However, relying simply on early prevention is insufficient if you want to thoroughly win the ultimate victory in the war against malignant tumors. Early detection and therapy may be utilized to reduce tumor mortality or considerably prolong the lives of tumor patients, thanks to the fast advancement of contemporary medical technology. Except for a few diseases, the 40-year “war against cancer” has been a failure. These questions and others like them must be addressed immediately. Few people understand the underlying issues that lead to malignant tumors, including what causes tumors to begin with and what causes them to spread and proliferate once they have metastasized. These studies have far-reaching significance for revealing the pathogenesis of cancer.Īlthough cancer’s high death rate is concerning, human knowledge of cancer is woefully inadequate at this point.

In a cancer review study, Professor Weinberg briefly described recent hotspots and progress in oncology and proposed some professional concepts in malignant tumors, including tumor cell characteristics, autophagy, tumor microenvironment, and tumor stem cells.

Since the 21st century, research on data mining and identification based on cancer data has sprung up. A large amount of biological data has laid a solid foundation for researchers to re-understand cancer. With the maturity of sequencing technology for large-scale samples, cancer researchers have shifted their focus to mining based on cancer big data. With the rapid development and promotion of high-throughput sequencing technology, many international scientific research institutions have hosted large-scale cancer genome sequencing projects. The analysis of the research results shows the reliability of high-throughput sequencing technology. In order to overcome the problem of gene mutation heterogeneity, this model introduces gene covariates as the weight of gene mutation frequency so that the model is adaptive to each gene. Based on the high coverage and high exclusivity of the driving gene set in the pathway and the influence of gene covariates, this paper constructs an adaptive multiobjective optimization model. After setting the size of the gene set, compared with the Dendrix algorithm, the model does not need to delete the gene set that has been found in the process of searching the pathway, and it can find more driving pathway gene sets. Based on the Dendrix algorithm, the model proposed in this paper selects the gene replication time as an inherent attribute that affects the frequency of gene mutations and adds it to the model. This paper studies the role of high-throughput measurement technology in cancer molecular typing.

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