Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice. Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients' survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (, and ) with two reference genes was defined to calculate a risk score which determined groups of different risks. Our risk scoring efficiently classified patients ( = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort ( = 77) and showed high accuracy. Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery.
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http://dx.doi.org/10.3389/fgene.2021.728476 | DOI Listing |
Zhonghua Yu Fang Yi Xue Za Zhi
November 2024
Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou350122, China.
To construct a signature for identifying active tuberculosis (TB) based on the relative expression orderings (REOs) of gene expression within a single sample. Using peripheral whole blood samples from 75 active TB and 69 latently infected individuals from four datasets as the training set, and highly stable REO patterns were extracted from the gene expression profile of the two groups of samples. Then, the gene pairs that reversed the REO pattern between the two groups were selected, and each gene pair was ranked in descending order based on their reversal degree.
View Article and Find Full Text PDFInt J Mol Sci
October 2024
Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina.
Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (, , , , , , and ) was validated across independent cohorts and patient-derived xenograft (PDX) models.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
Emergency Department, Minhang Hospital, Fudan University, Shanghai 201100, China. Electronic address:
In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functionally related ALI-associated hub genes in sepsis were identified by MCODE analysis and they were enriched in infection and inflammtory responses, lung and cardiovascular disease pathways. These hub genes stratified ALI-sepsis and sepsis and further stratified two subtypes of sepsis-ALI with differential ALI scores, hub gene expression patterns, and levels of immune cells.
View Article and Find Full Text PDFMedicine (Baltimore)
October 2024
Department of Urology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
CD8+ T lymphocytes are important elements of the tumor microenvironment, hence their involvement in the development and progression of tumors is complex. Data on the precise tumor-infiltrating lymphocytes gene signature in renal cell carcinoma (RCC) remain limited. Therefore, this study created a tumor-infiltrating lymphocytes-related predictive model for patients with RCC using data from The Cancer Genome Atlas.
View Article and Find Full Text PDFTransl Cancer Res
September 2024
Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
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