Identification of lysosomal genes associated with prognosis in lung adenocarcinoma

Downregulation of DIP2B as a prognostic marker inhibited cancer proliferation and migration and was associated with immune infiltration in lung adenocarcinoma via CCND1 and MMP2

Background

DIP2B is related to cancer progression. This study investigated the roles and pathways of DIP2B in lung adenocarcinoma (LUAD).

Methods

DIP2B expression and the relationship between survival time of cancer patients and DIP2B expression were analyzed. The relationship between DIP2B expression and survival time in LUAD patients was evaluated by a meta-analysis. Cox and survival analyses were used to evaluate the prognostic factors and construct a prognostic nomogram. The mechanisms and effects of DIP2B and the relationship between DIP2B expression and the immune microenvironment were investigated using bioinformatics, CCK-8, western blotting, and transwell experiments.

Results

DIP2B was overexpressed in LUAD tissues. DIP2B overexpression was associated with shorter prognosis and was an unfavorable risk factor for prognosis in LUAD patients. DIP2B co-expressed genes were involved in cell division, DNA repair, cell cycle, and others. Inhibition of DIP2B expression could downregulate the proliferation, migration, and invasion of LUAD A549 and H1299 cells, which was related to the decrease in CCND1 and MMP2 protein expression. BRCA1 overexpression was associated with short prognosis, and the nomogram formed by DIP2B and BRCA1 was associated with a poor prognosis in LUAD patients. DIP2B expression correlated with immune cells (such as CD8 T cells, Tcm, and iDCs) and cell markers.

Conclusion

DIP2B is a potential biomarker of poor prognosis and the immune microenvironment in LUAD. Inhibition of DIP2B expression downregulated cancer cell proliferation, migration, and invasion, which might be related to the decrease in CCND1 and MMP2 protein expression. DIP2B-related nomograms might be useful tools for predicting the prognosis of LUAD patients.

Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma

BACKGROUND

Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures.

METHODS

In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells.

RESULTS

Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients.

CONCLUSIONS

ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.

Low expression of lysosome-related genes KCNE1, NPC2, and SFTPD promote cancer cell proliferation and tumor associated M2 macrophage polarization in lung adenocarcinoma

Background

Recent research has shown that lysosomes play a critical role in the onset and progression of malignancy by regulating tumor cell death through several mechanisms. Nevertheless, the involvement of lysosome-associated genes (LSAGs) in lung adenocarcinoma (LUAD) is still not well understood.

Methods

LSAGs were identified in malignant lung epithelial cells, as well as biologically and functionally annotated by the comprehensive integration of single-cell and bulk RNA-sequencing data. Prognostic characterization of LSAGs was established, of which the accuracy and reliability were assessed by one-way Cox and LASSO regression. Correlations between LSAG properties and immune cell infiltration, chemotherapy, and immunotherapy were analyzed by integrated omics data. Finally, we characterized the expression of three LSAGs (KCNE1, NPC2, and SFTPD) in malignant lung epithelium and assessed their impact on tumor malignancy related phenotypes.

Results

We identified 18 LSAGs associated with prognosis, of which 3 LSAGs were used to construct prognostic models. High-risk patients had worse survival and the model predicted it better than other clinical indicators. Based on the functional enrichment analyses, LSAGs were associated with binding and molecular activity functions, inhibition of DNA damage repair and tumor growth, IL7 signaling pathway, and glycolysis. M0 macrophages and M1 macrophages were substantially enriched in high-risk patients. Conversely, there was a considerable enrichment of resting dendritic cells and M2 macrophages in patients at low risk. We also found that risk scores predicted the outcome of immunotherapy. In vitro, we found that KCNE1, NPC2, and SFTPD were lowly expressed in malignant epithelial cells and patients with low expression of KCNE1, NPC2, and SFTPD had a higher percentage of M2 macrophage infiltration. Overexpression of KCNE1, NPC2, and SFTPD suppressed the proliferation and invasion of malignant cells, and M0 macrophages remarkably reduced M2 macrophage polarization and cellular secretion of pro-tumor cytokines.

Conclusions

We used three LASGs-KCNE1, NPC2, and SFTPD-to develop and validate a predictive signature for LUAD patients. Furthermore, we found that low expression of KCNE1, NPC2, and SFTPD promotes lung cancer cell proliferation and invasion and M2 macrophage polarization. Our study may provide fresh perspectives for customized immunotherapy.

LINC01559 promotes lung adenocarcinoma metastasis by disrupting the ubiquitination of vimentin

BACKGROUND

Distant metastasis is the major cause of lung adenocarcinoma (LUAD)-associated mortality. However, molecular mechanisms involved in LUAD metastasis remain to be fully understood. While the role of long non-coding RNAs (lncRNAs) in cancer development, progression, and treatment resistance is being increasingly appreciated, the list of dysregulated lncRNAs that contribute to LUAD pathogenesis is also rapidly expanding.

METHODS

Bioinformatics analysis was conducted to interrogate publicly available LUAD datasets. In situ hybridization and qRT-PCR assays were used to test lncRNA expression in human LUAD tissues and cell lines, respectively. Wound healing as well as transwell migration and invasion assays were employed to examine LUAD cell migration and invasion in vitro. LUAD metastasis was examined using mouse models in vivo. RNA pulldown and RNA immunoprecipitation were carried out to test RNA-protein associations. Cycloheximide-chase assays were performed to monitor protein turnover rates and Western blotting was employed to test protein expression.

RESULTS

The expression of the lncRNA LINC01559 was commonly upregulated in LUADs, in particular, in those with distant metastasis. High LINC01559 expression was associated with poor outcome of LUAD patients and was potentially an independent prognostic factor. Knockdown of LINC01559 diminished the potential of LUAD cell migration and invasion in vitro and reduced the formation of LUAD metastatic lesions in vivo. Mechanistically, LINC01559 binds to vimentin and prevents its ubiquitination and proteasomal degradation, leading to promotion of LUAD cell migration, invasion, and metastasis.

CONCLUSION

LINC01559 plays an important role in LUAD metastasis through stabilizing vimentin. The expression of LINC01559 is potentially an independent prognostic factor of LUAD patients, and LINC01559 targeting may represent a novel avenue for the treatment of late-stage LUAD.

Development and validation of a DNA damage repair-related gene-based prediction model for the prognosis of lung adenocarcinoma

Background

Lung cancer is the leading cause of morbidity and mortality among all cancer types, with lung adenocarcinoma (LUAD) being the most prevalent subtype. DNA damage repair (DDR)-related genes are closely associated with cancer progression and treatment, with emerging evidence highlighting their correlation with tumor development. However, the relationship between LUAD prognosis and DDR-related genes remains unclear.

Methods

RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The GSE31210 dataset, utilized for external validation, was retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed DDR genes were identified, and a DDR-related prognostic model was established and validated using Kaplan-Meier (KM) survival analysis, time-dependent receiver operating characteristic (ROC) curves, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, and immune cell infiltration. A P value of less than 0.05 was considered statistically significant.

Results

A total of 514 patients with LUAD from TCGA database were divided into distinct subtypes to characterize the diversity within the DDR pathway. DDR-activated and DDR-suppressed subgroups showed distinct clinical characteristics, molecular characteristics, and immune profiles. Nine genes were identified as hub DDR-related genes, including CASP14, DKK1, ECT2, FLNC, HMMR, IGFBP1, KRT6A, TYMS, and FCER2. By using the expression levels of these selected genes, the corresponding risk scores for each sample was predicted. In the training group, KM survival analysis revealed that the high-risk group exhibited significantly diminished overall survival (OS) [hazard ratio (HR) =3.341, P=1.38e-08]. The corresponding area under the curve (AUC) values for the 1-year follow-up periods was 0.767, respectively. Upon validation in the external cohort, patients with higher risk scores manifested significantly reduced OS (HR =2.372, P=1.87e-03). The AUC values of the ROC curves for the 1-year OS in the validation cohort was 0.87, respectively. Moreover, advanced DDR risk score was correlated with increased TMB scores, a heightened frequency of TP53 mutations, an increased abundance of cancer-testicular antigens (CTAs), and a lower tumor immune dysfunction and exclusion (TIDE) score in patients with LUAD (P<0.05).

Conclusions

A nine-gene risk signature associated with DDR in LUAD was effectively developed, demonstrating its potential as a robust and reliable classification tool for clinical practice. This model exhibited the capability to accurately predict the prognosis and survival outcomes of LUAD patients.

Establishment of a lysosome-related prognostic signature in breast cancer to predict immune infiltration and therapy response

Background

Lysosomes are instrumental in intracellular degradation and recycling, with their functional alterations holding significance in tumor growth. Nevertheless, the precise role of lysosome-related genes (LRGs) in breast cancer (BC) remains elucidated. This study aimed to establish a prognostic model for BC based on LRGs.

Methods

Employing The Cancer Genome Atlas (TCGA) BC cohort as a training dataset, this study identified differentially expressed lysosome-related genes (DLRGs) through intersecting LRGs with differential expression genes (DEGs) between tumor and normal samples. A prognostic model of BC was subsequently developed using Cox regression analysis and validated within two Gene Expression Omnibus (GEO) external validation sets. Further analyses explored functional pathways, the immune microenvironment, immunotherapeutic responses, and sensitivity to chemotherapeutic drugs in different risk groups. Additionally, the mRNA and protein expression levels of genes within the risk model were examined by utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. Clinical tissue specimens obtained from patients were gathered to validate the expression of the model genes via Real-Time Polymerase Chain Reaction (RT-PCR).

Results

We developed a risk model of BC based on five specific genes (ATP6AP1, SLC7A5, EPDR1, SDC1, and PIGR). The model was validated for overall survival (OS) in two GEO validation sets (p=0.00034 for GSE20685 and p=0.0095 for GSE58812). In addition, the nomogram incorporating clinical factors showed better predictive performance. Compared to the low-risk group, the high-risk group had a higher level of certain immune cell infiltration, including regulatory T cells (Tregs) and type 2 T helper cells (Th2). The high-risk patients appeared to respond less well to general immunotherapy and chemotherapeutic drugs, according to the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and drug sensitivity scores. The RT-PCR results validated the expression trends of some prognostic-related genes in agreement with the previous differential expression analysis.

Conclusion

Our innovative lysosome-associated signature can predict the prognosis for BC patients, offering insights for guiding subsequent immunotherapeutic and chemotherapeutic interventions. Furthermore, it has the potential to provide a scientific foundation for identifying prospective therapeutic targets.