Integrated analysis of patients with KEAP1/NFE2L2/CUL3 mutations in lung adenocarcinomas

A comparative analysis indicates SLC7A11 expression regulate the prognostic value of KEAP1-NFE2L2-CUL3 mutations in human uterine corpus endometrial carcinoma

Uterine corpus endometrial cancer (UCEC) is a third most common malignancy in women with a poor prognosis in advanced stages. In this study, we performed an integrated comparative analysis of exome and transcriptome data from The Cancer Genome Atlas (TCGA) of Lung Adenocarcinoma (LUAD), and UCEC patients. Our multi-omics analysis shows that the UCEC patients carrying mutations in the KEAP1-NFE2L2-CUL3 genes were associated with better progression-free survival (PFS), whereas the KEAP1-NFE2L2-CUL3 mutation in LUAD showed poor outcomes. Functional annotations and correlative expression studies show that genes, particularly GCLC and GCLM related to glutathione synthesis are expressed at lower levels in the KEAP1-NFE2L2-CUL3 mutant UCEC compared to LUAD. This events result in glutathione deficiency and it may compromise to combat intracellular reactive oxygen species (ROS). However, the expression of genes involved in the glutathione recycling process was not affected. On the other hand, cellular import of cystine is high due to increased SLC7A11 expression in UCEC. Because glutathione synthesis is impaired, the unconverted cysteine accumulates in cells, leading to di-sulfite stress. Apart from NRF2, ARID1A is one of the positive regulators of SLC7A11. In support, UCEC patients with co-occurrence of KEAP1-NFE2L2-CUL3 and ARID1A mutation shows significantly decreased PFS with decline of SLC7A11 expression as compared to patients carrying only KEAP1-NFE2L2-CUL3 mutation. Thus, we hypothesize that the KEAP1-NFE2L2-CUL3 mutation in UCEC leads to uncontrollable ROS with di-sulfite stress, reflecting a favorable clinical outcome.

JunD-miR494-CUL3 axis promotes radioresistance and metastasis by facilitating EMT and restraining PD-L1 degradation in esophageal squamous cell carcinoma

Therapy resistance and metastatic progression jointly determine the fatal outcome of cancer, therefore, elucidating their crosstalk may provide new opportunities to improve therapeutic efficacy and prevent recurrence and metastasis in esophageal squamous cell carcinoma (ESCC). Here, we have established radioresistant ESCC cells with the remarkable metastatic capacity, and identified miR-494-3p (miR494) as a radioresistant activator. Mechanistically, we demonstrated that cullin 3 (CUL3) is a direct target of miR494, which is transcriptionally regulated by JunD, and highlighted that JunD-miR494-CUL3 axis promotes radioresistance and metastasis by facilitating epithelial-mesenchymal transition (EMT) and restraining programmed cell death 1 ligand 1 (PD-L1) degradation. In clinical specimens, miR494 is significantly up-regulated and positively associated with T stage and lymph node metastasis in ESCC tissues and serum. Notably, patients with higher serum miR494 expression have poor prognosis, and patients with higher CUL3 expression have more conventional dendritic cells (cDCs) and plasmacytoid DCs (pDCs), less cancer-associated fibroblasts (CAF2/4), and tumor endothelial cells (TEC2/3) infiltration than patients with lower CUL3 expression, suggesting that CUL3 may be involved in tumor microenvironment (TME). Overall, miR494 may serve as a potential prognostic predictor and therapeutic target, providing a promising strategy for ESCC treatment.

Molecular classification reveals the sensitivity of lung adenocarcinoma to radiotherapy and immunotherapy: multi-omics clustering based on similarity network fusion

BACKGROUND

Due to individual differences in tumors and immune systems, the response rate to immunotherapy is low in lung adenocarcinoma (LUAD) patients. Combinations with other therapeutic strategies improve the efficacy of immunotherapy in LUAD patients. Although radioimmunotherapy has been demonstrated to effectively suppress tumors, the underlying mechanisms still need to be investigated.

METHODS

Total RNA from LUAD cells was sequenced before and after radiotherapy to identify differentially expressed radiation-associated genes. The similarity network fusion (SNF) algorithm was applied for molecular classification based on radiation-related genes, immune-related genes, methylation data, and somatic mutation data. The changes in gene expression, prognosis, immune cell infiltration, radiosensitivity, chemosensitivity, and sensitivity to immunotherapy were assessed for each subtype.

RESULTS

We used the SNF algorithm and multi-omics data to divide TCGA-LUAD patients into three subtypes. Patients with the CS3 subtype had the best prognosis, while those with the CS1 and CS2 subtypes had poorer prognoses. Among the strains tested, CS2 exhibited the most elevated immune cell infiltration and expression of immune checkpoint genes, while CS1 exhibited the least. Patients in the CS2 subgroup were more likely to respond to PD-1 immunotherapy. The CS2 patients were most sensitive to docetaxel and cisplatin, while the CS1 patients were most sensitive to paclitaxel. Experimental validation of signature genes in the CS2 subtype showed that inhibiting the expression of RHCG and TRPA1 could enhance the sensitivity of lung cancer cells to radiation.

CONCLUSIONS

In summary, this study identified a risk classifier based on multi-omics data that can guide treatment selection for LUAD patients.

Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies

BACKGROUND

Molecular classification of lung adenocarcinoma (LUAD) based on transcriptomic features has been widely studied. The complementarity of data obtained from multilayer molecular biology could help the LUAD classification via combining multi-omics information.

METHODS

We successfully divided samples from the The Cancer Genome Atlas (TCGA) (n=437) into four subtypes (CS1, CS2, CS3 and CS4) by 10 comprehensive multi-omics clustering methods in the "movics" R package. Meanwhile, external validation sets from different sequencing technologies proved the robustness of the grouping model. The relationship between subtypes, prognosis, molecular features, tumor microenvironment and response to first-line therapy was further analyzed. Next we used univariate Cox regression analysis and Lasso regression analysis to explore the application of biomarkers in clinical prognosis and constructed a prognostic model.

RESULTS

CS1 showed the worst overall survival (OS) among all four clusters, possibly related to its poor immune infiltration, higher tumor mutation and worse chromosomal stability. Patients in different subtypes differed significantly in cancer stem cell characteristics, activation of cancer-related pathways, sensitivity to chemotherapy and immunotherapy. The prognostic model showed good predictive performance. The 1-, 2- and 3-year areas under the curve of risk score were 0.779, 0.742 and 0.678, respectively. Seven genes (DKK1, TSPAN7, ID1, DLGAP5, HHIPL2, CD40 and SEMA3C) used to build the model may be potential therapeutic targets for LUAD.

CONCLUSIONS

Four LUAD subtypes with different molecular characteristics and clinical implications were identified successfully through bioinformatic analysis. Our results may contribute to precision medicine and inform the development of rational clinical strategies for targeted and immune therapies.

A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma

Background

The tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets.

Methods

The scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways' enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets.

Results

We clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate.

Conclusion

Our observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.

Identification of Two Subtypes and Prognostic Characteristics of Lung Adenocarcinoma Based on Pentose Phosphate Metabolic Pathway-Related Long Non-coding RNAs

This study analyzed the differences in subtypes and characteristics of advanced lung adenocarcinoma (LUAD) patients based on the pentose phosphate metabolic pathway-related long non-coding RNAs (lncRNAs), along with their potential regulatory mechanisms. Using the expression profiling and corresponding clinical information of LUAD patients from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Differential pathway scores between normal and tumor samples from TCGA were identified by rank-sum tests. Pearson correlation coefficients between pentose phosphate scores of the pentose phosphate samples and lncRNAs of the corresponding datasets were calculated. Next, the clusterProfiler software package was used for functional annotation. Clustering of pentose phosphate-related lncRNAs from LUAD samples categorized two molecular subtypes (C1, and C2). C1 was associated with a lower pentose phosphate score and a good prognosis; the C2 showed a higher pentose phosphate score and was related to poorer prognoses. The C2 was markedly associated with energy metabolic pathways. The expression of most immune cells were markedly higher in C1 subtype. Some crucial immune checkpoints, including CTLA4, CD274, and CD47, were also significantly upregulated in C1 subtype, leading to a higher score of clinical effect on the C1 subtype. Finally, one TF, BACH1, was found to be significantly upregulated in C1 subtypes; the pathways activated by this TF may be associated with tumor progression and poor prognoses. LUAD typing based on pentose phosphate metabolic pathway-related lncRNAs was confirmed. Differences in characteristics between C1 and C2 subtypes improved the current LUAD detection and treatment.