Cancer Genetics and Epigenetics 2024, Vol.12, No.3, 144-156 http://medscipublisher.com/index.php/cge 152 Figure 4 Proteomic subtyping of CCRC and clinical implications for each subtype (Adopted from Li et al., 2020) Image caption: (A) Consensus clustering based on differentially expressed proteins between tumor and remote normal tissues. Each column represents a patient sample and rows indicate proteins. (B) Kaplan-Meier curves for relapse-free survival based on proteomic subgroups. The p value was calculated by log rank test. (C) Kaplan-Meier curves for relapse-free survival based on proteomic subgroups for mCRC. The p value was calculated by log rank test. (D) Venn diagram illustrates the overlap of differential gene mutations, SCNAs, or proteins among three CCs.(E) The top 10 differentially mutated genes that also showed differences in SCNA and protein levels. (F) The expression of proteins enriched in the mismatch repair pathway (top). The correlation between methylation level and protein expression of RFC3 and SSBP1 (bottom). Correlation coefficients and p values were calculated by the Spearman correlation method. (G) Comparison of proteomic subtyping of non-mCRC with previous subtyping results based on RNA (Guinney et al., 2015) or Western CRC patients (Vasaikar et al., 2019; Zhang et al., 2014). M, mCRC; noM, non-mCRC (Adopted from Li et al., 2020) Pan-cancer studies that utilize integrative approaches have demonstrated high predictive accuracy across multiple cancer types. These studies highlight the potential for broad applications of integrative models in clinical practice, providing valuable insights for personalized cancer treatment strategies. 6 Challenges and Limitations 6.1 Data heterogeneity The heterogeneous nature of cancer poses significant challenges to predicting treatment responses. Tumor heterogeneity involves variations at the genetic, cellular, and microenvironmental levels, leading to diverse responses to therapies. Majumder et al. (2015) highlighted the complexity of capturing tumor heterogeneity in personalized treatment strategies. Their study utilized engineered tumor ecosystems to maintain heterogeneity and used this data to predict clinical responses to anticancer drugs, emphasizing the need for models that can accurately reflect the diverse tumor environments (Majumder et al., 2015). Standardizing data across different platforms and studies remains a significant barrier. Data collected from various sources, such as genomic sequencing, proteomics, and imaging, often lack consistency in format and quality. This inconsistency can lead to challenges in integrating and interpreting the data effectively. Mathur et al. (2020)
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==