
새로운 다중 기울기 순열 생존 분석(MEMORY) 방법을 사용하여 샘플 크기에 관계없이 암 환자 예후와 지속적으로 연관된 유전자(GEAR)를 식별하고, 이러한 GEAR가 유사 분열 및 면역과 관련된 기능을 통해 예후를 예측하는 데 유용한 바이오마커 역할을 한다는 것을 발견했습니다.


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The inconsistency of the association between genes and cancer prognosis is often attributed to many variables that contribute to patient survival. Whether there exist the Genes Steadily Associated with Prognosis (GEARs) and what their functions are remain largely elusive. We have developed a novel method called “Multi-gradient Permutation Survival Analysis” (MEMORY) to screen the GEARs using RNA-seq data from the TCGA database. Then we employed a network construction approach to identify hub genes from GEARs, and utilized them for cancer classification. In the case of LUAD, the GEARs were found to be related to mitosis. Our analysis suggested that LUAD cell lines carrying PIK3CA mutations exhibit increased drug resistance. For BRCA, the GEARs were related to immunity. The analysis revealed that CDH1 mutation might influence immune infiltration through the EMT process in BRCA. We further explored the prognostic relevance of mitosis and immunity through their respective scores. This study offers significant biological insights into GEARs and highlights their potential as robust prognostic indicators across diverse cancer types.

### Competing Interest Statement

The authors have declared no competing interest.

Multi-gradient Permutation Survival Analysis Identifies Mitosis and Immune Signatures Steadily Associated with Cancer Patient Prognosis

다중-기울기-순열-생존-분석을-통해-암-환자-예후와-지속적으로-연관된-유사-분열-및-면역-시그니처-식별


다중 기울기 순열 생존 분석을 통해 암 환자 예후와 지속적으로 연관된 유사 분열 및 면역 시그니처 식별


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HuLP는 임상 현장에서 예후 모델의 신뢰성과 해석 가능성을 높이기 위해 설계된 인간-AI 협업 모델입니다. 이 모델은 의료진의 개입을 허용하여 모델의 예측을 개선하고, 누락된 데이터를 효과적으로 처리할 수 있습니다.


암-예후-예측을-위한-인간-ai-협업-모델-hulp


암 예후 예측을 위한 인간-AI 협업 모델 HuLP
