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. 2022 May 5;9:834593. doi: 10.3389/fmolb.2022.834593

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Copyright © 2022 Xie, Tian, Tang, Zou, Zheng, Wu, Zeng, Wu, Xie and Xie.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Identification of potential subgroups using unsupervised consensus clustering. (A,B) Consensus clustering matrixes and cumulative distribution function (CDF) plots, which found k = 2 was the optimal cluster number not only in the training cohort (left) but also in the validation cohort (right). (C) Kaplan–Meier survival curves for OS of the TNBC patients with cluster 1 and cluster 2 in the training (left) and validation cohorts (right). (D) The alluvial diagram fully demonstrated the correlation of prognosis between the clusters and CNI values in both the training set (left) and validation set (right).