Gene expression and survival analysis study of KIAA0101 gene revealed its prognostic and diagnostic importance in breast cancer

Iqbal, Md Shahid; Ahmad, Nesar; Mirza, Zeenat; Karim, Sajjad

doi:10.1007/s42535-022-00561-6

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In this context,

refers to the count of individuals at risk at a specific time tᵢ , and

is the count of events that happen at the same time, tᵢ . The survival curve remains unchanging between the two events or times, i.e., between tᵢ and tᵢ + 1 [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…The KM estimator, a statistical technique tool (available at https://kmplot.com/ analysis/, accessed on 10 February 2023) was used for calculating survival probability functions to investigate the overall and relapse-free survival of prognostic genes for breast cancer patients. It is assumed that the occurrence of the event is fixed in time, and both the censored observations and data points have an equal chance of survival [8,36].…”

Section: Survival Analysis Using the Kaplan-meier Estimatormentioning

confidence: 99%

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Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis

Mirza

Ansari

Iqbal

et al. 2023

Cancers

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Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

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“…In this context,

refers to the count of individuals at risk at a specific time tᵢ , and

is the count of events that happen at the same time, tᵢ . The survival curve remains unchanging between the two events or times, i.e., between tᵢ and tᵢ + 1 [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Survival Analysis Using the Kaplan-meier Estimatormentioning

confidence: 99%