A novel protein-based prognostic signature improves risk stratification to guide clinical management in early-stage lung adenocarcinoma patients

Overexpression of Malat1 drives metastasis through inflammatory reprogramming of the tumor microenvironment

Expression of the long noncoding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) correlates with tumor progression and metastasis in many tumor types. However, the impact and mechanism of action by which MALAT1 promotes metastatic disease remain elusive. Here, we used CRISPR activation (CRISPRa) to overexpress MALAT1/Malat1 in patient-derived lung adenocarcinoma (LUAD) cell lines and in the autochthonous K-ras/p53 LUAD mouse model. Malat1 overexpression was sufficient to promote the progression of LUAD to metastatic disease in mice. Overexpression of MALAT1/Malat1 enhanced cell mobility and promoted the recruitment of protumorigenic macrophages to the tumor microenvironment through paracrine secretion of CCL2/Ccl2. Ccl2 up-regulation was the result of increased global chromatin accessibility upon Malat1 overexpression. Macrophage depletion and Ccl2 blockade counteracted the effects of Malat1 overexpression. These data demonstrate that a single lncRNA can drive LUAD metastasis through reprogramming of the tumor microenvironment.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Identification of SLC2A3 as a prognostic indicator correlated with the NF-κB/EMT axis and immune response in head and neck squamous cell carcinoma

SLC2A3 is an important member of the glucose transporter superfamily. It has been recently suggested that upregulation of SLC2A3 is associated with poor survival and acts as a prognostic marker in a variety of tumors. Unfortunately, the prognostic role of SLC2A3 in head and neck squamous cell carcinoma (HNSC) is less known. In the present study, we analyzed SLC2A3 expression in HNSC and its correlation with prognosis using TCGA and GEO databases. The results showed that SLC2A3 mRNA expression was higher in HNSC compared with adjacent normal tissues, which was validated with our 9 pairs of HNSC specimens. Moreover, high SLC2A3 expression predicted poor prognosis in HNSC patients. Mechanistically, GSEA revealed that high expression of SLC2A3 was enriched in epithelial-mesenchymal transition (EMT) and NF-κB signaling. In HNSC cell lines, SLC2A3 knockdown inhibited cell proliferation and migration. In addition, NF-κB P65 and EMT-related gene expression was suppressed upon SLC2A3 knockdown, indicating that SLC2A3 may play a preeminent role in the progression of HNSC through the NF-κB/EMT axis. Meanwhile, the expression of SLC2A3 was negatively correlated with immune cells, suggesting that SLC2A3 may be involved in the immune response in HNSC. The correlation between SLC2A3 expression and drug sensitivity was further assessed. In conclusion, our study demonstrated that SLC2A3 could predict the prognosis of HNSC patients and mediate the progression of HNSC via the NF-κB/EMT axis and immune responses.

Overexpressed Malat1 Drives Metastasis through Inflammatory Reprogramming of Lung Adenocarcinoma Microenvironment

Metastasis is the main cause of cancer deaths but the molecular events leading to metastatic dissemination remain incompletely understood. Despite reports linking aberrant expression of long noncoding RNAs (lncRNAs) with increased metastatic incidence , in vivo evidence establishing driver roles for lncRNAs in metastatic progression is lacking. Here, we report that overexpression of the metastasis-associated lncRNA Malat1 (metastasis-associated lung adenocarcinoma transcript 1) in the autochthonous K-ras/p53 mouse model of lung adenocarcinoma (LUAD) is sufficient to drive cancer progression and metastatic dissemination. We show that increased expression of endogenous Malat1 RNA cooperates with p53 loss to promote widespread LUAD progression to a poorly differentiated, invasive, and metastatic disease. Mechanistically, we observe that Malat1 overexpression leads to the inappropriate transcription and paracrine secretion of the inflammatory cytokine, Ccl2, to augment the mobility of tumor and stromal cells in vitro and to trigger inflammatory responses in the tumor microenvironment in vivo . Notably, Ccl2 blockade fully reverses cellular and organismal phenotypes of Malat1 overexpression. We propose that Malat1 overexpression in advanced tumors activates Ccl2 signaling to reprogram the tumor microenvironment to an inflammatory and pro-metastatic state.

miR-31/QKI-5 axis facilitates cell cycle progression of non-small-cell lung cancer cells by interacting and regulating p21 and CDK4/6 expressions

BACKGROUND

RNA-binding protein Quaking-5 (QKI-5), a major isoform of QKIs, inhibits tumor progression in non-small cell lung cancer (NSCLC). However, the underlying molecular mechanisms of QKI-5 in the cell cycle of NSCLC are still largely unknown.

METHODS

MTT, flow cytometry, and colony formation assays were used to investigate cellular phenotypic changes. Mice xenograft model was used to evaluate the antitumor activities of QKI-5. Co-immunoprecipitation, RNA immunoprecipitation (RIP), and RIP sequencing were used to investigate protein-protein interaction and protein-mRNA interaction.

RESULTS

The QKI-5 expression was downregulated in NSCLC tissues compared with that in paired normal adjacent lung tissues. Overexpression of QKI-5 inhibited NSCLC cell proliferative and colony forming ability. In addition, QKI-5 induced cell cycle arrest at G0/G1 phase through upregulating p21^Waf1/Cip1 (p21) expression and downregulating cyclin D1, cyclin-dependent kinase 4 (CDK4), and CDK6 expressions. Further analyses showed that QKI-5 interacts with p21 protein and CDK4, CDK6 mRNAs, suggesting a critical function of QKI-5 in cell cycle regulation. In agreement with in vitro study, the mouse xenograft models validated tumor suppressive functions of QKI-5 in vivo through altering cell cycle G1-phase-associated proteins. Moreover, we demonstrated that QKI-5 is a direct target of miR-31. The QKI-5 expression was anticorrelated with the miR-31 expression in NSCLC patient samples.

CONCLUSION

Our results suggest that the miR-31/QKI-5/p21-CDK4-CDK6 axis might have critical functions in the progression of NSCLC, and targeting this axis could serve as a potential therapeutic strategy for NSCLC.

Construction and experimental validation of an acetylation-related gene signature to evaluate the recurrence and immunotherapeutic response in early-stage lung adenocarcinoma

BACKGROUND

Acetylation is a reversible epigenetic process, playing an important role in the initiation and progression of malignant tumors. However, the prognosis value of acetylation-related genes in the early-stage lung adenocarcinoma (LUAD) remains obscure.

MATERIALS AND METHODS

The acetylation-related genes were collected and clustered based on transcriptome sequencing of the patients with early-stage LUAD from the Cancer Genome Atlas. The genomic divergence analysis, protein-protein interaction network construction, Lasso regression, and univariate Cox regression were used to identify the significant biomarkers for the recurrence of the early-stage LUAD. The multivariate Cox regression was used to establish the predictive model. Gene Expression Omnibus was systemically retrieved and four independent datasets were used for external validation. 23 early-stage LUAD samples were collected from the local hospital to detect the expression difference of the genes in the model. Transfection assays were performed to verify the regulatory ability of the screened gene to the proliferation of LUAD cell lines. The single-cell RNA sequencing of the early-stage LUAD patients and two lung cancer cohorts receiving immunotherapy were utilized to explore the predictive ability of the established model to immunotherapeutic sensitivity.

RESULTS

The clustering based on acetylation-related genes was significantly associated with the recurrence (P < 0.01) and immune infiltration statuses. Through a series of bioinformatical and machine learning methods, RBBP7 and YEATS2 were ultimately identified. Accordingly, a novel gene signature containing RBBP7 and YEATS2 was developed to evaluate the recurrence-free survival of early-stage LUAD, which was then validated in five independent cohorts (pooled hazard ratio = 1.88, 95% confidence interval = 1.49-2.37) and 23 local clinical samples (P < 0.01). The knock-down of YEATS2 obviously suppressed proliferation of H1975 and HCC-827 cells. Single-cell RNA sequencing analyses indicated that RBBP7 and YEATS2 were both associated with the tumor immune response, and the prognosis signature could predict the immunotherapeutic response in two cohorts receiving immunotherapy (P < 0.05; P < 0.01).

CONCLUSIONS

Totally, an acetylation-related gene signature is constructed, helping to evaluate the recurrence and immunotherapeutic effectiveness of early-stage LUAD patients.

Prognostic signature of lipid metabolism associated LncRNAs predict prognosis and treatment of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most predominant histological subtype of lung cancer. Abnormal lipid metabolism is closely related to the development of LUAD. LncRNAs are involved in the regulation of various lipid metabolism-related genes in various cancer cells including LUAD. Here, we aimed to identify lipid metabolism-related lncRNAs associated with LUAD prognosis and to propose a new prognostic signature.

METHODS

First, differentially expressed lncRNAs (DE-lncRNAs) from the TCGA-LUAD and the GSE31210 dataset were identified. Then the correlation analysis between DE-lncRNAs and lipid metabolism genes was performed to screen lipid metabolism-related lncRNAs. Cox regression analyses were performed in the training set to establish a prognostic model and the model was validated in the testing set and the validation set. Moreover, The role of this model in the underlying molecular mechanisms, immunotherapy, and chemotherapeutic drug sensitivity analysis was predicted by methods such as Gene Set Enrichment Analysis, immune infiltration, tumor mutational burden (TMB), neoantigen, Tumor Immune Dysfunction and Exclusion, chemosensitivity analysis between the high- and low-risk groups. The diagnostic ability of prognostic lncRNAs has also been validated. Finally, we validated the expression levels of selected prognostic lncRNAs by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

The prognostic model was constructed based on four prognostic lncRNAs (LINC00857, EP300-AS1, TBX5-AS1, SNHG3) related to lipid metabolism. The receiver operating characteristic curve (ROC) and Kaplan Meier (KM) curves of the risk model showed their validity. The results of Gene Set Enrichment Analysis suggested that differentially expressed genes in high- and low-risk groups were mainly enriched in immune response and cell cycle. There statistical differences in TMB and neoantigen between high- and low-risk groups. Drug sensitivity analysis suggested that patients with low risk scores may have better chemotherapy outcomes. The results of qRT-PCR were suggesting that compared with the normal group, the expressions of EP300-AS1 and TBX5-AS1 were down-regulated in the tumor group, while the expressions of LINC00857 and SNHG3 were up-regulated. The four prognostic lncRNAs had good diagnostic capabilities, and the overall diagnostic model of the four prognostic lncRNAs was more effective.

CONCLUSION

A total of 4 prognostic lncRNAs related to lipid metabolism were obtained and an effective risk model was constructed.

Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma

Background

As the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD).

Methods

First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation.

Results

LUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells.

Conclusions

We developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens.

Identification of HMGB2 associated with proliferation, invasion and prognosis in lung adenocarcinoma via weighted gene co-expression network analysis

Background

High mobility group protein B2 (HMGB2) is a multifunctional protein that plays various roles in different cellular compartments. Moreover, HMGB2 serves as a potential prognostic biomarker and therapeutic target for lung adenocarcinoma (LUAD).

Methods

In this study, the expression pattern, prognostic implication, and potential role of HMGB2 in LUAD were evaluated using the integrated bioinformatics analyses based on public available mRNA expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus databases, both at the single-cell level and the tissue level. Further study in the patient-derived samples was conducted to explore the correlation between HMGB2 protein expression levels with tissue specificity, (tumor size-lymph node-metastasis) TNM stage, pathological grade, Ki-67 status, and overall survival. In vitro experiments, such as CCK-8, colony-formation and Transwell assay, were performed with human LUAD cell line A549 to investigate the role of HMGB2 in LUAD progression. Furthermore, xenograft tumor model was generated with A549 in nude mice.

Results

The results showed that the HMGB2 expression was higher in the LUAD samples than in the adjacent normal tissues and was correlated with high degree of malignancy in different public data in this study. Besides, over-expression of HMGB2 promoted A549 cells proliferation and migration while knocking down of HMGB2 suppressed the tumor promoting effect.

Conclusions

Our study indicated that HMGB2 was remarkably highly expressed in LUAD tissues, suggesting that it is a promising diagnostic and therapeutic marker for LUAD in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02110-y.

Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.

Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis

Background

The early-stage lung adenocarcinoma (LUAD) rate has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure.

Methods

LMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response.

Results

First, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results.

Conclusions

This is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

CANT1 serves as a potential prognostic factor for lung adenocarcinoma and promotes cell proliferation and invasion in vitro

Background

Calcium-activated nucleotidase 1 (CANT1), functions as a calcium-dependent nucleotidase with a preference for UDP. However, the potential clinical value of CANT1 in lung adenocarcinoma (LA) has not been fully clarified. Thus, we sought to identify its potential biological function and mechanism through bioinformatics analysis and in vitro experiments in LA.

Methods

In the present study, we comprehensively investigated the prognostic role of CANT1 in LA patients through bioinformatics analysis and in vitro experiments. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were utilized to analyze the expression of CANT1 in LA patients and their clinical-prognostic value. The immunohistochemistry staining was obtained from the Human Protein Atlas (HPA). A Cox regression model was used to evaluate prognostic factors. Gene ontology (GO) and Gene set enrichment analysis (GSEA) was performed to explore the potential regulatory mechanism of CANT1 in the development of LA. Moreover, we also examined the relationship between CANT1 expression and DNA methylation. Finally, we did in vitro experiments to evaluate the biological behavior and role of CANT1 in LA cells (LACs).

Results

Our study showed that the CANT1 expression was significantly elevated in the LA tissues compared with the normal lung tissues. Increased CANT1 expression was significantly associated with the TN stage. A univariate Cox analysis indicated that high CANT1 expression levels were correlated with poor overall survival (OS) in LA. Besides, CANT1 expression was independently associated with OS in multivariate analysis. GO and GSEA analysis showed the enrichment of mitotic nuclear division, DNA methylation, and DNA damage. Then we found that the high expression of CANT1 is positively correlated with hypomethylation. The methylation level was associated with prognosis in LA patients. Finally, in vitro experiments indicated that knockdown of CANT1 resulted in decreased cell proliferation, invasion, and G1 phase cell-cycle arrest in LACs.

Conclusion

The present study suggested that CANT1 may serve as a potential prognosis biomarker in patients with LA. High CANT1 expression and promoter demethylation was associated with worse outcome. Finally, in vitro experiments verified the biological functions and behaviors of CANT1 in LA.

Impact of thymosin α1 as an immunomodulatory therapy on long-term survival of non-small cell lung cancer patients after R0 resection: a propensity score-matched analysis

BACKGROUND

There is limited information about thymosin α1 (Tα1) as adjuvant immunomodulatory therapy, either used alone or combined with other treatments, in patients with non-small cell lung cancer (NSCLC). This study aimed to evaluate the effect of adjuvant Tα1 treatment on long-term survival in margin-free (R0)-resected stage IA-IIIA NSCLC patients.

METHODS

A total of 5746 patients with pathologic stage IA-IIIA NSCLC who underwent R0 resection were included. The patients were divided into the Tα1 group and the control group according to whether they received Tα1 or not. A propensity score matching (PSM) analysis was performed to reduce bias, resulting in 1027 pairs of patients.

RESULTS

After PSM, the baseline clinicopathological characteristics were similar between the two groups. The 5-year disease-free survival (DFS) and overall survival (OS) rates were significantly higher in the Tα1 group compared with the control group. The multivariable analysis showed that Tα1 treatment was independently associated with an improved prognosis. A longer duration of Tα1 treatment was associated with improved OS and DFS. The subgroup analyses showed that Tα1 therapy could improve the DFS and/or OS in all subgroups of age, sex, Charlson Comorbidity Index (CCI), smoking status, and pathological tumor-node-metastasis (TNM) stage, especially for patients with non-squamous cell NSCLC and without targeted therapy.

CONCLUSION

Tα1 as adjuvant immunomodulatory therapy can significantly improve DFS and OS in patients with NSCLC after R0 resection, except for patients with squamous cell carcinoma and those receiving targeted therapy. The duration of Tα1 treatment is recommended to be >24 months.

Development and Validation of a Prognostic Autophagy-Related Gene Pair Index Related to Tumor-Infiltrating Lymphocytes in Early-Stage Lung Adenocarcinoma

The role of autophagy in lung cancer is context-dependent and complex. Recent studies have reported the important role of autophagy in tumor immune escape. However, the association between autophagy and tumor-infiltrating lymphocytes (TILs) in early-stage lung adenocarcinoma (LUAD) remains unclear. In this study, we aimed to develop and validate the autophagy-related gene pair index (ATGPI) and autophagy clinical prognostic index (ACPI) in multiple LUAD cohorts, including The Cancer Genome Atlas (TCGA) cohort, Gene Expression Omnibus cohorts, and one cohort from Union Hospital, Wuhan (UH cohort), using a Cox proportional hazards regression model with the least absolute shrinkage and selection operator. Multivariate Cox regression analysis demonstrated that there was a significant difference in overall survival (OS) between patients with high and low ATGPI in the testing [hazard ratio (HR) = 1.97; P < 0.001] and TCGA validation (HR = 2.25; P < 0.001) cohorts. Time-dependent receiver operating characteristic curve analysis was also performed. We found that high ATGPI could accurately identify patients with early-stage LUAD with shorter OS, with the areas under the curve of 0.703 and 0.676 in the testing and TCGA validation cohorts, respectively. Concordance index (C-index) was used to evaluate the efficiency of ATGPI and ACPI. The C-index of ACPI was higher than that of ATGPI in the testing (0.71 vs. 0.66; P < 0.001), TCGA validation (0.69 vs. 0.65; P = 0.028), and UH (0.80 vs. 0.70; P = 0.015) cohorts. TIL analysis demonstrated that the proportions of tumor-infiltrating CD4⁺ T cells were lower in the high-ATGPI group than in the low-ATGPI group in both the TCGA validation and UH cohorts. These results indicate the potential clinical use of ATG signatures which are associated with TILs, in identifying patients with early-stage LUAD with different OS.

Significance of stratifin in early progression of lung adenocarcinoma and its potential therapeutic relevance

Lung cancer is the most common cause of global cancer-related mortality, and the main histological type is adenocarcinoma, accounting for 50% of non-small cell lung cancer. In 2015, the World Health Organization (WHO) histological classification defined the concepts of "adenocarcinoma in situ" (AIS) and "minimally invasive adenocarcinoma" (MIA), which are considered to be adenocarcinomas at a very early stage. Although AIS and MIA have a very favorable outcome, once they progress to early but invasive adenocarcinoma (eIA), they can sometimes have a fatal outcome. We previously compared the expression profiles of eIA and AIS, and identified stratifin (SFN; 14-3-3 sigma) as a protein showing significantly higher expression in eIA than in AIS. Expression of SFN is controlled epigenetically by DNA demethylation, and its overexpression is significantly correlated with poorer outcome. In vitro and in vivo analyses have shown that SFN facilitates early progression of adenocarcinoma by enhancing cell proliferation. This review summarizes genetic and epigenetic abnormalities that can occur in early-stage lung adenocarcinoma and introduces recent findings regarding the biological significance of SFN overexpression during the course of lung adenocarcinoma progression. Therapeutic strategies for targeting SFN are also discussed.

A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma

Tumor microenvironment (TME) has been reported to exhibit a crucial effect in lung cancer. Therefore, this study was aimed to investigate the genes associated with TME and develop a risk score to predict the overall survival (OS) of patients with lung adenocarcinoma (LUAD) based on these genes. The immune and stromal scores were generated by the ESTIMATE algorithm for LUAD patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed gene and weighted gene co-expression network analyses were used to derive immune- and stromal-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was applied for further selection and the selected genes were inputted into stepwise regression to develop TME-related risk score (TMErisk) which was further validated in Gene Expression Omnibus (GEO) datasets. TMErisk-related biological phenotypes were analyzed in function enrichment, tumor immune signature, and tumor mutation signature. The patient's response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS). According to our results, TMErisk was developed based on SERPINE1, CX3CR1, CD200R1, GBP1, IRF1, STAP1, LOX, and OR7E47P. Furthermore, high TMErisk was identified as a poor factor for OS in TCGA and GEO datasets, as well as in subgroup analysis with different gender, smoking status, age, race, anatomic site, therapies, and tumor-node-metastasis (TNM) stages. Higher TMErisk is also associated negatively with the abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and other stromal or immune cells. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were less expressed in the high-TMErisk group. Mutations of 19 genes occurred more frequently in the high-TMErisk group. These mutations may be associated with TME change and indicate patients' response to immunotherapy. According to our analyses, a lower TMErisk score may indicate better response and OS outcome of immunotherapy.

Preoperative systemic immune-inflammation index predicts prognosis and guides clinical treatment in patients with non-small cell lung cancer

Objectives: The purpose of the present study was to evaluate the prognostic value of a systemic immune-inflammation index (SII) and the relationship between SII and the effectiveness of postoperative treatment in patients with non-small cell lung cancer (NSCLC).

Methods: A total of 538 patients diagnosed with NSCLC who had undergone curative surgery were retrospectively enrolled in the study. Clinicopathologic and laboratory variables were collected. SII was defined as neutrophil × platelet/lymphocyte counts. Both univariate and multivariate analyses were performed to analyze the prognostic value of these factors.

Results: The preoperative SII level was associated with sex, smoking history, histological type, lesion type, resection type, pathological stage, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), fibrinogen and bone metastasis (P<0.05). The univariate and multivariate analyses revealed that SII was an independent prognostic factor for disease-free survival (DFS, P=0.033) and overall survival (OS, P=0.020). Furthermore, the prognostic value of SII was also verified regardless of the histological type and pathological stage. The subgroup analysis demonstrated that patients with a high SII may benefit from adjuvant therapy (P=0.024 for DFS and P=0.012 for OS).

Conclusion: An increased preoperative SII may independently predict the poor DFS and OS in patients with resectable NSCLC. SII may help select NSCLC patients who might benefit from adjuvant chemotherapy.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma

Background

A hypoxia microenvironment plays a role in the initiation and progression of many cancer types, but its involvement in lung adenocarcinoma is still unclear. This study aimed to explore the potential correlation between hypoxia and lung adenocarcinoma and establish the hypoxia-associated gene signature in lung adenocarcinoma.

Methods

Lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The genes to be included in the hypoxia-associated signature were selected by performing univariate Cox regression analysis and lasso regression analysis. Then, the gene signature was verified by performing a survival analysis and constructing the multiple receiver operating characteristic (ROC) curve. The CIBERSORT tool was then used to investigate the potential correlation between the gene signature and immune cells. Moreover, a nomogram was constructed and evaluated by calculating the C-index.

Results

Four genes (XPNPEP1, ANGPTL4, SLC2A1, and PFKP) were included in the final signature. The results showed that patients in the high-risk group showed worse survival than those in the low-risk group. Moreover, we found two types of immune cells (memory activated CD4⁺ T cell and M0 macrophages) which showed a significant infiltration in the tissues of the high-risk group patients.

Conclusion

The hypoxia-associated gene signature established and validated in this study could be used as a potential prognostic factor in lung adenocarcinoma and may guide the immunotherapy choice.

Biomarkers in Early Diagnosis and Early Stage Lung Cancer: The Clinician's Point of View

Starting from the work of Ulivi and colleagues, we aim to summarize the research area of biomarkers for early diagnosis and early stage lung cancer.

Clinical significance and inflammatory landscapes of a novel recurrence-associated immune signature in early-stage lung adenocarcinoma

The prevalence of early-stage lung adenocarcinoma (LUAD) has increased alongside increased implementation of lung cancer screenings. Robust discrimination criteria are urgently needed to identify those patients who might benefit from additional systemic therapy. Here, to develop a reliable, individualized immune gene-set-based signature to predict recurrence in early-stage LUAD, a novel recurrence-associated immune signature was identified using a least absolute shrinkage and selection operator model, and a stepwise Cox proportional hazards regression model with a training set comprised of 338 early-stage LUAD samples form TCGA, which was subsequently validated in 226 cases from GSE31210 and an independent set of 68 frozen tumor samples with qRT-PCR data. This new classification system remained strongly predictive of prognoses across clinical subgroups and mutation status. Further analysis revealed that samples from high-risk cases were characterized by active interferon signal transduction, distinctive immune cell proportions and immune checkpoint profiles. Moreover, the signature was identified as an independent prognostic factor. In conclusion, the signature is highly predictive of recurrence in patients with early-stage LUAD, which may serve as a powerful prognostic tool to further optimize immunotherapies for cancer.

Novel prognostic model for stratifying survival in stage I lung adenocarcinoma patients

PURPOSE

We combined conventional clinical and pathological characteristics and pathological architectural grading scores to develop a prognostic model to identify a specific group of patients with stage I lung adenocarcinomas with poor survival following surgery.

METHODS

This retrospective study included 198 patients with stage I lung adenocarcinomas recruited from 2004 to 2013. Multivariate analyses were used to confirm independent risk factors, which were checked for internal validity using the bootstrapping method. The prognostic scores, derived from β-coefficients using the Cox regression model, classified patients into high- and low-risk groups. The predictive performance and discriminative ability of the model were assessed by the area under the receiver operating characteristic curve (AUC), concordance index (C-index) and Kaplan-Meier survival analyses.

RESULTS

Three risk factors were identified: T2 (rounding of β-coefficients = 81), necrosis (rounding of β-coefficients = 67), and pathological architectural score of 5-6 (rounding of β-coefficients = 58). The final prognostic score was the sum of points. The derived prognostic scores stratified patients into low- (score ≤ 103) and high- (score > 103) risk groups, with significant differences in 5-year overall survival (high vs. low risk: 49.3% vs. 88.0%, respectively; hazard ratio: 4.55; p < 0.001). The AUC for the proposed model was 0.717. The C-index of the model was 0.693.

CONCLUSION

An integrated prognostic model was developed to discriminate resected stage I adenocarcinoma patients into low- and high-risk groups, which will help clinicians select individual treatment strategies.

TMPRSS4: A Novel Tumor Prognostic Indicator for the Stratification of Stage IA Tumors and a Liquid Biopsy Biomarker for NSCLC Patients

Relapse rates in surgically resected non-small-cell lung cancer (NSCLC) patients are between 30% and 45% within five years of diagnosis, which shows the clinical need to identify those patients at high risk of recurrence. The eighth TNM staging system recently refined the classification of NSCLC patients and their associated prognosis, but molecular biomarkers could improve the heterogeneous outcomes found within each stage. Here, using two independent cohorts (MDA and CIMA-CUN) and the eighth TNM classification, we show that TMPRSS4 protein expression is an independent prognostic factor in NSCLC, particularly for patients at stage I: relapse-free survival (RFS) HR, 2.42 (95% CI, 1.47-3.99), p < 0.001; overall survival (OS) HR, 1.99 (95% CI, 1.25-3.16), p = 0.004). In stage IA, high levels of this protein remained associated with worse prognosis (p = 0.002 for RFS and p = 0.001 for OS). As TMPRSS4 expression is epigenetically regulated, methylation status could be used in circulating tumor DNA from liquid biopsies to monitor patients. We developed a digital droplet PCR (ddPCR) method to quantify absolute copy numbers of methylated and unmethylated CpGs within the TMPRSS4 and SHOX2 (as control) promoters in plasma and bronchoalveolar lavage (BAL) samples. In case-control studies, we demonstrated that TMPRSS4 hypomethylation can be used as a diagnostic tool in early stages, with an AUROC of 0.72 (p = 0.008; 91% specificity and 52% sensitivity) for BAL and 0.73 (p = 0.015; 65% specificity and 90% sensitivity) for plasma, in early stages. In conclusion, TMPRSS4 protein expression can be used to stratify patients at high risk of relapse/death in very early stages NSCLC patients. Moreover, analysis of TMPRSS4 methylation status by ddPCR in blood and BAL is feasible and could serve as a non-invasive biomarker to monitor surgically resected patients.

Exploiting metabolic vulnerabilities of Non small cell lung carcinoma

Lung cancer is the main cause of cancer death worldwide. Non-Small Cell Lung Carcinoma (NSCLC) is the most common subtype of lung cancer, and the prognosis of NSCLC patients in advanced stages is still very poor. Given the need for new therapies, the metabolism of NSCLC has been widely studied in the past two decades to identify vulnerabilities that could be translated into novel anti-metabolic therapeutic approaches. A number of studies have highlighted the role of glucose and mitochondrial metabolism in the development of NSCLC. The metabolic properties of lung tumors have been characterized in detail in vivo, and they include high glucose and lactate use and high heterogeneity regarding the use of nutrients and mitochondrial pathways. This heterogeneity has also been observed in patients infused with labeled nutrients. We will summarize here the knowledge about the use of amino acids, fatty acids and carbohydrates in NSCLC that could lead to new combination treatments.

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.

5 protein-based signature for resectable lung squamous cell carcinoma improves the prognostic performance of the TNM staging

INTRODUCTION

Prognostic biomarkers have been very elusive in the lung squamous cell carcinoma (SCC) and none is currently being used in the clinical setting. We aimed to identify and validate the clinical utility of a protein-based prognostic signature to stratify patients with early lung SCC according to their risk of recurrence or death.

METHODS

Patients were staged following the new International Association for the Study of Lung Cancer (IASLC) staging criteria (eighth edition, 2018). Three independent retrospective cohorts of 117, 96 and 105 patients with lung SCC were analysed to develop and validate a prognostic signature based on immunohistochemistry for five proteins.

RESULTS

We identified a five protein-based signature whose prognostic index (PI) was an independent and significant predictor of disease-free survival (DFS) (p<0.001; HR=4.06, 95% CI 2.18 to 7.56) and overall survival (OS) (p=0.004; HR=2.38, 95% CI 1.32 to 4.31). The prognostic capability of PI was confirmed in an external multi-institutional cohort for DFS (p=0.042; HR=2.01, 95% CI 1.03 to 3.94) and for OS (p=0.031; HR=2.29, 95% CI 1.08 to 4.86). Moreover, PI added complementary information to the newly established IASLC TNM 8th edition staging system. A combined prognostic model including both molecular and anatomical (TNM) criteria improved the risk stratification in both cohorts (p<0.05).

CONCLUSION

We have identified and validated a clinically feasible protein-based prognostic model that complements the updated TNM system allowing more accurate risk stratification. This signature may be used as an advantageous tool to improve the clinical management of the patients, allowing the reduction of lung SCC mortality through a more accurate knowledge of the patient's potential outcome.