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

Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach

Relevance

The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome's regulatory function is closely associated with the disease's pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine).

Methods

This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters.

Results

This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation.

Conclusions

Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

OVOL2 induces autophagy-mediated epithelial-mesenchymal transition by the ERK1/2 MAPK signaling in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related death worldwide. Epithelial-mesenchymal transition (EMT) plays an important role in malignant tumor progression. Recently, accumulating evidence has shown that autophagy is involved in the regulation of EMT-induced migration. Therefore, the exploration of targets to inhibit EMT by targeting autophagy is important. In this study, we found that OVO-like zinc finger 2 (OVOL2) may be a key target for regulating autophagy-induced EMT. Firstly, we found that OVOL2 expression was dramatically downregulated in LUAD. Low expression of OVOL2 is an indicator of poor prognosis in LUAD. In vitro experiments have shown that downregulation of OVOL2 expression induces EMT, thereby promoting malignant biological behavior, such as proliferation, migration, and invasion of LUAD cells. Interestingly, autophagy is a key step in regulating OVOL2 and inducing EMT. Furthermore, OVOL2 regulates autophagy through the MAPK signaling pathway, ultimately inhibiting the malignant progression of LUAD.

Prognostic Significance of Iron Metabolism Related Genes in Human Lung Adenocarcinoma

Background

Iron metabolism related genes participate in cell proliferation, cell growth, and redox cycling in multiple cancers. Limited studies have revealed the roles and clinical significance of iron metabolism in the pathogenesis and prognosis of lung cancer.

Methods

A total of 119 iron metabolism related genes were extracted from MSigDB database and their prognostic values were determined in The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset and the Gene Expression Profiling Interactive Analysis 2 (GEPIA 2) database. Immunohistochemistry technique and correlations with immune cell infiltration, gene mutation and drug resistance were used to identify the potential and underlying mechanisms of STEAP1 and STEAP2 as prognostic biomarkers of LUAD.

Results

The expression of STEAP1 and STEAP2 are negatively associated with the prognosis of LUAD patients both at the mRNA and protein level. The expression of STEAP1 and STEAP2 was not only negatively correlated with the trafficking degree of CD4+ T immune cells and positively related to most immune cells' trafficking degree, but also significantly associated with gene mutation status, particularly with mutations on TP53 and STK11. Four types of drug resistance showed significant correlation with the expression level of STEAP1 while 13 types of drug resistance were associated with the expression level of STEAP2.

Conclusion

Multiple iron metabolism related genes including STEAP1 and STEAP2 are significantly associated with the prognosis of LUAD patients. STEAP1 and STEAP2 might affect the prognosis of LUAD patients partially through immune cell infiltration, gene mutation and drug resistance, which indicated they were independent prognostic factors for LUAD patients.

Down-regulation of interleukin-2 predicts poor prognosis and associated with immune escape in lung adenocarcinoma

Background and Objective: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and has a poor prognosis. Interleukin-2 (IL2) is a cytokine that stimulates lymphocyte proliferation. However, its role in LUAD remains unclear. Methods: The UALCAN, human protein atlas (HPA), and tumor immune estimation resource (TIMER) databases were used to investigate IL2 expression in samples from patients with LUAD. The HPA, PrognoScan, and Kaplan-Meier plotter databases were used to examine the prognostic value of IL2 in LUAD. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to analyze IL2-interacting genes identified through the GeneMANIA database. TIMER was used to analyze the correlation of IL2 expression with immune cell infiltration and immune checkpoint expression levels in LUAD. Results: Bioinformatic analysis using the TIMER, The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN), and HPA public databases showed that IL2 expression was lower in patients with LUAD than in the normal control group. Moreover, patients with low IL2 expression exhibited poor overall survival. Furthermore, IL2 expression was significantly positively correlated with various immune cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells, in patients with LUAD. Additionally, IL2 expression was markedly positively associated with the above-mentioned immune cells. Furthermore, IL2 expression was positively correlated with PD-1, PD-L1, and CTLA-4 expression. Conclusion: Our results indicate that down-regulation of IL2 predicts poor prognosis and is associated with immune escape in LUAD, and IL2 could serve as a potential novel prognostic biomarker of LUAD.

Evaluating the Expression and Prognostic Value of Genes Encoding Microtubule-Associated Proteins in Lung Cancer

Microtubule-associated proteins (MAPs) play essential roles in cancer development. This study aimed to identify transcriptomic biomarkers among MAP genes for the diagnosis and prognosis of lung cancer by analyzing differential gene expressions and correlations with tumor progression. Gene expression data of patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) from the Cancer Genome Atlas (TCGA) database were used to identify differentially expressed MAP genes (DEMGs). Their prognostic value was evaluated by Kaplan-Meier and Cox regression analysis. Moreover, the relationships between alterations in lung cancer hallmark genes and the expression levels of DEMGs were investigated. The candidate biomarker genes were validated using three independent datasets from the Gene Expression Omnibus (GEO) database and by quantitative reverse transcription polymerase chain reaction (qRT-PCR) on clinical samples. A total of 88 DEMGs were identified from TCGA data. The 20 that showed the highest differential expression were subjected to association analysis with hallmark genes. Genetic alterations in TP53, EGFR, PTEN, NTRK1, and PIK3CA correlated with the expression of most of these DEMGs. Of these, six candidates-NUF2, KIF4A, KIF18B, DLGAP5, NEK2, and LRRK2-were significantly differentially expressed and correlated with the overall survival (OS) of the patients. The mRNA expression profiles of these candidates were consistently verified using three GEO datasets and qRT-PCR on patient lung tissues. The expression levels of NUF2, KIF4A, KIF18B, DLGAP5, NEK2, and LRRK2 can serve as diagnostic biomarkers for LUAD and LUSC. Moreover, the first five can serve as prognostic biomarkers for LUAD, while LRRK2 can be a prognostic biomarker for LUSC. Our research describes the novel role and potential application of MAP-encoding genes in clinical practice.

p21-activated kinase 2 binds to transcription factor SOX2 and up-regulates DEK to promote the progression of lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) is a prevalent and progressive subtype of lung cancer. This study aimed to substantiate the regulatory effect of the PAK2/SOX2/DEK axis on the LSCC development. LSCC tissues (n = 83) and adjacent normal tissues were collected and SOX2 expression was determined by qRT-PCR and Western blotting. Correlation between SOX2 expression and the prognosis of LSCC patients was then explored utilizing Kaplan-Meier analysis. Co-immunoprecipitation and glutathione-S-transferase pull-down assays were conducted to validate the binding of SOX2 to DEK. Gain- and loss- of function assays were then performed on LSCC cells, with CCK-8 and Transwell assays applied to detect the malignant behaviors of cells. A mouse xenograft model of LSCC was further established for in vivo validation. The expression levels of SOX2, PAK2 and DEK were up-regulated in LSCC tissues and cells. SOX2 overexpression was correlated with poor prognosis of LSCC patients. Knockdown of SOX2 weakened the viability and the migratory and invasive potential of LSCC cells. Further, PAK2 directly interacted with SOX2. PAK2 overexpression accelerated the malignant phenotypes of LSCC cells through interplay with SOX2. Moreover, SOX2 activated the expression of DEK, and silencing DEK attenuated the malignant behaviors of LSCC cells. In conclusion, PAK2 could bind to the transcription factor SOX2 and thus activate the expression of DEK, thereby driving the malignant phenotypes of LSCC cells both in vivo and in vitro.

AC099850.3/NCAPG Axis Predicts Poor Prognosis and is Associated with Resistance to EGFR Tyrosine-Kinase Inhibitors in Lung Adenocarcinoma

Background

TKI-acquired resistance markedly interferes with treatment of lung cancer patients with EGFR mutant features. Long non-coding RNAs (lncRNAs) modify EGFR-TKI resistance during tumor progression. Non-structural maintenance of chromosomes condensin I complex subunit G (NCAPG) is a mitosis-related protein that is involved in tumorigenesis. We investigated the potential regulatory lncRNAs of NCAPG in lung adenocarcinoma (LUAD) and assessed their roles in EGFR-TKI resistance.

Methods

Data for 1678 lung cancer patients were retrieved from TCGA and GEO databases and used to evaluate NCAPG and lncRNAs expressions, as well as their prognostic significance in LUAD. Protein levels of NCAPG in LUAD were validated by immuno-histochemistry. To assess the relationship between NCAPG levels and EGFR-TKIs sensitivity, a cohort of 57 LUAD patients administered with EGFR-TKIs was used.

Results

Both NCAPG and lncRNA AC099850.3 were over-expressed in LUAD tissues, and correlated with tumor progression and poor prognosis in LUAD. LncRNA AC099850.3 was identified as a potential regulator of NCAPG expressions. The AC099850.3/NCAGP axis was markedly correlated with EGFR mutations and IC₅₀ of EGFR-TKIs. Besides, elevated NCAPG levels were associated with EGFR-TKIs resistance in 57 LUAD patients undergoing TKIs treatment. Gene set enrichment analysis revealed that both AC099850.3 and NCAGP were abundant in the cell cycle and the p53 signaling pathway.

Conclusion

The AC099850.3/NCAPG axis is a potential prognostic predictor and therapeutic biomarker for EGFR-TKIs in LUAD.

M6A-Related Bioinformatics Analysis Reveals a New Prognostic Risk Signature in Cutaneous Malignant Melanoma

Cutaneous malignant melanoma (CMM) is the most deadly skin cancer worldwide. Despite advances in the treatments of CMM, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer initiation and progression. However, the role of m6A regulators in CMM and their correlation with prognosis remain elusive. Here, we demonstrated that by applying consensus clustering, all CMM patient cases can be divided into two clusters based on overall expression levels of 25 m6A genes. We systematically analyzed the prognostic value of the 25 m6A RNA methylation regulators in CMM and found that ELAVL1, ABCF1, and IGF2BP1 yield the highest scores for predicting the prognosis of CMM. Accordingly, we derived a risk signature consisting of three selected m6A genes as an independent prognostic marker for CMM and validated our findings with data derived from a different CMM cohort. Next, we determined that CNVs in m6A genes had a significant negative impact on patient survival. The mRNA expression levels of m6A genes were correlated with CNV mutation. Moreover, in the selected three risk signature m6A regulators, GSEA analysis showed that they were closely correlated with inflammation and immune pathways. TME analysis proved that m6A gene expressions were negatively correlated with immune cell infiltration. In conclusion, m6A regulators are vital participants in CMM pathology; and ELAVL1, ABCF1, and IGF2BP1 mRNA levels are valuable factors for prognosis prediction and treatment strategy development.

Prognostic and Immunological Role of FAT Family Genes in Non-Small Cell Lung Cancer

Background

The FAT atypical cadherin 1/2/3/4 (FAT1/2/3/4) has been linked to the occurrence and development of various cancers. However, the prognostic and immunological role of FAT1/2/3/4 in non-small cell lung cancer (NSCLC) has not been clarified.

Methods

The association of FAT1/2/3/4 mutations with tumor mutation burden (TMB), tumor immunity in the microenvironment, and response to ICIs in NSCLC was investigated. Whole-exome sequencing data of lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) samples from the Cancer Genome Atlas (TCGA), and an immunotherapy data set comprising mutation and survival data of 75 NSCLC patients were analyzed. Two independent pan-cancer cohorts with large samples were used to validate the prognostic value of FAT1/2/3/4 mutations in immunotherapy.

Results

A high mutation rate of FAT1/2/3/4 (57.3%, 603/1052) was observed in NSCLC patients. TMB was significantly higher in samples with mutated FAT1/2/3/4 compared to samples with wildtype FAT1/2/3/4 (P < .05). FAT2 mutation was found to be an independent prognostic biomarker in LUAD. FAT1/2/3/4 were aberrantly expressed in LUAD and LUSC, and high FAT2 expression strongly correlated with high PD-L1 levels in LUAD. Moreover, LUAD patients with FAT1 mutations showed significantly high activated dendritic cells infiltration, whereas those with FAT2/3/4 mutations had high infiltration of CD8⁺ T-cells, M1 macrophages, activated memory CD4⁺ T-cells, and helper follicular T-cells. It was also observed that FAT1/2/4 mutations were significantly associated with better enhanced objective response and durable clinical benefit, whereas FAT1/2/3 mutations correlated with longer progression-free survival in ICI-treated NSCLC cohort. FAT1/4 mutations were related to better overall survival in pan-cancer patients treated with ICIs.

Conclusions

FAT family genes are potential prognostic and immunological biomarkers and correlate with response to ICIs in NSCLC.

KDM6A missense variants hamper H3 histone demethylation in lung squamous cell carcinoma

KDM6A is the disease causative gene of type 2 Kabuki Syndrome, a rare multisystem disease; it is also a known cancer driver gene, with multiple somatic mutations found in a few cancer types. In this study, we looked at eleven missense variants in lung squamous cell carcinoma, one of the most common lung cancer subtypes, to see how they affect the KDM6A catalytic mechanisms. We found that they influence the interaction with histone H3 and the exposure of the trimethylated Lys27, which is critical for wild-type physiological function to varying degrees, by altering the conformational transition.

CHEK1: a hub gene related to poor prognosis for lung adenocarcinoma

Aim: The study aims to pinpoint hub genes and investigate their functions in order to gain insightful understandings of lung adenocarcinoma (LUAD). Methods: Bioinformatic approaches were adopted to investigate genes in databases including Gene Expression Omnibus, WebGestalt, STRING and Cytoscape, GEPIA2, Oncomine, Human Protein Atlas, TIMER2.0, UALCAN, cBioPortal, TargetScanHuman, OncomiR, ENCORI, Kaplan-Meier plotter, UCSC Xena, European Molecular Biology Laboratory - European Bioinformatics Institute Single Cell Expression Atlas and CancerSEA. Results: Five hub genes were ascertained. CHEK1 was overexpressed in a range of cancers, including LUAD. Promoter methylation, amplification and miRNA regulation might trigger CHEK1 upregulation, signaling poor prognosis. CHEK1 with its coexpressed genes were enriched in the cell cycle pathway. Intratumor heterogeneity of CHEK1 expression could be observed. Cell clusters with CHEK1 expression were more prone to metastasis and epithelial-to-mesenchymal transition. Conclusion: CHEK1 might potentially act as a prognostic biomarker for LUAD.

An integrated model of FTO and METTL3 expression that predicts prognosis in lung squamous cell carcinoma patients

Background

Lung squamous cell carcinoma (LUSC) approximately accounts for a third of lung cancers. However, the role of N6-methyladenosine (m6A) in LUSC remains largely unknown according to previous studies.

Methods

In this study, we investigated the mutations, copy number variants (CNVs), expression of 20 m6A RNA methylation regulators, and clinical data from The Cancer Genome Atlas-LUSC (TCGA-LUSC). These data were used for the training cohort of screening potential biomarkers. The prognostic model of m6A RNA methylation regulators was constructed. A receiver operating characteristic (ROC) analysis was undertaken to determine the area under the curves (AUCs) (for 3- and 5-year survival) for the model. Additionally, the accuracy of the two-gene model was confirmed with external data verifications. Combined two-gene model and clinincal information were performed to construct a nomogram to predict patient’s prognostic risk assessment.

Results

Fat mass- and obesity-associated protein (FTO) and methyltransferase-like 3 (METTL3) were identified as potential prognostic biomarkers to evaluate benign and malignant tumors and prognosticate. The following prognostic model of m6A RNA methylation regulators was constructed: risk score = 0.162 × FTO − 0.069 × METTL3. Patients in low-risk group [median overall survival (mOS), 43.4 months] had longer survival than those with high-risk (mOS, 67.3 months) with P=0.0023. The smoking grade and risk score could be independent prognostic factors (P=0.00098 and P=0.0014, respectively). Ultimately, a nomogram was developed to assist clinicians to predict clinical outcomes.

Conclusions

FTO and METTL3 are potential prognostic biomarkers of LUSC. The two-gene model’s use of prognostic risk scores may provide guidance in the selection of therapeutic strategies.

Predictive Value of Max's Giant Associated Protein Mutation in Outcomes of Lung Adenocarcinoma Patients Treated With Immune Checkpoint Inhibitors

Treatment with immune checkpoint inhibitors (ICIs) has considerably improved prognosis in multiple cancers. However, regardless of PD-L1 expression and TMB, better predictive biomarkers are required to identify ICI-responsive patients. We analyzed a pan-cancer cohort as the discovery cohort to identify the role of Max’s giant associated protein (MGA) mutation in the outcome of ICI treatment in different types of cancers. A pooled lung adenocarcinoma (LUAD) cohort was considered as the validation cohort. Another two LUAD cohorts who received conventional treatment were included for prognostic analysis and mechanism exploration. In the discovery cohort, MGA mutation was a favorable survival biomarker for patients with LUAD than in those with other types of cancers. MGA mutation was positively correlated with the TMB score. The results of the validation cohort were consistent with those of the discovery cohort. Patients with MGA mutation in the TMB-low subgroup had longer survival. Two LUAD cohorts who received standard treatment showed that the MGA mutation was not a prognostic biomarker for standard treatment. Mechanically, we found that the co-mutant genes did not affect the prognostic role of MGA mutation. Gene-set enrichment analysis revealed that genes belonging to the immunodeficiency pathway were enriched in the MGA wild-type group in LUAD. Moreover, activated NK cells were more enriched in the MGA mutant LUAD group. In conclusion, our results demonstrated that MGA mutation was an independent predictive biomarker for ICI therapy. These results may provide a novel insight into identifying potential patients with LUAD for ICI therapy.

Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Simple Summary

Despite recent advancements in lung cancer treatment, individuals with lung cancer have a dismal 5-year survival rate of only 15%. In patients with non-small cell lung cancer (NSCLC), medical images have lately been employed as a valuable marker for predicting overall survival. The primary goal of this study was to develop a risk score based on computed tomography (CT) based radiomics feature signatures that may be used to predict survival in NSCLC patients. After analyzing 577 NSCLC patients from two data sets, we discovered that the risk score model’s prediction ability as a prognostic indicator was superior to other clinical indicators (age, stage, and gender), and the possibility of patient risk stratification with survival was evaluated using a risk score representation of 10 radiomics signatures. According to this study, the risk score generated using CT-based radiomics signatures promises to predict overall survival in NSCLC patients.

Abstract

This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.

Hepcidin Upregulation in Lung Cancer: A Potential Therapeutic Target Associated With Immune Infiltration

Lung cancer has the highest death rate among cancers globally. Hepcidin is a fascinating regulator of iron metabolism; however, the prognostic value of hepcidin and its correlation with immune cell infiltration in lung cancer remain unclear. Here, we comprehensively clarified the prognostic value and potential function of hepcidin in lung cancer. Hepcidin expression was significantly increased in lung cancer. High hepcidin expression was associated with sex, age, metastasis, and pathological stage and significantly predicted an unfavorable prognosis in lung cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) results suggested that hepcidin is involved in the immune response. Furthermore, hepcidin expression was positively correlated with the infiltration levels of immune cells and the expression of diverse immune cell marker sets. Importantly, hepcidin may affect prognosis partially by regulating immune infiltration in lung cancer patients. Hepcidin may serve as a candidate prognostic biomarker for determining prognosis associated with immune infiltration in lung cancer.