizidou, Gianluca Gaidano, Gino M. Dettorre, Salvatore Grisanti, Marco Tucci, Claudia A. M. Fulgenzi, Alessandra Gennari, Andrea Napolitano and David J. Pinato in Therapeutic Advances in Medical Oncology</p