Prognostic Cancer Gene Expression Signatures: Current Status and Challenges

Qian, Yuquan; Daza, Jimmy; Itzel, Timo; Betge, Johannes; Zhan, Tianzuo; Marmé, Frederik; Teufel, Andreas

doi:10.3390/cells10030648

Cited by 70 publications

(77 citation statements)

References 117 publications

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“… 9 , 10 These classification systems are currently limited in clinical practice due to a lack of standardization and the requirement of bioinformatics resources. 11 An immunohistochemistry-based scoring approach used to assess the recurrence risk, termed Immunoscore®, has been established and validated, which measures the tumor core and invasive margin of CD3+ and CD8+ T cells. 12 Although Immunoscore® exhibits stable power in assessing the recurrence risk of early-stage CRC, its performance remains moderately accurate according to the C-index evaluation in an international trial.…”

Section: Introductionmentioning

confidence: 99%

Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer

et al. 2022

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Summary Background Long non-coding RNAs (lncRNAs) have recently emerged as essential biomarkers of cancer progression. However, studies are limited regarding lncRNAs correlated with recurrence and fluorouracil-based adjuvant chemotherapy (ACT) in stage II/III colorectal cancer (CRC). Methods 1640 stage II/III CRC patients were enrolled from 15 independent datasets and a clinical in-house cohort. 10 prevalent machine learning algorithms were collected and then combined into 76 combinations. 109 published transcriptome signatures were also retrieved. qRT-PCR assay was performed to verify our model. Findings We comprehensively identified 27 stably recurrence-related lncRNAs from multi-center cohorts. According to these lncRNAs, a consensus machine learning-derived lncRNA signature (CMDLncS) that exhibited best power for predicting recurrence risk was determined from 76 kinds of algorithm combinations. A high CMDLncS indicated unfavorable recurrence and mortality rates. CMDLncS not only could work independently of common clinical traits (e.g., AJCC stage) and molecular features (e.g., microsatellite state, KRAS mutation), but also presented dramatically better performance than these variables. qRT-PCR results from 173 patients further verified our in-silico findings and assessed its feasible in different centers. Comparisons of CMDLncS with 109 published transcriptome signatures further demonstrated its predictive superiority. Additionally, patients with high CMDLncS benefited more from fluorouracil-based ACT and were characterized by activation of stromal and epithelial-mesenchymal transition, while patients with low CMDLncS suggested the sensitivity to bevacizumab and displayed enhanced immune activation. Interpretation CMDLncS provides an attractive platform for identifying patient at high risk of recurrence and could optimize precision treatment to improve the clinical outcomes in stage II/III CRC. Funding This study was supported by the National Natural Science Foundation of China (81,972,663); Henan Province Young and Middle‐Aged Health Science and Technology Innovation Talent Project (YXKC2020037); and Henan Provincial Health Commission Joint Youth Project (SB201902014).

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Section: Introductionmentioning

confidence: 99%

Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer

et al. 2022

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“…38 A recent review also arise the concern about the gene signatures that lack reproducibility and cannot enter clinical applications. 39 Although state-of-the-art bioinformatics tools have been extensively applied to HCC to mine biomarkers, current analysis pipelines still focus on hub genes, ignoring the module-level information. 40,41 We also observed that hub genes/signature genes are not so reliable.…”

Section: Discussionmentioning

confidence: 99%

“…Switch genes were enriched within the top 10 genes in each module (Hypergeometric test P < 0.01). They were SNAPC4, AXL, HNF1A, HNF4A, NCAPG, XAB2, TARS2, MED27, TAOK2, CD2BP2, ZNF768, (39) Factor: EHF (3E-6), ELK-1 (4E-6) PUF60 S3). MIENTURNET tool identified that these switch genes may be regulated by miR-98-5p (P = 0.02) and miR-484 (P = 0.03).…”

Section: Differentially Expressed Modules (Dem) In Hcc Developmentmentioning

confidence: 99%

Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance

Ye¹,

Sun²,

Huang³

et al. 2021

IJGM

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Background: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. HCC transcriptome has been extensively studied; however, the progress in disease mechanisms, prognosis, and treatment is still slow. Methods: A rank-based module-centric workflow was introduced to analyze important modules associated with HCC development, prognosis, and drug resistance. The currently largest HCC cell line RNA-Seq dataset from the LIMORE database was used to construct the reference modules by weighted gene co-expression network analysis. Results: Thirteen reference modules were identified with validated reproducibility. These modules were all associated with specific biological functions. Differentially expressed module analysis revealed the crucial modules during HCC development. Modules and hub genes are indicative of patient survival. Modules can differentiate patients in different HCC stages. Furthermore, drug resistance was revealed by drug-module association analysis. Based on differentially expressed modules and hub genes, six candidate drugs were screened. The hub genes of those modules merit further investigation. Conclusion:We proposed a reference module-based analysis of the HCC transcriptome. The identified modules are associated with HCC development, survival, and drug resistance. M3 and M6 may play important roles during HCV to HCC development. M1, M3, M5, and M7 are associated with HCC survival. High M4, high M9, low M1, and low M3 may be associated with dasatinib, doxorubicin, CD532, and simvastatin resistance. Our analysis provides useful information for HCC diagnosis and treatment.

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“…Hu and other researchers used the NMF algorithm with the rank value set to 3 to classify patients with CRC, but they did not compare the differences in different typing methods when the rank value was at 2, 3, and 5 respectively. Some scholars [32][33][34][35][36][37][38][39][40] built risk models to predict the survival of patients with CRC, but no one compared the accuracy of their respective models.…”

Section: Discussionmentioning

confidence: 99%

Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

Wang

2021

Preprint

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This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

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Prognostic Cancer Gene Expression Signatures: Current Status and Challenges

Cited by 70 publications

References 117 publications

Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer

Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer

Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance

Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

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