Open Access

ARTICLE

The lactylation index predicts the immune microenvironment and prognosis of pan-cancer patients

XUEJIA ZHAI^1,2,3,4,#, JIE LIU^2,3,4,#, JINWEI XIAO^2,3,4, TAO ZHANG^2,3,4, JUN WANG^2,3,4,5, JIANJUN LI^1,*, SHICANG YU^2,3,4,5,*

1 Department of Oncology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
2 Department of Stem Cell and Regenerative Medicine, Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China
3 Key Laboratory of Cancer Immunopathology, Ministry of Education, Chongqing, 400038, China
4 International Joint Research Center for Precision Biotherapy, Ministry of Science and Technology, Chongqing, 400038, China
5 Jin-feng Laboratory, Chongqing, 401329, China

* Corresponding Authors: JIANJUN LI. Email: ; SHICANG YU. Email:

BIOCELL 2024, 48(8), 1223-1239. https://doi.org/10.32604/biocell.2024.050803

Received 18 February 2024; Accepted 29 April 2024; Issue published 02 August 2024

Abstract

Background: Protein lactylation is a new way for the “metabolic waste” lactic acid to perform novel functions. Nevertheless, our understanding of the contribution of protein lactylation to both tumor progression and therapeutic interventions remains imited. The construction of a scoring system for lactylation to predict the prognosis of pan-cancer patients and to evaluate the tumor immune microenvironment (TIME) would improve our understanding of the clinical significance of lactylation. Methods: Consensus clustering analysis of lactylation-related genes was used to cluster 177 pancreatic adenocarcinoma (PAAD) patients. Subsequently, a scoring system was developed using the least absolute shrinkage and selection operator (LASSO) regression. Internal validation and external validation were both conducted to assess and confirm the predictive accuracy of the scoring system. Finally, leucine rich repeat containing 1 (LRRC1), a newly discovered lactylation-related gene, was analyzed in PAAD in vitro. Results: Utilizing the profiles of 332 lactylation-related genes, a total of 177 patients with PAAD were segregated into two distinct groups. LacCluster^high patients had a poorer prognosis than LacCluster^low patients. Through the differential analysis between the LacCluster^high and LacCluster^low groups, we identified additional genes associated with lactylation. These genes were then integrated to construct the LacCluster-enhanced system, which enabled more accurate prognosis prediction for patients with PAAD. Then, a lactylation index containing three genes (LacI-3) was constructed using LASSO regression. This was done to enhance the usability of the LacCluster-enhanced system in the clinic. Compared to those in the LacI-3^high subgroup, patients in the LacI-3^low subgroup exhibited increased expression of immune checkpoint-related genes, more immune cell infiltration, lower tumor mutation burdens, and better prognoses, indicating a “hot tumor” phenotype. Moreover, knocking down the expression of LRRC1, the hub gene in the LacI-3 scoring system, inhibited PAAD cell invasion, migration, and proliferation in vitro. Ultimately, the significance of LacI-3 across cancers was confirmed. Conclusion: Our findings strongly imply that protein lactylation may represent a new approach to diagnosing and treating malignant tumors.

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Keywords

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Supplementary Material

Supplementary Material File

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