bims-meluca 2025-06-01 papers

Hum Cell. 2025 May 24. 38(4): 106

KDELR3 is transcriptionally activated by FOXM1 and accelerates lung adenocarcinoma growth and metastasis via inhibiting endoplasmic reticulum stress-induced cell apoptosis.

Cheng Wang, Zhaoxuan Wang, Shiqing Wang, Lin Jing, Chundong Gu.

Lung cancer is still considered to be the leading cause of cancer-related death worldwide, and lung adenocarcinoma (LUAD) is the most common kind. KDEL Endoplasmic Reticulum Protein Retention Receptor 3 (KDELR3) is a critical regulator of the endoplasmic reticulum (ER) stress and the followed unfolded protein response (UPR) process, which are critical in tumor development. However, the role of KDELR3 in LUAD tumor progression remains poorly understood. In this work, we demonstrated that KDELR3 is significantly upregulated in LUAD tumor tissues and cell lines. Suppression of KDELR3 promoted the phosphorylation level of UPR-related pathways, PERK, and EIF2α in LUAD cell lines. The downregulation of KDELR3 promoted ER stress-induced cell apoptosis, decreased the protein expression of Bcl-2, and increased the protein expression of Bax in LUAD cells. Moreover, the knockdown of KDELR3 inhibits LUAD cell invasion. In vivo animal experiments confirmed that the inhibition of KDELR3 suppresses LUAD tumor growth and metastasis. Mechanistic studies showed that transcription factor FOXM1 may serve as an upstream factor of KDELR3. The upregulation of FOXM1 increased the transcriptional activity of KDELR3. Further results illustrated that FOXM1 directly binds to the promoter of KDELR3, thus upregulating its expression. Finally, rescue experiments demonstrated that FOXM1 inhibition-induced cell apoptosis and invasion could be reversed by KDELR3 overexpression. Overall, our findings indicated that KDELR3 is transcriptionally upregulated by FOXM1 and accelerates tumor growth and lung metastasis in LUAD by inhibiting ER stress-induced cell apoptosis.

Keywords: Endoplasmic reticulum stress; FOXM1; KDELR3; Lung adenocarcinoma; Lung metastasis

DOI: https://doi.org/10.1007/s13577-025-01238-3

Clin Chim Acta. 2025 May 27. pii: S0009-8981(25)00278-5. [Epub ahead of print] 120399

Integrated metabolomics and lipidomics reveal plasma markers of non-small cell lung cancer with brain metastasis.

Xudong Jin, Jinhua Rong, Shen Peng, You Xiao, Ruting Wang, Yixuan Gu, Wenhao Ji, Xiancong Huang, Weimin Mao.

Brain metastasis (BM) is a fatal complication of non-small cell lung cancer (NSCLC). The lack of non-invasion methods for early diagnosis and risk stratification is the major cause for the poor prognosis of NSCLC with BM. The metabolic state of BM has a significant change characterized with high reactive oxygen species accumulation and the subsequent antioxidant metabolic response. We sought to screen plasma markers based on metabolomics and lipidomics for the early diagnosis and prognostic evaluation of NSCLC patients with BM. Plasma samples collected from 48 NSCLC patients with BM and 49 gender- and age- matched primary lung cancer (PLC) patients were randomly divided into train set and test set, then analyzations of metabolomics and lipidomics were performed using liquid chromatography-tandem mass spectrometry (LC-MS). Differential metabolites and lipids were discovered from the train set. A diagnostic biomarker panel was constructed using logistic regression. The test set were utilized to validate the accuracy of the diagnostic model. Furthermore, a risk score was established to stratify risk for NSCLC patients. There were 34 differential metabolites and 35 differential lipids annotated in the train set. The diagnostic biomarker panel consisting of homocysteine, ascorbic acid, LPC (22:0) and LPC (20:0) is able to discriminate BM from PLC with an excellent performance. The risk score based on protocatechuic acid and LPC (20:0) could efficiently stratify risk for NSCLC patients with BM. Our study reports plasma biomarkers and predictive models for the early diagnosis and prognostic evaluation of NSCLC patients with BM.

Keywords: Brain metastasis; Lipidomics; Metabolomics; Non-small cell lung cancer; Predictive model

DOI: https://doi.org/10.1016/j.cca.2025.120399

Nat Cancer. 2025 May 26.

Skeletal muscle endothelial dysfunction through the activin A-PGC1α axis drives progression of cancer cachexia.

Cachexia is the wasting of skeletal muscle in cancer and is a major complication that impacts a person's quality of life. We hypothesized that cachexia is mediated by dysfunction of the vascular system, which is essential for maintaining perfusion and tempering inappropriate immune responses. Using transparent tissue topography, we discovered that loss of muscle vascular density precedes muscle wasting in multiple complementary tumor models, including pancreatic adenocarcinoma, colon carcinoma, lung adenocarcinoma and melanoma models. We also observed that persons suffering from cancer cachexia exhibit substantial loss of muscle vascular density. As tumors progress, increased circulating activin A remotely suppresses the expression of peroxisome proliferator-activated receptor-γ coactivator 1α (PGC1α) in the muscle endothelium, thus inducing vascular leakage. Restoring endothelial PGC1α activity preserved vascular density and muscle mass in tumor-bearing mice. Our study suggests that restoring muscle endothelial function could be a valuable therapeutic approach for cancer cachexia.

DOI: https://doi.org/10.1038/s43018-025-00975-6

Cancers (Basel). 2025 May 16. pii: 1681. [Epub ahead of print]17(10):

High KYNU Expression Is Associated with Poor Prognosis, KEAP1/STK11 Mutations, and Immunosuppressive Metabolism in Patient-Derived but Not Murine Lung Adenocarcinomas.

Ling Cai, Thomas J Rogers, Reza Mousavi Jafarabad, Hieu Vu, Chendong Yang, Nicole Novaresi, Ana Galán-Cobo, Luc Girard, Edwin J Ostrin, Johannes F Fahrmann, Jiyeon Kim, John V Heymach, Kathryn A O'Donnell, Guanghua Xiao, Yang Xie, Ralph J DeBerardinis, John D Minna.

Background/Objectives: We aimed to discover genes with bimodal expression linked to patient outcomes, to reveal underlying oncogenotypes and identify new therapeutic insights in lung adenocarcinoma (LUAD). Methods: We performed meta-analysis to screen LUAD datasets for prognostic genes with bimodal expression patterns. Kynureninase (KYNU), a key enzyme in tryptophan catabolism, emerged as a top candidate. We then examined its relationship with LUAD mutations, metabolic alterations, immune microenvironment states, and expression patterns in human and mouse models using bulk and single-cell transcriptomics, metabolomics, and preclinical model datasets. Pan-cancer prognostic associations were also assessed. Results: Model-based clustering of KYNU expression outperformed median-based dichotomization in prognostic accuracy. KYNU was elevated in tumors with KEAP1 and STK11 co-mutations but remained a strong independent prognostic marker. Metabolomic analysis showed that KYNU-high tumors had increased anthranilic acid, a catalytic product, while maintaining stable kynurenine levels, suggesting a compensatory mechanism sustaining immunosuppressive signaling. Single-cell and bulk data showed KYNU expression was cancer cell-intrinsic in immune-cold tumors and myeloid-derived in immune-infiltrated tumors. In murine LUAD models, Kynu expression was predominantly immune-derived and uncoupled from Nrf2/Lkb1 signaling, indicating poor model fidelity. KYNU's prognostic associations extended across cancer types, with poor outcomes in pancreatic and kidney cancers but favorable outcomes in melanoma, underscoring the need for lineage-specific considerations in therapy development. Conclusions:KYNU is a robust prognostic biomarker and potential immunometabolic target in LUAD, especially in STK11 and KEAP1 co-mutated tumors. Its cancer cell-intrinsic expression and immunosuppressive metabolic phenotype offer translational potential, though species-specific expression patterns pose challenges for preclinical modeling.

Keywords: KEAP1; KYNU; NAD metabolism; STK11; immune suppression; kynureninase; kynurenine pathway; lung adenocarcinoma; mouse model limitations; prognostic biomarker; tryptophan catabolism

DOI: https://doi.org/10.3390/cancers17101681

Biology (Basel). 2025 May 19. pii: 566. [Epub ahead of print]14(5):

Integrated Bioinformatics Analysis and Cellular Experimental Validation Identify Lipoprotein Lipase Gene as a Novel Biomarker for Tumorigenesis and Prognosis in Lung Adenocarcinoma.

Wanwan He, Meilian Wei, Yan Huang, Junsen Qin, Meng Liu, Na Liu, Yanli He, Chuanbing Chen, Yali Huang, Heng Yin, Ren Zhang.

Lung adenocarcinoma (LUAD) is one of the leading causes of death worldwide, and thus, more biomarker and therapeutic targets need to be explored. Herein, we aimed to explore new biomarkers of LUAD by integrating bioinformatics analysis with cell experiments. We firstly identified 266 druggable genes that were significantly differentially expressed between LUAD tissues and adjacent normal lung tissues. Among these genes, SMR analysis with p-value correction suggested that declining lipoprotein lipase (LPL) levels may be causally associated with an elevated risk of LUAD, which was corroborated by co-localization analysis. Analyses of clinical data showed that LPL in lung cancer tissues has considerable diagnostic value for LUAD, and elevated LPL levels were positively associated with improved patient survival outcomes. Cell experiments with an LPL activator proved these findings; the activator inhibited the proliferation and migration of lung cancer cells. Next, we found that LPL promoted the infiltration of immune cells such as DCs, IDCs, and macrophages in LUAD by mononuclear sequencing analysis and TIMER2.0. Meanwhile, patients with low levels of LPL expression demonstrated superior immunotherapeutic responses to anti-PD-1 therapy. We conclude that LPL acts as a diagnostic and prognostic marker for LUAD.

Keywords: IncuCyte; SMR; lipoprotein lipase; lung adenocarcinoma; targeted therapeutic

DOI: https://doi.org/10.3390/biology14050566

Metabolites. 2025 May 20. pii: 340. [Epub ahead of print]15(5):

Characterizing Plasma-Based Metabolomic Signatures for Metastasis in Non-Small Cell Lung Cancer.

Manlu Liu, Yanlong Zhu, Sean J McIlwain, Haotian Deng, Allan R Brasier, Ying Ge, Michelle E Kimple, Andrew M Baschnagel.

Background/Objectives: The current staging of non-small cell lung cancer (NSCLC) relies on conventional imaging, which lacks the sensitivity to detect micrometastatic disease. The functional assessment of NSCLC progression may provide independent information to enhance the prediction of metastatic risk. The objective of this study was to determine if we could identify a metabolomic signature predictive of metastasis in patients with NSCLC treated with definitive radiation. Methods: Plasma samples were collected prospectively from patients enrolled in a clinical trial with non-metastatic NSCLC treated with definitive radiation. Metabolites were extracted, and mass spectrometry-based analysis was performed using a flow injection electrospray (FIE)-Fourier transform ion cyclotron resonance (FTICR) mass spectrometry (MS) method. Early metastasis was defined as metastasis within 1 year of radiation treatment. Results: The study cohort included 28 patients. FIE-FITCR produced highly reproducible profiles in technical replicates. A total of 51 metabolic features were identified to be different in patients with early metastasis compared to patients without early metastasis (all adjusted p-values < 0.05, Welch's t-test), including glycerophospholipids, sphingolipids, and fatty acyls. In the follow-up samples collected after the initiation of chemotherapy and radiation treatment, a total of 174 metabolic features were significantly altered in patients who developed early metastasis compared to those who did not. Conclusions: We identified several distinct changes in the metabolic profiles of patients with NSCLC who developed metastatic disease within 1 year of definitive radiation. These findings highlight the potential of metabolomic profiling as a predictive tool for assessing metastatic risk in NSCLC.

Keywords: biomarkers; clinical study; lung cancer; metabolomics

DOI: https://doi.org/10.3390/metabo15050340