mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes

Wang, Canbiao; Qin, Si; Pan, Wanwan; Shi, Xuejia; Gao, Hanyu; Jin, Peng; Xia, Xinyi; Ma, Fei

doi:10.1016/j.csbj.2022.06.011

Cited by 8 publications

(6 citation statements)

References 72 publications

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“…Studies have suggested that mRNAsi might serve as an effective index for the survival, classification, and disease progression of tumor patients [25][26][27]. Huang et al found that basal breast cancer patients have high mRNAsi values [28].…”

Section: Discussionmentioning

confidence: 99%

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

Wang

Luo

et al. 2022

BMC Pulm Med

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Background To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD). Methods The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis. Results Patients in the high mRNAsi groups with poor prognosis were screened along with 144 immune-stemness genes. IL-6, FPR2, and RLN3 showed a higher degree in the PPI network. A total of 26 immune-stemness genes associated with prognosis were screened. Two clusters were obtained (cluster 1 and cluster 2). Survival analysis revealed that patients in cluster 2 had a poor prognosis. A total of 12 immune cell subpopulations exhibited significant differences between cluster 1 and cluster 2 (P < 0.05). A total of 10 immune checkpoint genes exhibited significantly higher expression in cluster 1 (P < 0.05) than in cluster 2. Further, the TMB value in cluster 2 was higher than that in cluster 1 (P < 0.05). Conclusion Immune-stemness genes, including L-6, FPR2, and RLN3, might play significant roles in LUAD development via cytokine–cytokine receptor interaction, neuroactive ligand‒receptor interaction, and the JAK‒STAT pathway. Immune-stemness genes were related to tumor-infiltrating immune cells, TMB, and expression of immune checkpoint gene.

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Section: Discussionmentioning

confidence: 99%

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

Wang

Luo

et al. 2022

BMC Pulm Med

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“…6,7 In 2018, Malta et al developed a mRNA expression based-index (mRNAsi) model using machine learning algorithm to evaluate the stemness of tumors. 8 Based on mRNAsi, many stemness signatures have been constructed for prognostic analysis, [9][10][11] including GC. [12][13][14] However, the absolute stemness index calculated by mR-NAsi is easily influenced by sample composition, 15 and in some cancers, including GC, contrary to previous research, higher stemness corresponds to a better prognosis.…”

Section: Introductionmentioning

confidence: 99%

An individualized stemness‐related signature to predict prognosis and immunotherapy responses for gastric cancer using single‐cell and bulk tissue transcriptomes

Zheng,

Chen,

et al. 2024

Cancer Medicine

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BackgroundCurrently, many stemness‐related signatures have been developed for gastric cancer (GC) to predict prognosis and immunotherapy outcomes. However, due to batch effects, these signatures cannot accurately analyze patients one by one, rendering them impractical in real clinical scenarios. Therefore, we aimed to develop an individualized and clinically applicable signature based on GC stemness.MethodsMalignant epithelial cells from single‐cell RNA‐Seq data of GC were used to identify stemness‐related signature genes based on the CytoTRACE score. Using two bulk tissue datasets as training data, the enrichment scores of the signature genes were applied to classify samples into two subtypes. Then, using the identified subtypes as criteria, we developed an individualized stemness‐related signature based on the within‐sample relative expression orderings of genes.ResultsWe identified 175 stemness‐related signature genes, which exhibited significantly higher AUCell scores in poorly differentiated GCs compared to differentiated GCs. In training datasets, GC samples were classified into two subtypes with significantly different survival times and genomic characteristics. Utilizing the two subtypes, an individualized signature was constructed containing 47 gene pairs. In four independent testing datasets, GC samples classified as high risk exhibited significantly shorter survival times, higher infiltration of M2 macrophages, and lower immune responses compared to low‐risk samples. Moreover, the potential therapeutic targets and corresponding drugs were identified for the high‐risk group, such as CD248 targeted by ontuxizumab.ConclusionsWe developed an individualized stemness‐related signature, which can accurately predict the prognosis and efficacy of immunotherapy for each GC sample.

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“…The index accuracy of the new m1A scoring model related to the immune microenvironment reached 0.71, 0.69, and 0.74 [ 19 ]. Considering the important influence of tumor stem cells on tumor growth, Wang et al adopted one-class Logistic regression (OCLR) algorithm to quantify the ability of tumor dedifferentiation, dividing patients into subgroups representing different prognoses and drug resistance [ 20 ]. Using the immune characteristics of TIME to classify tumor samples, Hu found that the subtypes with better immune features usually responded well to immunotherapy [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

Deng,

Lai,

Zhang

et al. 2024

Heliyon

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mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes

Cited by 8 publications

References 72 publications

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

An individualized stemness‐related signature to predict prognosis and immunotherapy responses for gastric cancer using single‐cell and bulk tissue transcriptomes

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

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