HSSG: Identification of Cancer Subtypes Based on Heterogeneity Score of A Single Gene

Abstract

Cancer is a highly heterogeneous disease, which leads to the fact that even the same cancer can be further classified into different subtypes according to its pathology. With the multi-omics data widely used in cancer subtypes identification, effective feature selection is essential for accurately identifying cancer subtypes. However, the feature selection in the existing cancer subtypes identification methods has the problem that the most helpful features cannot be selected from a biomolecular perspective, and the relationship between the selected features cannot be reflected. To solve this problem, we propose a method for feature selection to identify cancer subtypes based on the heterogeneity score of a single gene: HSSG. In the proposed method, the sample-similarity network of a single gene is constructed, and pseudo-F statistics calculates the heterogeneity score for cancer subtypes identification of each gene. Finally, we construct gene-gene networks using genes with higher heterogeneity scores and mine essential genes from the networks. From the seven TCGA data sets for three experiments, including cancer subtypes identification in single-omics data, the performance in feature selection of multi-omics data, and the effectiveness and stability of the selected features, HSSG achieves good performance in all. This indicates that HSSG can effectively select features for subtypes identification.

MMKNF: Integrating multi-omics data to identify cancer subtypes based on multi-kernel network fusion

“…This algorithm can not only classify patients but also discover biomarkers. The speci city of cancer samples is also important in the identi cation of cancer subtypes, and based on this idea, some works have applied speci city identi cation of cancer subtypes, such as SSIG [23] , HSSG [24] . It takes into account the heterogeneity of multi-omics data and integrates multi-omics data.…”