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Purpose <p>Lung cancer is the malignant tumor with the highest morbidity and mortality in the world. There is growing evidence that lysosomes are closely associated with tumor proliferation, invasion and the construction of immune microenvironment. Therefore, a lysosome-related signature that can predict the clinical outcomes and assess the efficiency of immunotherapy in lung adenocarcinoma (LUAD) patients becomes a pressing need.</p> Methods <p>In our study, RNA sequencing and clinical data were obtained from the TCGA and GEO databases. Through univariate and multivariate cox regression, we pinpointed LRGs with prognostic potential. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was conducted to construct a prognostic signature named of LRPS. Through the training dataset, we established a lysosomal associated prognostic signature (LRPS) with 13 genes. Then, a nomogram was constructed based on the risk score and clinicopathological characteristics to facilitate the clinical application of the LRPS. Further analyses explored the distribution of model genes in different cell types, the immune microenvironment, tumor mutation burden, and drug susceptibility in different risk groups.</p> Results <p>We developed a prognostic framework of LUAD based on 13 specific genes (DKK1, RHOV, DLGAP5, NTSR1, BCAN, GREB1L, OLAH, ACSM5, SPOCK1, LY6K, MS4A1, SEC14L3, and ELOVL2). KM survival curve revealed that LUAD patients with high-risk had a worse prognosis compared with patients with low-risk. Multicox regression analysis showed that LRPS-based risk score was an independent prognostic factor. Meanwhile, we found that most immune cells were closely linked to the 13 model genes. The high-risk patients was negatively associated with StromalScore, ImmuneScore and ESTIMATEScore according to ssGSEA and CIBERSORT algorithm.</p> Conclusions <p>Collectively, we identified a lysosome-related prognostic signature for LUAD patients, which could serve as a guide for clinicians to develop individualized treatment strategies.</p>

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Identification of a lysosome-related prognostic signature to predict prognosis, tumor microenvironment and therapeutic responses in lung adenocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月1日

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  • Jingze Yan,
  • Zeyuan Liu,
  • Xinchen Sun,
  • Xiaojie Xia

摘要

Purpose

Lung cancer is the malignant tumor with the highest morbidity and mortality in the world. There is growing evidence that lysosomes are closely associated with tumor proliferation, invasion and the construction of immune microenvironment. Therefore, a lysosome-related signature that can predict the clinical outcomes and assess the efficiency of immunotherapy in lung adenocarcinoma (LUAD) patients becomes a pressing need.

Methods

In our study, RNA sequencing and clinical data were obtained from the TCGA and GEO databases. Through univariate and multivariate cox regression, we pinpointed LRGs with prognostic potential. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was conducted to construct a prognostic signature named of LRPS. Through the training dataset, we established a lysosomal associated prognostic signature (LRPS) with 13 genes. Then, a nomogram was constructed based on the risk score and clinicopathological characteristics to facilitate the clinical application of the LRPS. Further analyses explored the distribution of model genes in different cell types, the immune microenvironment, tumor mutation burden, and drug susceptibility in different risk groups.

Results

We developed a prognostic framework of LUAD based on 13 specific genes (DKK1, RHOV, DLGAP5, NTSR1, BCAN, GREB1L, OLAH, ACSM5, SPOCK1, LY6K, MS4A1, SEC14L3, and ELOVL2). KM survival curve revealed that LUAD patients with high-risk had a worse prognosis compared with patients with low-risk. Multicox regression analysis showed that LRPS-based risk score was an independent prognostic factor. Meanwhile, we found that most immune cells were closely linked to the 13 model genes. The high-risk patients was negatively associated with StromalScore, ImmuneScore and ESTIMATEScore according to ssGSEA and CIBERSORT algorithm.

Conclusions

Collectively, we identified a lysosome-related prognostic signature for LUAD patients, which could serve as a guide for clinicians to develop individualized treatment strategies.