Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma

Smoking-induced CCNA2 expression promotes lung adenocarcinoma tumorigenesis by boosting AT2/AT2-like cell differentiation

Lung adenocarcinoma (LUAD), a type of non-small cell lung cancer (NSCLC), originates from not only bronchial epithelial cells but also alveolar type 2 (AT2) cells, which could differentiate into AT2-like cells. AT2-like cells function as cancer stem cells (CSCs) of LUAD tumorigenesis to give rise to adenocarcinoma. However, the mechanism underlying AT2 cell differentiation into AT2-like cells in LUAD remains unknown. We analyze genes differentially expressed and genes with significantly different survival curves in LUAD, and the combination of these two analyses yields 147 differential genes, in which 14 differentially expressed genes were enriched in cell cycle pathway. We next analyze the protein levels of these genes in LUAD and find that Cyclin-A2 (CCNA2) is closely associated with LUAD tumorigenesis. Unexpectedly, high CCNA2 expression in LUAD is restrictedly associated with smoking and independent of other driver mutations. Single-cell sequencing analyses reveal that CCNA2 is predominantly involved in AT2-like cell differentiation, while inhibition of CCNA2 significantly reverses smoking-induced AT2-like cell differentiation. Mechanistically, CCNA2 binding to CDK2 phosphorylates the AXIN1 complex, which in turn induces ubiquitination-dependent degradation of β-catenin and inhibits the WNT signaling pathway, thereby failing AT2 cell maintenance. These results uncover smoking-induced CCNA2 overexpression and subsequent WNT/β-catenin signaling inactivation as a hitherto uncharacterized mechanism controlling AT2 cell differentiation and LUAD tumorigenesis.

LINC01572 promotes the malignant progression of lung adenocarcinoma by modulating p53 mediated by miRNA-338-5p/TTK axis

OBJECTIVES

Lung cancer is one of the malignant tumors that threaten human health seriously. Long non-coding RNA (lncRNA) is an important factor affecting tumorigenesis and development. However, the mechanism of lncRNA in lung cancer progression remains to be further explored.

METHODS

In this study, the TCGA database was analyzed, and LINC01572 was found to be increased in lung adenocarcinoma (LUAD) tissues. Thereafter, with the help of databases including lncBase, TargetScan, and mirDIP, as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, LINC01572/miRNA-338-5p/TTK regulatory axis and downstream p53 signaling pathway were excavated. qRT-PCR was adopted to detect levels of LINC01572, miRNA-338-5p, and TTK in LUAD cells. The role that LINC01572 played in LUAD cells was validated by CCK-8 assay, flow cytometry, colony formation, Transwell, and scratch healing assays. The binding ability between LINC01572/TTK and miRNA-338-5p was then verified by dual-luciferase and RIP analysis.

KEY FINDINGS

The results of this study demonstrated that LINC01572 was elevated in LUAD cells compared with normal cells. The overexpression of LINC01572 promoted the proliferative and migratory properties of LUAD cells but inhibited cell apoptosis. The inhibition of LINC01572 resulted in the opposite result. In addition, rescue experiments revealed that LINC01572, as a molecular sponge of miRNA-338-5p, targeted TTK to manipulate p53 for facilitating LUAD cell malignant progression. Apart from this, we constructed a mouse xenograft model and confirmed that the knockdown of LINC01572 hindered the growth of LUAD solid tumors in vivo.

CONCLUSIONS

Our findings illuminated the molecular mechanism of LINC01572 influencing LUAD and provided new insights for targeted therapy of LUAD cells.

CDC6 may serve as an indicator of lung adenocarcinoma prognosis and progression based on TCGA and GEO data mining and experimental analyses

Lung adenocarcinoma (LUAD) is one of the most lethal types of cancer worldwide, and accurately predicting patient prognosis is an important challenge. Gene prediction models, which are known for their simplicity and efficiency, have the potential to be used for prognostic predictions. However, the availability of models with true clinical value is limited. The present study integrated tissue sequencing and the clinical information of patients with LUAD from The Cancer Genome Atlas and Gene Expression Omnibus databases using bioinformatics. This comprehensive approach enabled the identification of 252 differentially expressed genes. Subsequently, univariate and multivariate Cox analyses were performed using these genes, and 14 and 3 genes [including cell division cycle 6 (CDC6), hyaluronan mediated motility receptor and STIL centriolar assembly protein] were selected for the construction of two prognostic models. Notably, the 3‑gene prognostic model exhibited a comparable predictive ability to that of the 14‑gene model. Functionally, pathway enrichment analysis revealed that CDC6 played a role in regulating the cell cycle and promoting tumor staging. To further investigate the relevance of CDC6, in vitro experiments involving the downregulation of CDC6 expression were conducted, which resulted in significant inhibition of tumor cell migration, invasion and proliferation. Moreover, in vivo experiments demonstrated that downregulating CDC6 expression significantly reduced the burden and metastasis of in situ lung tumors in mice. These findings suggested that CDC6 may be a critical gene involved in the development and prognosis of LUAD. In summary, the present study successfully constructed a simple yet accurate prognostic prediction model consisting of 3 genes. Additionally, the functional importance of CDC6 as a key gene in the model was identified. These findings lay a crucial foundation for further exploration of prognostic prediction models and a deeper understanding of the functional mechanisms of CDC6. Notably, these results have potential clinical implications for improving personalized treatment and prognosis evaluation for patients with LUAD.

IGL CDR3 Hydropathy and Antigen Chemical Complementarity Associated with Greater Disease-Free Survival in Lung Adenocarcinoma: Implications for Gender Disparities

With lung cancer remaining a challenging disease, new approaches to biomarker discovery and therapy development are needed. Recent immunogenomics, adaptive immune receptor approaches have indicated that it is very likely that B cells play an important role in mediating better overall outcomes. As such, we assessed physicochemical features of lung adenocarcinoma resident IGL complementarity determining region-3 (CDR3) amino acid (AA) sequences and determined that hydrophobic CDR3 AA sequences were associated with a better disease-free survival (DFS) probability. Further, using a recently developed chemical complementarity scoring algorithm particularly suitable for the evaluation of large patient datasets, we determined that IGL CDR3 chemical complementarity with certain cancer testis antigens was associated with better DFS. Chemical complementarity scores for IGL CDR3-MAGEC1 represented a gender bias, with an overrepresentation of males among the higher IGL-CDR3-CTA complementarity scores that were in turn associated with better DFS (logrank p < 0.065). Overall, this study pointed towards potential biomarkers for prognoses that, in some cases are likely gender-specific; and towards biomarkers for guiding therapy, e.g., IGL-based opportunities for antigen targeting in the lung cancer setting.

Prognosis of early-stage lung adenocarcinoma in young patients

Lung adenocarcinoma (LUAD) is a familiar lung cancer with a poor prognosis. This study was meant to determine whether there are differences in survival between younger and older patients with early-stage LUAD because of the rise in the incidence of LUAD in young individuals over the previous few decades. We analysed the clinical, therapeutic and prognostic features of a cohort (2012-2013) of 831 consecutive patients with stage I/II LUAD who underwent curative surgical resection at Shanghai Pulmonary Hospital. Propensity score matching (PSM) was performed for age, sex, tumour size, tumour stage and therapy in a 2:1 ratio between the two groups without taking gender, illness stage at operation or decisive treatment into account. Following PSM analysis to create a 2:1 match for comparison, the final survival study included 163 patients with early-stage LUAD <50 years and 326 patients ≥50 years. Surprisingly, younger patients were overwhelmingly female (65.6%) and never smokers (85.9%). There were no statistical differences between the two groups in terms of the overall survival rate (P = 0.067) or time to advancement (P = 0.76). In conclusion, no significant differences stood out between older and younger patients with stage I/II LUAD regarding overall and disease-free survival rates. Younger patients with early-stage LUAD were more likely to be female and never smokers, which suggests that risk factors other than active smoking may be responsible for lung carcinogenesis in these patients.

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.

CDCA8/SNAI2 Complex Activates CD44 to Promote Proliferation and Invasion of Pancreatic Ductal Adenocarcinoma

Simple Summary

There is an urgent need to find an effective therapeutic target for pancreatic cancer owing to late diagnosis, tumor metastasis, and current ineffective targeted drugs. We aimed to identified potential targets for the treatment of pancreatic cancer. In this study, the specific mechanism by which the CDCA8 contributes to pancreatic cancer progression via the activation of CD44 was clarified, and CDCA8 knockdown inhibited the proliferation and metastasis of pancreatic cancer. This finding may provide a promising target for future targeted therapies of pancreatic cancer.

Abstract

(1) Background: Recently, cell division cycle associated 8 (CDCA8) was found to be overexpressed in pancreatic ductal adenocarcinoma (PDAC). Here, we aimed to explore the specific mechanism of action of CDCA8 in PDAC progression. (2) Methods: All human PDAC samples and clinical data were collected from Huashan Hospital, Fudan University. All experimental studies were carried out using many in vitro and in vivo assays, including lentiviral transfection, real-time quantitative polymerase chain reaction (qPCR), western blotting, co-immunoprecipitation (Co-IP), chromatin IP (ChIP)-qPCR, dual-luciferase reporter, and in vivo imaging assays. (3) Results: Clinical data analysis of human PDAC samples revealed that CDCA8 overexpression were positively and negatively associated with tumor grade (p = 0.007) and overall survival (p = 0.045), respectively. CDCA8 knockdown inhibited PDAC proliferation and invasion in in vitro and in vivo assays. CD44 was also up-regulated by CDCA8 during PDAC progression. CDCA8 could be combined with SNAI2 to form a CDCA8/SNAI2 complex to integrate with the CD44 promoter as indicated through ChIP-qPCR and dual-luciferase reporter assays. (4) Conclusion: We showed that CDCA8-CD44 axis plays a key role in the proliferation and invasion of PDAC, which provides a potential target for treatment.

ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG May Serve as Diagnostic and Prognostic Biomarkers in Endometrial Carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC), the most common gynecologic malignancy in developed countries, remains to be a major public health problem. Further studies are surely needed to elucidate the tumorigenesis of UCEC. Herein, intersecting 203 differentially expressed genes (DEGs) were identified with the GSE17025, GSE63678, and The Cancer Genome Atlas-UCEC datasets. The Gene Ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis and protein-protein interaction (PPI) network were performed on those 203 DEGs. Intriguingly, 6 of the top 10 nodes in the PPI network were related to unfavorable prognosis, that is, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG. The mRNA and protein expression levels of the 6 hub genes were elevated in UCEC tissues compared to normal tissues. Higher expression of the 6 hub genes was associated with poor prognostic clinicopathological characteristics. The receiver operating characteristic curve suggested the significant diagnostic ability of the 6 hub genes for UCEC. Then, underlying pathogeneses of UCEC including promoter methylation level, TP53 mutation status, genomic genetic variation, and immune cells infiltration were analyzed. The mRNA expression level of the 6 hub genes was also higher in cervical squamous cell carcinoma and endocervical adenocarcinoma, uterine carcinosarcoma, and ovarian serous cystadenocarcinoma tissues than in corresponding normal tissues. In conclusion, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG may be considered diagnostic and prognostic biomarkers in UCEC.

Establishing an 8-gene immune prognostic model based on TP53 status for lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) results in a majority of cancer burden worldwide. TP53 is the most commonly mutated in LUAD. This study aimed to reveal the relation between TP53 and tumor microenvironment (TME) for improving LUAD treatment.

Methods

Differentially expressed genes (DEGs) related to immunity were analyzed between TP53‐WT and TP53‐MUT groups. Least absolute shrinkage and selection operator (LASSO) Cox regression was applied to screen prognostic DEGs. Two independent datasets were included to evaluate the robustness of the prognostic model.

Results

An 8‐gene prognostic model containing ANLN, CCNB1, DLGAP5, FAM83A, GJB2, NAPSA, SFTPB, and SLC2A1 was established based on DEGs. LUAD samples were classified into high‐ and low‐risk groups with differential overall survival in the two datasets. M0 macrophages, M1 macrophages, and activated memory CD4 T cells were more enriched in high‐risk group. Immune checkpoints of PDCD1, LAG3, and CD274 were also high‐expressed in high‐risk group.

Conclusion

The study improved the understanding of the role of TP53 in the TME modulation. The 8‐gene model had robust performance to predict LUAD prognosis in clinical practice. In addition, the eight prognostic genes may also serve as potential targets for designing therapeutic drugs for LUAD patients.

Deciphering the tumor cell-infiltrating landscapes reveal microenvironment subtypes and therapeutic potentials for nonsquamous NSCLC

Recent studies highlighted the clinicopathologic importance of tumor microenvironment (TME) in delineating molecular attributes and therapeutic potentials. However, the overall TME cell-infiltration landscape in non-squamous NSCLC have not been comprehensively recognized. In this study, we employed consensus non-negative matrix factorization (NMF) molecular subtyping to determine the TME cell infiltration patterns and identified three TME clusters (TME-C1, -C2, -C3) characterized by distinct clinicopathologic features, infiltrating cells, and biological processes. Proteomics analyses revealed that cGAS-STING immune signaling mediated protein and phosphorylation level were significantly upregulated in inflamed-related TME-C2 clusters. The TMEsig-score extracted from the TME-related signature divided NSCLC patients into high- and low-score subgroups, where a high score was associated with favorable prognosis and immune infiltration. Genomic landscape revealed that patients with low TMEsig-score harbored greater somatic copy number alternations and higher mutation frequency of driver genes involving STK11, KEAP1 and SMARCA4 et al. Drug sensitivity analyses suggested that tumors with high TMEsig-score were responsible for favorable clinical response to immune check-point inhibitors (ICI) treatment. In summary, this study highlights that comprehensive recognizing of the TME cell infiltration landscape will contribute to enhance our understanding of TME immune regulation and promote effectiveness of precision biotherapy strategies.

Identification of a Prognostic Signature Composed of GPI, IL22RA1, CCT6A and SPOCK1 for Lung Adenocarcinoma Based on Bioinformatic Analysis of lncRNA-Mediated ceRNA Network and Sample Validation

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high morbidity and mortality in China and worldwide. Long non-coding RNAs (lncRNAs) as the competing endogenous RNA (ceRNA) play an essential role in the occurrence and development of LUAD. However, identifying lncRNA-related biomarkers to improve the accuracy of LUAD prognosis remains to be determined. This study downloaded RNA sequence data from The Cancer Genome Atlas (TCGA) database and identified the differential RNAs by bioinformatics. A total of 214 lncRNA, 198 miRNA and 2989 mRNA were differentially identified between LUAD and adjacent nontumor samples. According to the ceRNA hypothesis, we constructed a lncRNA-miRNA-mRNA network including 95 protein-coding mRNAs, 7 lncRNAs and 15 miRNAs, and found 24 node genes in this network were significantly associated with the overall survival of LUAD patients. Subsequently, through LASSO regression and multivariate Cox regression analyses, a four-gene prognostic signature composed of GPI, IL22RA1, CCT6A and SPOCK1 was developed based on the node genes of the lncRNA-mediated ceRNA network, demonstrating high performance in predicting the survival and chemotherapeutic responses of low- and high-risk LUAD patients. Finally, independent prognostic factors were further analyzed and combined into a well-executed nomogram that showed strong potential for clinical applications. In summary, the data from the current study suggested that the four-gene signature obtained from analysis of lncRNA-mediated ceRNA could serve as a reliable biomarker for LUAD prognosis and evaluation of chemotherapeutic response.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

Identification of Novel Subtypes in Lung Adenocarcinoma: Evidence from Gene Set Variation Analysis in Tumor and Adjacent Nontumor Samples

In patients with lung adenocarcinoma (LUAD), the prognostic role of adjacent nontumor tissues is still unknown. Alterations in the activity of immunologic and hallmark gene sets in adjacent nontumor tissues may have a potential influence on cell proliferation of normal lung cell after pulmonary lobectomy. We sought to discover LUAD subgroups and prognostic gene sets based on changes in gene set activity in tumor and adjacent nontumor tissues. Firstly, we used gene set variation analysis (GSVA) to characterize the activity changes of 4922 gene sets in LUAD and nontumor samples. Luckily, we identified three novel LUAD subtypes using the nonnegative matrix factorization (NMF) algorithm. In detailed, patients with subtype-3 had a favorable prognosis, but subtypes 1 and 2 had a bad prognosis. In addition, patients with subtype-3 in the validation cohort also lived longer. Meanwhile, using the LASSO-Cox algorithm, we discovered 15 prognostic gene sets in tumors (T gene sets) and two prognostic gene sets in adjacent nontumors (N gene sets). Interestingly, genes from N gene sets were related with immune response in nontumor tissues, but genes from T gene sets were correlated with DNA damaging and repairing in tumor tissues. These findings highlighted the possibility of a stronger immune response in nearby nontumor tissues. In conclusion, our study established a theoretical foundation for selecting therapy strategy for LUAD patients that should be guided by changes in activity in tumor and adjacent nontumor tissues, particularly after pulmonary lobectomy.

Identification of HMMR as a prognostic biomarker for patients with lung adenocarcinoma via integrated bioinformatics analysis

Background

Lung adenocarcinoma (LUAD) is the most prevalent tumor in lung carcinoma cases and threatens human life seriously worldwide. Here we attempt to identify a prognostic biomarker and potential therapeutic target for LUAD patients.

Methods

Differentially expressed genes (DEGs) shared by GSE18842, GSE75037, GSE101929 and GSE19188 profiles were determined and used for protein-protein interaction analysis, enrichment analysis and clinical correlation analysis to search for the core gene, whose expression was further validated in multiple databases and LUAD cells (A549 and PC-9) by quantitative real-time PCR (qRT-PCR) and western blot analyses. Its prognostic value was estimated using the Kaplan-Meier method, meta-analysis and Cox regression analysis based on the Cancer Genome Atlas (TCGA) dataset and co-expression analysis was conducted using the Oncomine database. Gene Set Enrichment Analysis (GSEA) was performed to illuminate the potential functions of the core gene.

Results

A total of 115 shared DEGs were found, of which 24 DEGs were identified as candidate hub genes with potential functions associated with cell cycle and FOXM1 transcription factor network. Among these candidates, HMMR was identified as the core gene, which was highly expressed in LUAD as verified by multiple datasets and cell samples. Besides, high HMMR expression was found to independently predict poor survival in patients with LUAD. Co-expression analysis showed that HMMR was closely related to FOXM1 and was mainly involved in cell cycle as suggested by GSEA.

Conclusion

HMMR might be served as an independent prognostic biomarker for LUAD patients, which needs further validation in subsequent studies.

Development of a novel embryonic germline gene-related prognostic model of lung adenocarcinoma

Background

Emerging evidence implicates the correlation of embryonic germline genes with the tumor progress and patient's outcome. However, the prognostic value of these genes in lung adenocarcinoma (LUAD) has not been fully studied. Here we systematically evaluated this issue, and constructed a novel signature and a nomogram associated with embryonic germline genes for predicting the outcomes of lung adenocarcinoma.

Methods

The LUAD cohorts retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were used as training set and testing set, respectively. The embryonic germline genes were downloaded from the website https://venn.lodder.dev. Then, the differentially expressed embryonic germline genes (DEGGs) between the tumor and normal samples were identified by limma package. The functional enrichment and pathway analyses were also performed by clusterProfiler package. The prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO)-Cox regression method. Survival and Receiver Operating Characteristic (ROC) analyses were performed to validate the model using training set and four testing GEO datasets. Finally, a prognostic nomogram based on the signature genes was constructed using multivariate regression method.

Results

Among the identified 269 DEGGs, 249 were up-regulated and 20 were down-regulated. GO and KEGG analyses revealed that these DEGGs were mainly enriched in the process of cell proliferation and DNA damage repair. Then, 103 DEGGs with prognostic value were identified by univariate Cox regression and further filtered by LASSO method. The resulting sixteen DEGGs were included in step multivariate Cox regression and an eleven embryonic germline gene related signature (EGRS) was constructed. The model could robustly stratify the LUAD patients into high-risk and low-risk groups in both training and testing sets, and low-risk patients had much better outcomes. The multi-ROC analysis also showed that the EGRS model had the best predictive efficacy compared with other common clinicopathological factors. The EGRS model also showed robust predictive ability in four independent external datasets, and the area under curve (AUC) was 0.726 (GSE30219), 0.764 (GSE50081), 0.657 (GSE37745) and 0.668 (GSE72094). More importantly, the expression level of some genes in EGRS has a significant correlation with the progression of LUAD clinicopathology, suggesting these genes might play an important role in the progression of LUAD. Finally, based on EGRS genes, we built and calibrated a nomogram for conveniently evaluating patients' outcomes.

Construction of an immune-related lncRNA signature as a novel prognosis biomarker for LUAD

The tumor immune microenvironment of lung cancer is associated with prognosis and immunotherapy efficacy. Long noncoding RNAs are identified as prognostic biomarkers associated with immune functions. We constructed a signature comprising differentially expressed immune-related lncRNAs to predict the prognosis of patients with lung adenocarcinoma. We established the immune-related lncRNA signature by pairing immune-related lncRNAs regardless of expression level and lung adenocarcinoma patients were divided into high- and low-risk groups. The prognosis of patients in the two groups was significantly different; The immune-related lncRNA signature could serve as an independent lung adenocarcinoma prognostic indicator. The signature correlated negatively with B cell, CD4+ T cell, M2 macrophage, neutrophil, and monocyte immune infiltration. Patients with low risk scores had a higher abundance of immune cells and stromal cells around the tumor. Gene set enrichment analysis showed that samples from low-risk group were more active in the IgA production in intestinal immune network and the T and B cell receptor signaling pathway. High-risk groups had significant involvement of the cell cycle, DNA replication, adherens junction, actin cytoskeleton regulation, pathways in cancer, and TGF-β signaling pathways. High risk scores correlated significantly negatively with high CTLA-4 and HAVCR2 expression and higher median inhibitory concentration of common anti-tumor chemotherapeutics (e.g., cisplatin, paclitaxel, gemcitabine) and targeted therapy (e.g., erlotinib and gefitinib). We identified a reliable immune-related lncRNA lung adenocarcinoma prognosis model, and the immune-related lncRNA signature showed promising clinical prediction value.

PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery

For high-dimensional expression data, most prognostic models perform feature selection based on individual genes, which usually lead to unstable prognosis, and the identified risk genes are inherently insufficient in revealing complex molecular mechanisms. Since most genes carry out cellular functions by forming protein complexes-basic representatives of functional modules, identifying risk protein complexes may greatly improve our understanding of disease biology. Coupled with the fact that protein complexes have been shown to have innate resistance to batch effects and are effective predictors of disease phenotypes, constructing prognostic models and selecting features with protein complexes as the basic unit should improve the robustness and biological interpretability of the model. Here, we propose a protein complex-based, group lasso-Cox model (PCLasso) to predict patient prognosis and identify risk protein complexes. Experiments on three cancer types have proved that PCLasso has better prognostic performance than prognostic models based on individual genes. The resulting risk protein complexes not only contain individual risk genes but also incorporate close partners that synergize with them, which may promote the revealing of molecular mechanisms related to cancer progression from a comprehensive perspective. Furthermore, a pan-cancer prognostic analysis was performed to identify risk protein complexes of 19 cancer types, which may provide novel potential targets for cancer research.

Oncogenic role of abnormal spindle‑like microcephaly‑associated protein in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common malignant cancer worldwide. It is urgent to explore its underlying molecular mechanism and identify novel diagnostic biomarkers. Abnormal spindle-like microcephaly (ASPM) has recently received considerable attention due to its function in tumor progression. However, its role in LUAD is unclear. The present study aimed to explore the clinical role of ASPM in LUAD. Seven pairs of LUAD and adjacent normal tissues were collected to identify potential LUAD biomarkers using transcriptome sequencing. The association between ASPM expression and LUAD progression was evaluated using bioinformatics analysis and data obtained from clinical specimens. Using small interfering RNA technology, the function of ASPM was analyzed in the LUAD H1299 and A549 cell lines. Transcriptional profiling of ASPM-deficient H1299 cells was then performed to determine the downstream targets of ASPM. Using databases and clinical specimens, it was revealed that ASPM expression was frequently elevated in LUAD tissues, and this upregulation was highly associated with LUAD progression. ASPM served as an oncogenic regulator of LUAD cell proliferation and metastasis. Mechanistically, ASPM facilitated epithelial-mesenchymal transition (EMT) via the PI3K/AKT signaling pathway and 740 Y-P, an activator of this pathway, restored the migratory ability of ASPM-knockdown LUAD cells. The current study identified ASPM as an independent prognostic biomarker of LUAD that served an important oncogenic role in regulating LUAD cell metastasis by promoting EMT via the PI3K/AKT signaling pathway. Targeting ASPM may therefore be a therapeutic strategy for treating LUAD.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.