Shengwei Li

Dr. Shengwei Li

orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2021 in Cancer/Radiothérapie
Verified
Journal Article
Closed access
SLShengwei Li
Shengwei Li
JGJinhe Guo
Jinhe Guo
JLJian Lu
Jian Lu
CWC. Wang
C. Wang
HWH. Wu
H. Wu
RFR. Fan
R. Fan

25 May 2026 in Frontiers in Cell and Developmental Biology
Verified
Journal Article
Open access
PDF file
XLXuesong Li
Xuesong Li
GLGuanghao Li
Guanghao Li
TPTianchou Peng
Tianchou Peng
JQJunjun Qiu
Junjun Qiu
SLShengwei Li
Shengwei Li
JZJin Zhang
Jin Zhang
Background: Osteosarcoma (OS) is the most common primary malignant bone tumor. Intratumoral heterogeneity (ITH) contributes to therapy resistance and disease spread. Understanding the molecular structure and cellular groups contributing to ITH, as well as their clinical impacts, remains limited. Methods: To build our investigation, we started by integrating single-cell transcriptomic data with multi-cohort transcriptomic datasets sourced from public repositories. Our bioinformatics pipeline was then designed to systematically unpack this information: first, we identified malignant cells by inferring their copy number variation (CNV); next, we assessed the degree of heterogeneity and defined distinct molecular subtypes; and finally, we conducted an in-depth analysis of the functional pathways mediating these heterogeneous characteristics. To sharpen the prognostic model linked to ITH, we employed machine learning to develop and validate a prognostic risk model for ten critical genes and performed both clinical and functional assays. Meanwhile, we validated the pro-tumorigenic role and therapeutic viability of CKMT2 through immunohistochemistry (IHC) and a series of in vitro functional assays. Results: Analysis of transcriptomic data from single-cells highlighted ten primary cell types in osteosarcoma. The dominant malignant population as per CNV analysis has been identified as osteoblastic cells (OBs). The subsequent subclassification of OB led to the discovery of five malignant subtypes, resulting in stratification of high and low ITH. Consensus clustering, driven by the differential expression genes discovered in ITH, successfully divided the population into two distinct molecular subtypes. These subtypes exhibited a robust correlation with clinical prognosis and immune infiltration in multiple independent cohorts. Through machine learning modeling, we identified a highly promising therapeutic target, Creatine Kinase Mitochondrial 2 (CKMT2), and performed both clinical and functional assays. IHC validated high CKMT2 expression in tumor tissues corresponding to clinical stage, and in vitro CKMT2 knockdown significantly decreased OS cell proliferation and colony formation. Conclusion: This study provides a systematic map of osteosarcoma’s ITH landscape, identifying distinct molecular subtypes with varying clinical outcomes and immune profiles. CKMT2 is established as a key driver of osteosarcoma progression, serving both as a prognostic biomarker and a potential therapeutic target.