SLAS technology · 2026
Identification of a telomere-aging-related gene as a novel prognostic factor for intrahepatic cholangiocarcinoma: Machine learning-aided biomarker discovery and experimental verification.
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Yin Jianjian, Wang Yingying, Zhang Hongmei, Qu Wenhao
Аннотация
BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is an aggressive hepatobiliary malignancy characterized by a complex pathogenesis and poor prognosis. Telomere dysfunction and cellular senescence are involved in the pathogenesis of various types of cancers. However, the prognostic significance of telomere- and aging-related genes in ICC remains unclear. This study aimed to identify such genes with prognostic significance, and construct a prognostic risk model for ICC.
METHODS: We screened prognostic genes associated with telomere-aging in patients using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We then integrated 10 base machine learning algorithms, and generated 101 model configurations via parameter optimization and algorithm combination strategies, to construct and validate a prognostic risk model. The clinical prognostic value was analyzed using a nomogram. Immune infiltration, drug sensitivity, immune responses, and subgroups were analyzed based on telomere-aging-related prognostic genes. We also validated core gene expression in cell lines and tissue samples using the quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and enrolled 76 patients with ICC to identify the prognostic value of key genes.
RESULTS: We constructed a prognostic model using the telomere-aging-related prognostic genes BET1L, RAD50, ANXA1, and AURKA. Survival analysis revealed a significant difference in overall survival between high- and low-risk groups. The expression of these genes was significantly increased in ICC cell lines and tissues. High BET1L expression was significantly associated with lymph node metastasis, tumor-node-metastasis (TNM) stage, tumor differentiation, and a poor prognosis for patients with ICC. Knocking down BET1L significantly reduced the proliferative ability of HUCCT1 cells.
CONCLUSIONS: We established a risk model comprising the telomere-aging-related prognostic genes BET1L, RAD50, ANXA1, and AURKA to predict the prognosis of patients with ICC. Elevated BET1L expression indicated a poor prognosis for patients with ICC, and low BET1L expression inhibited HUCCT1 cell proliferation.
METHODS: We screened prognostic genes associated with telomere-aging in patients using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We then integrated 10 base machine learning algorithms, and generated 101 model configurations via parameter optimization and algorithm combination strategies, to construct and validate a prognostic risk model. The clinical prognostic value was analyzed using a nomogram. Immune infiltration, drug sensitivity, immune responses, and subgroups were analyzed based on telomere-aging-related prognostic genes. We also validated core gene expression in cell lines and tissue samples using the quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and enrolled 76 patients with ICC to identify the prognostic value of key genes.
RESULTS: We constructed a prognostic model using the telomere-aging-related prognostic genes BET1L, RAD50, ANXA1, and AURKA. Survival analysis revealed a significant difference in overall survival between high- and low-risk groups. The expression of these genes was significantly increased in ICC cell lines and tissues. High BET1L expression was significantly associated with lymph node metastasis, tumor-node-metastasis (TNM) stage, tumor differentiation, and a poor prognosis for patients with ICC. Knocking down BET1L significantly reduced the proliferative ability of HUCCT1 cells.
CONCLUSIONS: We established a risk model comprising the telomere-aging-related prognostic genes BET1L, RAD50, ANXA1, and AURKA to predict the prognosis of patients with ICC. Elevated BET1L expression indicated a poor prognosis for patients with ICC, and low BET1L expression inhibited HUCCT1 cell proliferation.