Molecular charcterization of lung adenocarcinoma combining whole exome sequencing, copy number analysis and gene expression profiling

tRNA-derived fragments: mechanism of gene regulation and clinical application in lung cancer

Lung cancer, being the most widespread and lethal form of cancer globally, has a high incidence and mortality rate primarily attributed to challenges associated with early detection, extensive metastasis, and frequent recurrence. In the context of lung cancer development, noncoding RNA molecules have a crucial role in governing gene expression and protein synthesis. Specifically, tRNA-derived fragments (tRFs), a subset of noncoding RNAs, exert significant biological influences on cancer progression, encompassing transcription and translation processes as well as epigenetic regulation. This article primarily examines the mechanisms by which tRFs modulate gene expression and contribute to tumorigenesis in lung cancer. Furthermore, we provide a comprehensive overview of the current bioinformatics analysis of tRFs in lung cancer, with the objective of offering a systematic and efficient approach for studying the expression profiling, functional enrichment, and molecular mechanisms of tRFs in this disease. Finally, we discuss the clinical significance and potential avenues for future research on tRFs in lung cancer. This paper presents a comprehensive systematic review of the existing research findings on tRFs in lung cancer, aiming to offer improved biomarkers and drug targets for clinical management of lung cancer.

Single-cell RNA-sequencing uncovers the dynamic changes of tumour immune microenvironment in advanced lung adenocarcinoma

BACKGROUND

The heterogeneity of lung adenocarcinoma (LUAD) plays a vital role in determining the development of cancer and therapeutic sensitivity and significantly hinders the clinical treatment of LUAD.

OBJECTIVE

To elucidate the cellular composition and reveal previously uncharacterised tumour microenvironment in LUAD using single-cell RNA-sequencing (scRNA-seq).

METHODS

Two scRNA-seq datasets with 106 829 high-quality cells from 34 patients including 11 normal, 9 early (stage I and II) and 14 advanced (stage III and IV) LUAD were integrated and clustered to explore diagnostic and therapeutic cell populations and their biomarkers for diverse stages of LUAD. Three independent bulk RNA-seq datasets were used to validate the results from scRNA-seq analysis. The expression of marker genes for specific cell types in early and advanced LUAD was verified by immunohistochemistry (IHC).

RESULTS

Comprehensive cluster analysis identified that S100P+ epithelial and SPP1+ macrophage, positively related to poor outcomes, were preferentially enriched in advanced stage. Although the accumulation of KLRB1+CD8+ T cell and IGHA1+/IGHG1+ plasma cell both significantly associated the favourable prognosis, we also found KLRB1+CD8+ T cell decreased in advanced stage while IGHA1+/IGHG1+ plasma cells were increased. Cell-cell communication analysis showed that SPP1+ macrophage could interact with most of CD8+ subclusters through SPP1-CD44 axis. Furthermore, based on three independent bulk RNA-seq datasets, we built risk model with nine marker genes for specific cell subtypes and conducted deconvolution analysis, both supporting our results from scRNA-seq data. We finally validated the expression of four marker genes in early and advanced LUAD by IHC.

CONCLUSION

Our analyses highlight the molecular dynamics of LUAD epithelial and microenvironment and provide new targets to improve LUAD therapy.

Constructing an APOBEC-related gene signature with predictive value in the overall survival and therapeutic sensitivity in lung adenocarcinoma

Background

APOBEC family play an important role in cancer mutagenesis and tumor development. The role of APOBEC family in lung adenocarcinoma (LUAD) has not been studied comprehensively.

Materials and methods

The expression data of pan-cancer as well as LUAD was obtained from public databases. The expression level of APOBEC family genes was analyzed in different normal and cancer tissues. APOBEC mutagenesis enrichment score (AMES) was utilized to evaluate the APOBEC-induced mutations and the relation of APOBEC with genomic instability. Gene set enrichment analysis was used to identify differentially enriched pathways. Univariate Cox regression and Lasso regression were applied to screen key prognostic genes. The immune cell infiltration was estimated by CIBERSORT. RT-qPCR assay, CCK-8 and Transwell assay were conducted to explore gene expression and lung cancer cell invasion.

Results

Cancer tissues had significantly altered expression of APOBEC family genes and the expression patterns of APOBEC family were different in different cancer types. APOBEC3B was the most aberrantly expressed in most cancer types. In LUAD, we observed a significantly positive correlation of AMES with intratumor heterogeneity (ITH), tumor neoantigen burden (TNB), and tumor mutation burden (TMB). High AMES group had high mutation counts of DNA damage repair pathways, and high enrichment of cell cycle and DNA repair pathways. We identified four prognostic genes (LYPD3, ANLN, MUC5B, and FOSL1) based on AMES, and constructed an AMES-related gene signature. The expressions of four genes were enhanced and accelerated the invasion ability and viability of lung cancer cells. Furthermore, we found that high group increased oxidative stress level.

Conclusions

APOBEC family was associated with genomic instability, DNA damage-related pathways, and cell cycle in LUAD. The AMES-related gene signature had a great potential to indicate the prognosis and guide immunotherapy/chemotherapy for patients suffering from LUAD.

Identification of age- and immune-related gene signatures for clinical outcome prediction in lung adenocarcinoma

BACKGROUND

The understanding of the factors causing decreased overall survival (OS) in older patients compared to younger patients in lung adenocarcinoma (LUAD) remains.

METHODS

Gene expression profiles of LUAD were obtained from publicly available databases by Kaplan-Meier analysis was performed to determine whether age was associated with patient OS. The immune cell composition in the tumor microenvironment (TME) was evaluated using CIBERSORT. The fraction of stromal and immune cells in tumor samples were also using assessed using multiple tools including ESTIMATE, EPIC, and TIMER. Differentially expressed genes (DEGs) from the RNA-Seq data that were associated with age and immune cell composition were identified using the R package DEGseq. A 22-gene signature composed of DEGs associated with age and immune cell composition that predicted OS were constructed using Least Absolute Shrinkage and Selection Operator (LASSO).

RESULTS

In The Cancer Genome Atlas (TCGA)-LUAD dataset, we found that younger patients (≤70) had a significant better OS compared to older patients (>70). In addition, older patients had significantly higher expression of immune checkpoint proteins including inhibitory T cell receptors and their ligands. Moreover, analyses using multiple bioinformatics tools showed increased immune infiltration, including CD4+ T cells, in older patients compared to younger patients. We identified a panel of genes differentially expressed between patients >70 years compared to those ≤70 years, as well as between patients with high or low immune scores and selected 84 common genes to construct a prognostic gene signature. A risk score calculated based on 22 genes selected by LASSO predicted 1, 3, and 5-year OS, with an area under the curve (AUC) of 0.72, 0.72, 0.69, receptively, in TCGA-LUAD dataset and an independent validation dataset available from the European Genome-phenome Archive (EGA).

CONCLUSION

Our results demonstrate that age contributes to OS of LUAD patients atleast in part through its association with immune infiltration in the TME.

Mutational landscape of SWI/SNF complex genes reveal correlation to predictive biomarkers for immunotherapy sensitivity in lung adenocarcinoma patients

BACKGROUND

The search for prognostic biomarkers indicating sensitivity to immunotherapy in lung adenocarcinoma patients has zeroed in on genes in the switch/sucrose non-fermentable (SWI/SNF) pathway. The mutational profiles of key genes are not clearly defined, however, and no comparisons have been conducted on whether mutations in the genes involved provide the same predictive value.

METHODS

In this study, analysis of clinical factors, tumor mutation burden (TMB), chromosomal instability, and co-alterations was conducted for 4344 lung adenocarcinoma samples. Independent online cohorts (N = 1661 and 576) were used to supplement the analysis with survival and RNA-seq data.

RESULTS

Mutational burden and chromosomal instability analysis showed that ARID family mutations (including ARID1A, ARID1B, or ARID2 mutations) and SMARC family mutations (including SMARCA4 or SMARCB1 mutations) display different profiles from wild-type (WT) samples (TMB: ARID versus WT: P < 2.2 × 10^-16, SMARC versus WT: P < 2.2 × 10^-16; CIN: ARID versus WT: P = 1.8 × 10^-5, SMARC versus WT: P = 0.027). Both mutant groups have a higher proportion of transversions than transitions, whereas the ratio is more equal for wild-type samples. Survival analysis shows that patients with ARID mutations were more sensitive to immunotherapy treatment than wild-type and SMARC-mutated patients (P < 0.001 and P = 0.013, respectively), and multivariate Cox analysis reveals that the presence of ARID mutations is likely the main cause.

CONCLUSIONS

The research presented in this study shows that mutations in the ARID gene family, including ARID1A, ARID1B, and ARID2, are primarily responsible for the sensitive response to immunotherapy treatment in patients with lung adenocarcinoma.

Oligo-Metastatic Cancers: Putative Biomarkers, Emerging Challenges and New Perspectives

Some cancer patients display a less aggressive form of metastatic disease, characterized by a low tumor burden and involving a smaller number of sites, which is referred to as "oligometastatic disease" (OMD). This review discusses new biomarkers, as well as methodological challenges and perspectives characterizing OMD. Recent studies have revealed that specific microRNA profiles, chromosome patterns, driver gene mutations (ERBB2, PBRM1, SETD2, KRAS, PIK3CA, SMAD4), polymorphisms (TCF7L2), and levels of immune cell infiltration into metastases, depending on the tumor type, are associated with an oligometastatic behavior. This suggests that OMD could be a distinct disease with specific biological and molecular characteristics. Therefore, the heterogeneity of initial tumor burden and inclusion of OMD patients in clinical trials pose a crucial methodological question that requires responses in the near future. Additionally, a solid understanding of the molecular and biological features of OMD will be necessary to support and complete the clinical staging systems, enabling a better distinction of metastatic behavior and tailored treatments.

Preparation, characterisation, and in vitro cancer-suppression function of RNA nanoparticles carrying miR-301b-3p Inhibitor

BACKGROUND

Multidrug resistance is the biggest barrier on the way to chemotherapy for lung adenocarcinoma (LUAD). For some LUAD patients with cisplatin (DDP) resistance and poor prognoses, the authors put forward RNA nanoparticles (NPs) carrying miR-301b-3p Inhibitor.

METHODS

The NPs were composed of miR-301b-3p, A549 aptamer (A549apt), and Cyanine 5 in a bottom-up manner with a 3-way-junction (3WJ) structure. Diameter, assembly process, and morphology of NPs were observed by Dynamic Light Scattering, Native-Polyacrylamide Gel Electrophoresis, and Atomic Force Microscopy. Cell internalisation, toxicity, proliferation, migration, invasion, and apoptosis were assayed by confocal laser scanning microscope, CCK8, colony formation assay, Transwell, western blot, and flow cytometry.

RESULTS

3WJ-apt-miR was evenly distributed, with diameter of 19.61 ± 0.49 nm and triangular branching structures. The accurate delivery of this NP in vivo was ensured by A549 aptamer featuring specific targeting, with smaller side effects than traditional chemotherapy. Such nanomaterials were effectively internalized by cancer cells, with normal cell activity intact. Cancer cell proliferation, invasion, and migration were suppressed, and DDP sensitivity was enhanced, causing DNA damage and facilitating apoptosis of DDP-resistant cells.

CONCLUSION

Based on RNA self-assembling, the authors researched the effect of miRNA on DDP sensitivity in LUAD regarding gene regulation. 3WJ-apt-miR paves the way for clinical tumour therapy.

Treatment of advanced non-small cell lung cancer with driver mutations: current applications and future directions

With the improved understanding of driver mutations in non-small cell lung cancer (NSCLC), expanding the targeted therapeutic options improved the survival and safety. However, responses to these agents are commonly temporary and incomplete. Moreover, even patients with the same oncogenic driver gene can respond diversely to the same agent. Furthermore, the therapeutic role of immune-checkpoint inhibitors (ICIs) in oncogene-driven NSCLC remains unclear. Therefore, this review aimed to classify the management of NSCLC with driver mutations based on the gene subtype, concomitant mutation, and dynamic alternation. Then, we provide an overview of the resistant mechanism of target therapy occurring in targeted alternations ("target-dependent resistance") and in the parallel and downstream pathways ("target-independent resistance"). Thirdly, we discuss the effectiveness of ICIs for NSCLC with driver mutations and the combined therapeutic approaches that might reverse the immunosuppressive tumor immune microenvironment. Finally, we listed the emerging treatment strategies for the new oncogenic alternations, and proposed the perspective of NSCLC with driver mutations. This review will guide clinicians to design tailored treatments for NSCLC with driver mutations.

Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma

Aim: To find biomarkers for immunity and immunotherapy in lung adenocarcinoma (LUAD) through multiomics analysis. Materials & methods: The multiomics data of patients with LUAD were downloaded from the TCGA and GEO databases. CIBERSORT, quanTIseq, ESTIMATEScore, k-means clustering, gene set enrichment analysis, gene set variation analysis, immunophenoscore and logistic regression were used in this study. Results: PSMB8 HypoMet-HighExp group patients have more active immune-related pathways, more antitumor immune cells, less protumor immune cells, higher immunophenoscore and longer progression-free survival of immune checkpoint inhibitor therapy than HyperMet-LowExp group. In multivariate analysis, PSMB8 showed an independent value. Conclusion: The combination of DNA methylation and mRNA expression of PSMB8 could independently distinguish types of tumor immune microenvironment and predict programmed cell death protein 1/programmed cell death-ligand 1 inhibitors' effects in patients with LUAD.

Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most lethal malignancies and is currently lacking in effective biomarkers to assist in diagnosis and therapy. The aim of this study is to investigate hub genes and develop a risk signature for predicting prognosis of LUAD patients.

METHODS

RNA-sequencing data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes associated with mRNA expression-based stemness indices (mRNAsi) in TCGA. We utilized LASSO Cox regression to assemble our predictive model. To validate our predictive model, me applied it to an external cohort.

RESULTS

mRNAsi index was significantly associated with the tissue type of LUAD, and high mRNAsi scores may have a protective influence on survival outcomes seen in LUAD patients. WGCNA indicated that the turquoise module was significantly correlated with the mRNAsi. We identified a 9-gene signature (CENPW, MCM2, STIL, RACGAP1, ASPM, KIF14, ANLN, CDCA8, and PLK1) from the turquoise module that could effectively identify a high-risk subset of these patients. Using the Kaplan-Meier survival curve, as well as the time-dependent receiver operating characteristic (tdROC) analysis, we determined that this gene signature had a strong predictive ability (AUC = 0.716). By combining the 9-gene signature with clinicopathological features, we were able to design a predictive nomogram. Finally, we additionally validated the 9-gene signature using two external cohorts from GEO and the model proved to be of high value.

CONCLUSION

Our study shows that the 9-gene mRNAsi-related signature can predict the prognosis of LUAD patient and contribute to decisions in the treatment and prevention of LUAD patients.