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Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach

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Abstract

Relevance

The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome’s regulatory function is closely associated with the disease’s pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine).

Methods

This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan–Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters.

Results

This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin–proteasome system for degradation.

Conclusions

Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice.

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Data availability

All the data used in this study were collected in this article and supplemental materials.

Code availability

All protein and gene accession codes can be available in the Swiss-Prot and Genbank databases.

Abbreviations

ADRM1:

ADRM1 26S proteasome ubiquitin receptor

AIC:

Akaike Information Criterion

AUC:

Area under the curve

BP:

Biological process

CAR-T:

Chimeric antigen receptor T cell

CC:

Cellular component

CD274:

Programmed cell death 1 ligand 1

CD276:

CD276 antigen

CD80:

T-lymphocyte activation antigen CD80

CD86:

T-lymphocyte activation antigen CD86

CHST7:

Carbohydrate sulfotransferase 7

CMTM6:

CKLF-like MARVEL transmembrane domain-containing protein 6

CSN5:

COP9 signalosome complex subunit 5

CTLA4:

Cytotoxic T-lymphocyte protein 4

DEGs:

Differentially expressed genes

DTP:

Developmental Therapeutics Program

GAP43:

Gap junction alpha-1 protein

GO:

Gene ontology

GSEA:

Gene Set Enrichment Analysis

IL:

Interleukin

ITGA8:

Integrin alpha-8

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LUSC:

Lung squamous carcinomas

MAD:

Median absolute deviation

MF:

Molecular function

MHC:

Major Histocompatibility Complex

NCBP1:

Nuclear cap-binding protein subunit 1

NCI:

National Cancer Institute

NMF:

Nonnegative matrix factorization method

OPA1:

Dynamin-like 120 kDa protein

PD-1:

Programmed cell death protein 1

PDCD1:

Programmed cell death protein 1

PDCD1LG2:

Programmed cell death 1 ligand 2

PD-L1:

Programmed cell death 1 ligand 1

PPI:

Protein-protein interaction

PSMA1:

Proteasome subunit alpha type-1

PSMA2:

Proteasome subunit alpha type-2

PSMA3:

Proteasome subunit alpha type-3

PSMA4:

Proteasome subunit alpha type-4

PSMA5:

Proteasome subunit alpha type-5

PSMA6:

Proteasome subunit alpha type-6

PSMA7:

Proteasome subunit alpha type-7

PSMB1:

Proteasome subunit beta type-1

PSMB10:

Proteasome subunit beta type-10

PSMB11:

Proteasome subunit beta type-11

PSMB2:

Proteasome subunit beta type-2

PSMB3:

Proteasome subunit beta type-3

PSMB4:

Proteasome subunit beta type-4

PSMB5:

Proteasome subunit beta type-5

PSMB6:

Proteasome subunit beta type-6

PSMB7:

Proteasome subunit beta type-7

PSMB8:

Proteasome subunit beta type-8

PSMB9:

Proteasome subunit beta type-9

PSMC1:

26S proteasome regulatory subunit 4

PSMC2:

26S proteasome regulatory subunit 7

PSMC3:

26S proteasome regulatory subunit 6A

PSMC4:

26S proteasome regulatory subunit 6B

PSMC5:

26S proteasome regulatory subunit 8

PSMC6:

26S proteasome regulatory subunit 10B

PSMD1:

26S proteasome non-ATPase regulatory subunit 1

PSMD11:

26S proteasome non-ATPase regulatory subunit 11

PSMD12:

26S proteasome non-ATPase regulatory subunit 12

PSMD13:

26S proteasome non-ATPase regulatory subunit 13

PSMD14:

26S proteasome non-ATPase regulatory subunit 14

PSMD2:

26S proteasome non-ATPase regulatory subunit 2

PSMD3:

26S proteasome non-ATPase regulatory subunit 3

PSMD4:

26S proteasome non-ATPase regulatory subunit 4

PSMD6:

26S proteasome non-ATPase regulatory subunit 6

PSMD7:

26S proteasome non-ATPase regulatory subunit 7

PSMD8:

26S proteasome non-ATPase regulatory subunit 8

PSMD9:

26S proteasome non-ATPase regulatory subunit 9

PSME1:

Proteasome activator complex subunit 1

PSME2:

Proteasome activator complex subunit 2

PSME3:

Proteasome activator complex subunit 3

PSME4:

Proteasome activator complex subunit 4

ROC:

Receiver operating characteristic

Rpn10:

26S proteasome non-ATPase regulatory subunit 4

Rpn13:

26S proteasome regulatory subunit RPN13

SEM1:

26S proteasome complex subunit SEM1

ssGSEA:

Single-sample Gene Set Enrichment Analysis

TCA:

Tricarboxylic acid

TCGA:

The Cancer Genome Atlas

TMB:

Tumor mutation burden

TME:

Tumor microenvironment

TNF:

Tumor necrosis factor

UPS:

Ubiquitin-proteasome system

VTCN1:

V-set domain-containing T cell activation inhibitor 1

WGCNA:

Weighted gene co-expression network analysis

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Acknowledgements

The authors acknowledge The Cancer Genome Atlas (TCGA) project organizers as well as all study participants to provide the publicly available TCGA RNA-seq data and clinical data.

Funding

This work was supported by the Shandong Provincial Taishan Scholar Engineering Project Special Funds (NO.tstp20221143 to X.Z.), the Shandong Provincial Natural Science Foundation (ZR2021MH156 to X.Z.; ZR2022QH112 to N.L.), the Shandong First Medical University Talent Introduction Funds (to X.Z.), and China National Nature Scientific Funds (82203592 to N.L.).

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Authors and Affiliations

  1. Medical Science and Technology Innovation Center, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250117, People’s Republic of China

    Jingru Yang, Serge Yannick Ouedraogo, Zhijun Li, Xiaoxia Feng, Zhen Ye, Shu Zheng, Na Li & Xianquan Zhan

  2. Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong, 250117, People’s Republic of China

    Jingjing Wang

  3. School of Basic Medicine, Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong, 250117, People’s Republic of China

    Zhen Ye

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  7. Shu Zheng
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  8. Na Li
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  9. Xianquan Zhan
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Contributions

J.Y. analyzed the data and wrote the manuscript. S.Y.O. edited and critically revised the manuscript. J.W., Z.L., X.F. and Z.Y. participated in partial data analysis. S.Z. carried out partial experiments. X.Z. and N.L. conceived the concept, designed the manuscript, coordinated and critically revised the manuscript, and was responsible for its financial support and the corresponding works. All authors approved the final manuscript.

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Correspondence to Na Li or Xianquan Zhan.

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Yang, J., Ouedraogo, S.Y., Wang, J. et al. Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach. EPMA Journal 15, 67–97 (2024). https://doi.org/10.1007/s13167-024-00352-w

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