Ciencias Exactas y Ciencias de la Salud

Now showing 1 - 2 of 2

Development of biosensor-based diagnostic systems for breast cancer using biorecognition engineering techniques and machine learning approaches for biomarker discovery
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-07-25) Mayoral Peña, Kalaumari; De Donato Capote, Marcos; emipsanchez; Artzi, Natalie; Víctor Manuel Treviño Alvarado; Alfaro Ponce, Mariel; School of Engineering and Sciences; Campus Monterrey; González Peña, Omar Israel
Cancer is the second cause of mortality worldwide, while breast cancer is the second leading cause of global female mortality. Diagnosing and treating breast cancer patients at early stages is relevant for successful treatment and increasing the patient's survival rate. However, early diagnosis of this complex disease is challenging, especially in populations with limited healthcare services. As a result, developing more accessible and accurate diagnostic tools is necessary. The development of low-cost biosensor technologies that have been relevant in the last two decades, but these technologies are still in the process of reaching maturity. For these reasons, we decided to study two promising technologies that can be implemented in cancer biosensor development: 1) biorecognition engineering techniques; 2) machine learning approaches for biomarker discovery. The first technology comprises alternative techniques to generate molecules and molecule-based scaffolds with similar properties to those presented by antibodies. In this study, we presented a systematic analysis of the scientific peer-reviewed literature in the Web of Science from the last two decades to present the fundamentals of this technology and address questions about how it has been implemented in biosensors for cancer detection. The three techniques analyzed were molecularly imprinted polymers, recombinant antibodies, and antibody mimetic molecules. The PRISMA methodology included 131 scientific from 2019 to 2021 for further analysis. The results showed that antibody mimetic molecules technology was the biorecognition technology with the highest number of reports. The most studied cancer types were: multiple, breast, leukemia, colorectal, and lung. Electrochemical and optical detection methods were the most frequently used. Finally, the most analyzed biomarkers and cancer entities in the studies were carcinoembryonic antigen, MCF-7 cells, and exosomes. For the second technology, we developed a novel bioinformatics pipeline that uses machine learning algorithms (MLAs) to identify genetic biomarkers for classifying breast cancer into non-malignant, non-triple-negative, and triple-negative categories. Five Gene Selection Approaches (GSAs) were employed: LASSO (Least Absolute Shrinkage and Selection Operator), Membrane LASSO, Surfaceome LASSO, Network Analysis, and Feature Importance Score (FIS). We implemented three factorial designs to assess the impact of MLAs and GSAs on classification performance (F1 Macro and Accuracy) in both cell lines and patient samples. Using Recursive Feature Elimination (RFE) and Genetic Algorithms (GAs) in the first four GSAs, we reduced the gene count to eight per GSA while maintaining an F1 Macro ≥ 80%. Consequently, 95.5% of our treatments with these gene sets achieved an F1 Macro or Accuracy ranging from 70.3% to 97.2%. As a result, 37 different genes were obtained. We analyzed the 37 genes for their predictive power in terms of five-year survival and relapse-free survival and compared them with genes from four commercial panels. Notably, thirteen genes (MFSD2A, TMEM74, SFRP1, UBXN10, CACNA1H, ERBB2, SIDT1, TMEM129, MME, FLRT2, CA12, ESR1, and TBC1D9) showed significant predictive capabilities for up to five years of survival. TBC1D9, UBXN10, SFRP1, and MME were significant for relapse-free survival after five years. The FOXC1, MLPH, FOXA1, ESR1, ERBB2, and SFRP1 genes also matched those described in commercial panels. The influence of MLA on F1 Macro and Accuracy was not statistically significant. Altogether, the genetic biomarkers identified in this study hold potential for use in biosensors aimed at breast cancer diagnosis and treatment. We concluded that both technologies had demonstrated their utility in cancer biosensor development for vulnerable populations with limited access to healthcare. However, further studies are required, and a long road exists to establish a commercial biosensor. For this reason, we generated a research proposal to develop a biosensor integrating this study's information in an optical and electrochemical sensing platform. Also, some designs of this biosensor and preliminary results are presented.
Landscape of mutations in DNA repair genes in young women with breast cancer
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12-06) Urbina Jara, Laura Keren; ORTIZ LOPEZ, ROCIO; 21416; Ortiz López, Rocío; emipsanchez; Santuario Facio, Sandra Karina; Rojas Martínez, Augusto; Martínez Ledesma, Juan Emmanuel; Escuela de Medicina y Ciencias de la Salud; Campus Monterrey; Gómez Flores Ramos, Liliana
There is a geographical difference in the distribution of breast cancer (BC) incidence for young women with BC (YWBC) under 40 years old (y.o.), with 15% in Latin America (LA) versus 7% in high-income countries. Most YWBC are generally diagnosed in advanced stages and with larger tumors. Multiple altered DNA repair genes are implied in BC predisposition, development, and outcome. Among these, pathogenic variants in BRCA1/2 genes account for 50–60%, the remaining to non-BRCA genes like ATM, PALB2, and RAD51. Moreover, germline alterations in DNA repair genes have been reported in YWBC. Considering that cancer cell lines databases analysis can be used to test causal hypotheses and search for new therapeutic agents. We compared BC cell lines and DNA repair variants reported in YWBC from LA. We identified only 6 BC cell lines that carry three variants for TP53 and 2 variants for BRCA1. This shows an underrepresentation for variants in LA in commercial BC cell lines used in preliminary drug studies. Further, to identify rare likely pathogenic variants for DNA repair genes, we analyzed our cohort of 115 Mexican women ≤40 years with BC. Fourty variants were identified in 18 DNA repair genes. 80% of variants were not reported in databases like COSMIC. A frequency of 35.6% of rare, likely pathogenic variants (RLPV) was observed. There are lifestyle factors that might influence the penetrance of DNA repair gene mutations. Herein, associations were found for Luminal BC and smoking for RLPV in DNA repair genes. Considering the roles of these genes in DNA repair it is possible to suggest an additive effect in terms of absolute risk in these mutation carriers. While there are several reports of germline and somatic variants in BC from Latin America (LA), they are scarce compared to other populations. Identifying and studying of RLPV in YWBC will allow more informed decisions concerning BC treatment and prevention.

Ciencias Exactas y Ciencias de la Salud

Browse

Filters

Settings

Sort By

Results per page

Search Results