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Theranostics 2022; 12(13):5931-5948. doi:10.7150/thno.74281 This issue Cite

Research Paper

Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma

Nan Zhang1,2,8#, Hao Zhang1,8,9#, Wantao Wu1,3,8, Ran Zhou4, Shuyu Li5, Zeyu Wang1,8, Ziyu Dai1,8, Liyang Zhang1,8, Fangkun Liu1,8, Zaoqu Liu6, Jian Zhang7, Peng Luo7, Zhixiong Liu1,8✉, Quan Cheng1,8✉

1. Department of Neurosurgery, Xiangya Hospital, Central South University, China.
2. One-third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, China.
3. Department of Oncology, Xiangya Hospital, Central South University, China.
4. Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, UK.
5. Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, China.
6. Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou, China.
7. Department of Oncology, Zhujiang Hospital, Southern Medical University, China.
8. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, China.
9. Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, China.
#These authors contributed equally to this work.

✉ Corresponding authors: Dr. Zhixiong Liu, Department of Neurosurgery, Xiangya Hospital, Center South University, Address: Changsha 410008, Hunan, P. R. China. E-mail: zhixiongliuedu.cn; Dr. Quan Cheng, Department of Neurosurgery, Xiangya Hospita More

Citation:
Zhang N, Zhang H, Wu W, Zhou R, Li S, Wang Z, Dai Z, Zhang L, Liu F, Liu Z, Zhang J, Luo P, Liu Z, Cheng Q. Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics 2022; 12(13):5931-5948. doi:10.7150/thno.74281. https://www.thno.org/v12p5931.htm
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Abstract

Graphic abstract

Rationale: Accumulating evidence demonstrated that long noncoding RNAs (lncRNAs) involved in the regulation of the immune system and displayed a cell-type-specific pattern in immune cell subsets. Given the vital role of tumor-infiltrating lymphocytes in effective immunotherapy, we explored the tumor-infiltrating immune cell-associated lncRNA (TIIClncRNA) in low-grade glioma (LGG), which has never been uncovered yet.

Methods: This study utilized a novel computational framework and 10 machine learning algorithms (101 combinations) to screen out TIIClncRNAs by integratively analyzing the sequencing data of purified immune cells, LGG cell lines, and bulk LGG tissues.

Results: The established TIIClnc signature based on the 16 most potent TIIClncRNAs could predict outcomes in public datasets and the Xiangya in-house dataset with decent efficiency and showed better performance when compared with 95 published signatures. The TIIClnc signature was strongly correlated to immune characteristics, including microsatellite instability, tumor mutation burden, and interferon γ, and exhibited a more active immunologic process. Furthermore, the TIIClnc signature predicted superior immunotherapy response in multiple datasets across cancer types. Notably, the positive correlation between the TIIClnc signature and CD8, PD-1, and PD-L1 was verified in the Xiangya in-house dataset.

Conclusions: The TIIClnc signature enabled a more precise selection of the LGG population who were potential beneficiaries of immunotherapy.

Keywords: Immunotherapy, low-grade glioma, lncRNA, immune checkpoint, immune infiltration

Citation styles

APA
Zhang, N., Zhang, H., Wu, W., Zhou, R., Li, S., Wang, Z., Dai, Z., Zhang, L., Liu, F., Liu, Z., Zhang, J., Luo, P., Liu, Z., Cheng, Q. (2022). Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics, 12(13), 5931-5948. https://doi.org/10.7150/thno.74281.

ACS
Zhang, N.; Zhang, H.; Wu, W.; Zhou, R.; Li, S.; Wang, Z.; Dai, Z.; Zhang, L.; Liu, F.; Liu, Z.; Zhang, J.; Luo, P.; Liu, Z.; Cheng, Q. Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics 2022, 12 (13), 5931-5948. DOI: 10.7150/thno.74281.

NLM
Zhang N, Zhang H, Wu W, Zhou R, Li S, Wang Z, Dai Z, Zhang L, Liu F, Liu Z, Zhang J, Luo P, Liu Z, Cheng Q. Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics 2022; 12(13):5931-5948. doi:10.7150/thno.74281. https://www.thno.org/v12p5931.htm

CSE
Zhang N, Zhang H, Wu W, Zhou R, Li S, Wang Z, Dai Z, Zhang L, Liu F, Liu Z, Zhang J, Luo P, Liu Z, Cheng Q. 2022. Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma. Theranostics. 12(13):5931-5948.

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