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Long Liu

Bio: Long Liu is an academic researcher from Zhengzhou University. The author has contributed to research in topics: Medicine & Immunotherapy. The author has an hindex of 5, co-authored 13 publications receiving 49 citations.

Papers
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Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper developed a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS), which is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival.
Abstract: Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.

113 citations

Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors developed a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS), which is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival.
Abstract: Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.

102 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper developed and validated a recurrenceassociated immune signature (RAIS) based on global immune genes to identify patients at high recurrence-risk for improving post-operative individual management.
Abstract: Background A considerable number of patients with stage II/III colorectal cancer (CRC) will relapse within 5 years after surgery, which is a leading cause of death in early-stage CRC. The current TNM stage system is limited due to the heterogeneous clinical outcomes displayed in patients of same stage. Therefore, searching for a novel tool to identify patients at high recurrence-risk for improving post-operative individual management is an urgent need. Methods Using four independent public cohorts and qRT-PCR data from 66 tissues, we developed and validated a recurrence-associated immune signature (RAIS) based on global immune genes. The clinical and molecular features, tumor immune microenvironment landscape, and immune checkpoints profiles of RAIS were also investigated. Results In five independent cohorts, this novel scoring system was proven to be an independent recurrent factor and displayed excellent discrimination and calibration in predicting the recurrence-risk at 1~5 years. Further analysis revealed that the high-risk group displayed high mutation rate of TP53, while the low-risk group had more abundance of activated CD4+/CD8+ T cells and high expression of PD-1/PD-L1. Conclusions The RAIS model is highly predictive of recurrence in patients with stage II/III CRC, which might serve as a powerful tool to further optimize decision-making in adjuvant chemotherapy and immunotherapy, as well as tailor surveillance protocol for individual patients.

37 citations

Journal ArticleDOI
Zaoqu Liu1, Libo Wang1, Long Liu1, Taoyuan Lu1, Dechao Jiao1, Yuling Sun1, Xinwei Han1 
TL;DR: In this article, a robust and promising signature termed ferroptosis related risk score (FRRS) was proposed for assessing prognosis and immunotherapy of hepatocellular carcinoma (HCC).
Abstract: Background Ferroptosis is essential for tumorigenesis and progression of hepatocellular carcinoma (HCC). The heterogeneity of ferroptosis and its relationship with tumor microenvironment (TME) have still remain elusive. Methods Based on 74 ferroptosis related genes (FRGs) and 3,933 HCC samples from 32 datasets, we comprehensively explored the heterogenous ferroptosis subtypes. The clinical significance, functional status, immune infiltration, immune escape mechanisms, and genomic alterations of different subtypes were further investigated. Results We identified and validated two heterogeneous ferroptosis subtypes: C1 was metabolismlowimmunityhigh subtype and C2 was metabolismhighimmunitylow subtype. Compared to C2, C1 owned worse prognosis, and C1 tended to occur in the patients with clinical characteristics such as younger, female, advanced stage, higher grade, vascular invasion. C1 and C2 were more sensitive to immunotherapy and sorafenib, respectively. The immune escape mechanisms of C1 might be accumulating more immunosuppressive cells, inhibitory cytokines, and immune checkpoints, while C2 was mainly associated with inferior immunogenicity, defecting in antigen presentation, and lacking leukocytes. In addition, C1 was characterized by BAP1 mutation, MYC amplification, and SCD1 methylation, while C2 was characterized by the significant alterations in cell cycle and chromatin remodeling processes. We also constructed and validated a robust and promising signature termed ferroptosis related risk score (FRRS) for assessing prognosis and immunotherapy. Conclusion We identified and validated two heterogeneous ferroptosis subtypes and a reliable risk signature which used to assess prognosis and immunotherapy. Our results facilitated the understood of ferroptosis as well as clinical management and precise therapy of HCC.

36 citations

Journal ArticleDOI
Zaoqu Liu1, Taoyuan Lu1, Libo Wang1, Long Liu1, Lifeng Li, Xinwei Han1 
TL;DR: Wang et al. as mentioned in this paper performed cluster analysis based on the mutational signatures and further investigated the multidimensional heterogeneity of the novel glioma molecular subtypes, the clinical significance and immune landscape of four clusters also investigated.
Abstract: Background: Glioma is the most common malignant brain tumor with complex carcinogenic process and poor prognosis. The current molecular classification cannot fully elucidate the molecular diversity of glioma. Methods: Using broad public datasets, we performed cluster analysis based on the mutational signatures and further investigated the multidimensional heterogeneity of the novel glioma molecular subtypes. The clinical significance and immune landscape of four clusters also investigated. The nomogram was developed using the mutational clusters and clinical characteristics. Results: Four heterogenous clusters were identified, termed C1, C2, C3, and C4, respectively. These clusters presented distinct molecular features: C1 was characterized by signature 1, PTEN mutation, chromosome seven amplification and chromosome 10 deletion; C2 was characterized by signature 8 and FLG mutation; C3 was characterized by signature 3 and 13, ATRX and TP53 mutations, and 11p15.1, 11p15.5, and 13q14.2 deletions; and C4 was characterized by signature 16, IDH1 mutation and chromosome 1p and 19q deletions. These clusters also varied in biological functions and immune status. We underlined the potential immune escape mechanisms: abundant stromal and immunosuppressive cells infiltration and immune checkpoints (ICPs) blockade in C1; lack of immune cells, low immunogenicity and antigen presentation defect in C2 and C4; and ICPs blockade in C3. Moreover, C4 possessed a better prognosis, and C1 and C3 were more likely to benefit from immunotherapy. A nomogram with excellent performance was also developed for assessing the prognosis of patients with glioma. Conclusion: Our results can enhance the mastery of molecular features and facilitate the precise treatment and clinical management of glioma.

26 citations


Cited by
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Journal ArticleDOI
TL;DR: Li et al. as mentioned in this paper developed a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS), which is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival.
Abstract: Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.

113 citations

Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors developed a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS), which is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival.
Abstract: Long noncoding RNAs (lncRNAs) are recently implicated in modifying immunology in colorectal cancer (CRC). Nevertheless, the clinical significance of immune-related lncRNAs remains largely unexplored. In this study, we develope a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS). IRLS is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival. Additionally, IRLS possesses distinctly superior accuracy than traditional clinical variables, molecular features, and 109 published signatures. Besides, the high-risk group is sensitive to fluorouracil-based adjuvant chemotherapy, while the low-risk group benefits more from bevacizumab. Notably, the low-risk group displays abundant lymphocyte infiltration, high expression of CD8A and PD-L1, and a response to pembrolizumab. Taken together, IRLS could serve as a robust and promising tool to improve clinical outcomes for individual CRC patients.

102 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a consensus machine learning-derived lncRNA signature (CMDLncS) that exhibited best power for predicting recurrence risk from 76 kinds of algorithm combinations.

50 citations

Journal ArticleDOI
TL;DR: This study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options, and developed a nine-gene stemness cluster predictor, which robustly validated and reproduced the authors' stemhood clusters in three independent datasets and an in-house cohort.
Abstract: Background Stemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC. Methods This study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored. Results Based on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes. Conclusions This study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

28 citations

Journal ArticleDOI
TL;DR: A lactate-related prognostic signature (LRPS) was developed for KIRC that was closely related to the immune landscape and therapeutic response and may guide clinicians to make more precise and personalized treatment decisions for K IRC patients.
Abstract: Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent primary malignancies with high heterogeneity in the urological system. Growing evidence implies that lactate is a significant carbon source for cell metabolism and plays a vital role in tumor development, maintenance, and therapeutic response. However, the global influence of lactate-related genes (LRGs) on prognostic significance, tumor microenvironment characteristics, and therapeutic response has not been comprehensively elucidated in patients with KIRC. In the present study, we collected RNA sequencing and clinical data of KIRC from The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts. Unsupervised clustering of 17 differentially expressed LRG profiles divided the samples into three clusters with distinct immune characteristics. Three genes (FBP1, HADH, and TYMP) were then identified to construct a lactate-related prognostic signature (LRPS) using the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The novel signature exhibited excellent robustness and predictive ability for the overall survival of patients. In addition, the constructed nomogram based on the LRPS-based risk scores and clinical factors (age, gender, tumor grade, and stage) showed a robust predictive performance. Furthermore, patients classified by risk scores had distinguishable immune status, tumor mutation burden, response to immunotherapy, and sensitivity to drugs. In conclusion, we developed an LRPS for KIRC that was closely related to the immune landscape and therapeutic response. This LRPS may guide clinicians to make more precise and personalized treatment decisions for KIRC patients.

22 citations