Current medicinal chemistry · 2026
Identification and Experimental Validation of PLAC8 as a Biomarker for Follicular Lymphoma.
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Zhang Ting, He Li, Zhu Yidong
Аннотация
INTRODUCTION: Follicular lymphoma (FL) is the second most common lymphoma type. However, the molecular mechanisms underlying its pathogenesis remain poorly understood. This study aimed to identify and validate potential FL biomarkers using a combination of microarray analysis, machine learning, and experimental validation.
METHODS: Differential expression analysis was performed to identify differentially expressed genes (DEGs) between FL patients and matched controls using microarray datasets. Multiple machine learning algorithms were used to identify the hub genes for FL. The predictive performance was further evaluated using the receiver operating characteristic curves for all datasets. Functional analyses were performed to explore the underlying mechanisms. The expression of the biomarker was validated in clinical FL samples. Additionally, gene knockdown and overexpression experiments were conducted to assess the effects of the biomarker on biological functions, such as cell proliferation, apoptosis, and migration in FL cells.
RESULTS: A total of 144 DEGs were identified between the FL and control samples. Machine learning algorithms refined the four FL hub genes. Following comprehensive evaluations across all datasets, placenta-associated 8 (PLAC8) was identified as the most significant gene with area under the curve values exceeding 0.879 for all datasets. Functional analyses suggested a correlation between PLAC8 and immune-related pathways in FL. In clinical samples, PLAC8 expression was significantly lower in patients with FL than in controls. Experimental validation revealed that reduced PLAC8 expression enhanced FL cell proliferation and migration while, inhibiting apoptosis, whereas increased PLAC8 expression suppressed proliferation, activated apoptotic pathways, and reduced migration in vitro.
DISCUSSION: The combination of microarray analysis and machine learning enables the identification of key variables and complex relationships within the data, offering insights that might be overlooked by traditional methods. Our findings were further supported by experimental validation, underscoring the potential clinical relevance. This integrated approach provides a robust framework for the discovery of biomarkers of complex diseases.
CONCLUSION: This study identified and validated PLAC8 as a promising biomarker for FL. Functional experiments further demonstrated that PLAC8 plays a regulatory role in FL cell behavior in vitro. These findings provide insights into the molecular mechanisms underlying this disease.
METHODS: Differential expression analysis was performed to identify differentially expressed genes (DEGs) between FL patients and matched controls using microarray datasets. Multiple machine learning algorithms were used to identify the hub genes for FL. The predictive performance was further evaluated using the receiver operating characteristic curves for all datasets. Functional analyses were performed to explore the underlying mechanisms. The expression of the biomarker was validated in clinical FL samples. Additionally, gene knockdown and overexpression experiments were conducted to assess the effects of the biomarker on biological functions, such as cell proliferation, apoptosis, and migration in FL cells.
RESULTS: A total of 144 DEGs were identified between the FL and control samples. Machine learning algorithms refined the four FL hub genes. Following comprehensive evaluations across all datasets, placenta-associated 8 (PLAC8) was identified as the most significant gene with area under the curve values exceeding 0.879 for all datasets. Functional analyses suggested a correlation between PLAC8 and immune-related pathways in FL. In clinical samples, PLAC8 expression was significantly lower in patients with FL than in controls. Experimental validation revealed that reduced PLAC8 expression enhanced FL cell proliferation and migration while, inhibiting apoptosis, whereas increased PLAC8 expression suppressed proliferation, activated apoptotic pathways, and reduced migration in vitro.
DISCUSSION: The combination of microarray analysis and machine learning enables the identification of key variables and complex relationships within the data, offering insights that might be overlooked by traditional methods. Our findings were further supported by experimental validation, underscoring the potential clinical relevance. This integrated approach provides a robust framework for the discovery of biomarkers of complex diseases.
CONCLUSION: This study identified and validated PLAC8 as a promising biomarker for FL. Functional experiments further demonstrated that PLAC8 plays a regulatory role in FL cell behavior in vitro. These findings provide insights into the molecular mechanisms underlying this disease.