Identification of a novel 15‐gene expression signature predicting overall survival of human colorectal cancer

Jiang, Cuiping; Zhao, Yue; Yuan, Binbin; Chang, Hang; Hang, Bo; Snijders, Antoine M.; Mao, Jian‐Hua; Zou, Xiaoping; Wang, Pin

doi:10.1002/ctm2.258

Cited by 3 publications

(6 citation statements)

References 10 publications

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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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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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“…Moreover, the efficacy of this signature was assessed in a retrospective cohort of 203 patients from Nanjing Drum Tower Hospital with stage I or II colorectal cancer. Overall survival analysis demonstrated significantly different survival rates (P < .0001 by log-rank test) among the three prognostic score groups in the Chinese patient cohort (21) with early-stage colorectal cancer. Similar to gastric cancer, we examined whether the prognostic power of this signature is independent of clinicopathological factors potentially associated with patient outcomes in both The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) and our Nanjing Drum Tower Hospital cohort.…”

Section: -Gene Prognostic Signature In Colorectal Cancermentioning

confidence: 91%

“…The final set of 15 genes demonstrated clear discriminative capability to stratify colorectal cancer patients based on good versus poor prognosis (21).…”

Section: -Gene Prognostic Signature In Colorectal Cancermentioning

confidence: 99%

“…For our gene expression signature development in colorectal cancer, we first identified 738 genes that were consistently deregulated in colorectal cancer versus normal colon tissue in six transcriptome datasets (21). Of them, 78 genes were significantly associated with overall survival of colorectal cancer patients.…”

Section: -Gene Prognostic Signature In Colorectal Cancermentioning

confidence: 99%

“…We also discovered the predictive potential of the 53-gene signature that can identify gastric cancer patients who may benefit from adjuvant FOLFOX (leucovorin calcium, fluorouracil, and oxaliplatin) chemotherapy (16). Later, we developed a 15-gene signature with robust prognostic function in colorectal cancer (21). Both signatures are independent of molecular subtypes and clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Multigene Expression Biomarkers and Score Systems for Predicting Therapeutic Benefit in Gastrointestinal Cancers

Hang¹,

Wang²,

Mao³

2022

Gastrointestinal Cancers

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An AI-Powered tissue-agnostic cellular morphometrics biomarker for risk assessment in patients with pan-gastrointestinal precancerous lesions and cancers

Wang,

Jiang,

Mao

et al. 2024

Preprint

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PURPOSE Tissue-agnostic biomarkers that capture the commonality in cancer biology, may provide a new avenue for treatment development and optimization across cancer types. Here, we aimed to evaluate and validate the clinical value of a tissue-agnostic cellular morphometrics biomarker (CMB) signature, which was discovered by artificial intelligence (AI) from H&E-stained whole-slide images (WSI) of diagnostic slides of colon cancers, in pan-gastrointestinal (pan-GI) pre-cancer lesions and cancers. METHODS We discovered CMBs from WSI using our well-established CMB-ML pipeline and established a CMB risk score (CMBRS) using multivariate regression models. Based on CMBRS, we assigned individual patients from The Cancer Genome Atlas Colon Adenocarcinoma Cohort (TCGA-COAD) (n=430) to CMB risk groups (CMBRG). We then extensively evaluated tissue-agnostic clinical value of CMB signature, CMBRS and CMBRG in multi-cohorts with different types of GI cancer (n=2,219) and risk assessment of precancerous lesions (n=1,016). We unraveled each CMB-related biological function using bulk RNA-sequencing, single-cell RNA-sequencing (scRNA-seq) and opal multiplex immunohistochemistry (IHC) techniques. RESULTS From the TCGA-COAD cohort, we developed a 13-CMB signature and constructed CMBRS/CMBRG that predict prognosis of colon cancer patients. Importantly, this 13-CMB signature proved prognostic and predictive values for TCGA patients with rectal, gastric and esophageal cancer independent of traditional clinical factors. These findings were independently validated using multiple cohorts from Drum Tower Hospital. Moreover, 13-CMB signature exhibited the power for risk stratification of colon adenoma and early esophageal neoplastic lesion patients for predicting cancer progression. In addition, we demonstrated and validated independent prognostic impacts of gene signatures and CMB signatures and a significant increase in predictive power by integration of CMB signature, gene signature and clinical factors. Correlations between CMBs and gene expression levels revealed the association of each CMB with biological functions including cell proliferation, epithelial-to-mesenchymal transition and immune microenvironment. The association of CMBs with the immune microenvironment was prospectively validated by scRNA-seq and was further confirmed by Opal multiplex IHC staining in colon cancer. CONCLUSION This study demonstrates the clinical value of tissue-agnostic AI-empowered CMB signature from WSI with defined biological functions, which can be used in clinical settings to assess risk, diagnose disease, and guide clinical interventions. Tissue-agnostic CMBs potentially provide a new avenue for a rapid, robust and cost-effective cross-cancer prediction that is essential for developing common treatment strategy for multiple cancers.

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Identification of a novel 15‐gene expression signature predicting overall survival of human colorectal cancer

Cited by 3 publications

References 10 publications

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

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

Multigene Expression Biomarkers and Score Systems for Predicting Therapeutic Benefit in Gastrointestinal Cancers

An AI-Powered tissue-agnostic cellular morphometrics biomarker for risk assessment in patients with pan-gastrointestinal precancerous lesions and cancers

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