Aggressive early-stage lung adenocarcinoma is characterized by epithelial cell plasticity with acquirement of stem-like traits and immune evasion phenotype

Unveiling the origin and functions of diagnostic circulating microRNAs in lung cancer

BACKGROUND

Circulating microRNAs (c-miRs) were shown to be effective biomarkers for lung cancer early detection. However, the understanding of c-miRs origin and their biological functions still remains elusive.

METHODS

We analysed miRNA expression in a large panel of lung cancer (LC) and hematopoietic cell lines (N = 252; CCLE database) coupled with c-miR profile of a large cohort of serum samples (N = 975), from high-risk subjects underwent annual LD-CT for 5 years. Furthermore, we examined intracellular and extracellular miR-29a-3p/223-3p expression profile in lung adenocarcinoma (LUAD) tissues, in matched serum samples and in LC and stromal cell lines. Lastly, through the modulation of expression of selected c-miRs by using mimic (OE) or antisense microRNA (KD), we explored their impact on lung cancer transcriptome and cancer and immune phenotypes.

RESULTS

Here, we investigated the origin of an extensively validated 13 c-miRs signature diagnostics for asymptomatic lung cancer (LC) in high-risk subjects (smokers, >20 packs/y; >50 y old). Overall, we found a mixed origin of these c-miRs, originating both from tumour cells and the tumour microenvironment (TME). Intriguingly, we revealed that circulating miR-29a-3p and miR-223-3p are abundantly released from LC epithelial cells and immune cells, respectively. In particular, we found that miR-223-3p triggered several lung cancer related phenotypes such as invasion, migration and tumour-promoting inflammation.

CONCLUSIONS

Our study highlights a mixed tumour epithelial and stroma-associated origin of LC c-miRs with new evidences on the multifaceted role of miR-223-3p in LC pathogenesis and immune modulation.

Unraveling the relapse-associated landscape and individualized therapy in stage I lung adenocarcinoma based on immune and mitochondrial metabolism hallmarks via multi-omics analyses

Lung adenocarcinoma (LUAD) is characterized by significant molecular heterogeneity and high recurrence rate even among stage I patients. There is an urgent quest for reliable biomarkers to recognize early-stage patients at high risk and guide potential treatment. Considering the pivotal role of immune and mitochondrial metabolic hallmarks in tumor initiation and progression, we rigorously included four independent cohorts of stage I LUAD patients with or without relapse. A consensus immune and mitochondrial metabolism genes-related signature (IMMS) is then constructed via 101 machine-learning combinations. IMMS classified all patients into high- and low-risk groups and exhibited a leading predicting accuracy compared with 70 previously published signatures. Subsequently, comprehensive analysis of the multi-omics data discovered elevated genomic heterogeneity, cancer stemness, metabolic reprogramming, immune escape, and tolerance to immune therapy in the high-risk group, which promotes the survival and proliferation of tumor cells. After the analysis of multiple drug databases, mitoxantrone is considered a candidate drug for stage I high-risk LUAD patients. The research of single-cell data further supported the tight association between IMMS and tumor cell characteristics. Overall, our study developed a novel signature and emphasized the role of immune escape and metabolic reprogramming hallmarks in recurrence, offering valuable insights into clinical prognosis, molecular mechanism, and individualized therapy for stage I LUAD patients.

Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism

Objective

This study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model.

Methods

From The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model.

Results

Multivariable cox analysis (HR = 1.488, 95 % CI 1.012-2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008-2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P < 0.001). The high PS group exhibited higher TMB values (P < 0.001).

Conclusions

The predictive model constructed based on pathomics features can forecast the mRNAsi and survival risk of LUAD. This model holds promise to aid clinical practitioners in identifying high-risk patients and devising more optimized treatment plans for patients by jointly employing therapeutic strategies targeting cancer stem cells (CSCs).

The circadian clock circuitry deconvolutes colorectal cancer and lung adenocarcinoma heterogeneity in a dynamic time-related framework

Increasing evidence imputes cancer progression and resistance to therapy to intra-tumor molecular heterogeneity set off by cancer cell plasticity. Re-activation of developmental programs strictly linked to epithelial-to-mesenchymal transition and gaining of stem cells properties are crucial in this setting. Many biological processes involved in cancer onset and progression show rhythmic fluctuations driven by the circadian clock circuitry. Novel cancer patient stratification tools taking into account the temporal dimension of these biological processes are definitely needed. Lung cancer and colorectal cancer (CRC) are the leading causes of cancer death worldwide. Here, by developing an innovative computational approach we named Phase-Finder, we show that the molecular heterogeneity characterizing the two deadliest cancers, CRC and lung adenocarcinoma (LUAD), rather than a merely stochastic event is the readout of specific cancer molecular states which correlate with time-qualified patterns of gene expression. We performed time-course transcriptome analysis of CRC and LUAD cell lines and upon computing circadian genes expression-based correlation matrices we derived pseudo-time points to infer time-qualified patterns in the transcriptomic analysis of real-world data (RWD) from large cohorts of CRC and LUAD patients. Our temporal classification of CRC and LUAD cohorts was able to effectively render time-specific patterns in cancer phenotype switching determining dynamical distribution of molecular subtypes impacting patient prognosis.

Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients.

METHODS

Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins.

FINDINGS

We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments.

INTERPRETATION

We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

A Signature Constructed Based on the Integrin Family Predicts Prognosis and Correlates with the Tumor Microenvironment of Patients with Lung Adenocarcinoma

As an important element in regulating the tumor microenvironment (TME), integrin plays a key role in tumor progression. This study aimed to establish prognostic signatures to predict the overall survival and identify the immune landscape of patients with lung adenocarcinoma based on integrins. The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and Gene Expression Omnibus datasets were used to obtain information on mRNA levels and clinical factors (GSE72094). The least absolute shrinkage and selection operator (LASSO) model was used to create a prediction model that included six integrin genes. The nomogram, risk score, and time-dependent receiver operating characteristic analysis all revealed that the signatures had a good prognostic value. The gene signatures may be linked to carcinogenesis and TME, according to a gene set enrichment analysis. The immunological and stromal scores were computed using the ESTIMATE algorithm, and the data revealed, the low-risk group had a higher score. We discovered that the B lymphocytes, plasma, CD4+ T, dendritic, and mast cells were much higher in the group with low-risk using the CiberSort. Inflammatory processes and several HLA family genes were upregulated in the low-risk group. The low-risk group with a better prognosis is more sensitive to immune checkpoint inhibitor medication, according to immunophenoscore (IPS) research. We found that the patients in the high-risk group were more susceptible to chemotherapy than other group patients, according to the prophetic algorithm. The gene signatures could accurately predict the prognosis, identify the immune status of patients with lung adenocarcinoma, and provide guidance for therapy.

Immune infiltration landscape on prognosis and therapeutic response and relevant epigenetic and transcriptomic mechanisms in lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype, but its immune infiltration features are not comprehensively understood. To address the issue, the present study was initiated to describe the immune infiltrations across LUAD from cellular compositional, functional, and mechanism perspectives.

Methods

We adopted five LUAD datasets (GSE32863, GSE43458, GSE75037, TCGA-LUAD, and GSE72094). Differentially expressed genes between LUAD and controls were selected for co-expression network analysis. Risky immune cell types were determined for classifying LUAD patients as diverse subtypes, followed by a comparison of antitumor immunity and therapeutic response between subtypes. Then, LUAD- and subtype-related key module genes affected by DNA methylation were determined for quantifying a scoring scheme. EXO1 was chosen for functional analysis via in vitro assays.

Results

Two immune cell infiltration-based subtypes (C1 and C2) were established across LUAD, with poorer prognostic outcomes and lower infiltration of immune cell types in C1. Additionally, C1 presented higher responses to immune checkpoint blockade and targeted agents (JNK inhibitor VIII, BI-D1870, RO-3306, etc.). The scoring system (comprising GAPDH, EXO1, FYN, CFTR, and KLF4) possessed higher accuracy in estimating patients’ prognostic outcomes. EXO1 upregulation contributed to the growth, migration, and invasion of LUAD cells. In addition, EXO1 facilitated PD-L1 and sPD-L1 expression in LUAD cells.

Conclusion

Altogether, our findings offer a comprehensive understanding of the immune infiltration landscape on prognosis and therapeutic response of LUAD as well as unveil potential epigenetic and transcriptomic mechanisms, which might assist personalized treatment.