KRT6A Promotes Lung Cancer Cell Growth and Invasion Through MYC-Regulated Pentose Phosphate Pathway

Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for predicting prognosis in lung adenocarcinoma

BACKGROUND

Metastasis has been documented as an independent and significant prognostic feature of lung adenocarcinoma (LUAD) patients. However, the underlying genetic and molecular mechanisms responsible for LUAD metastasis and their prognostic significance are not exactly defined.

METHODS

The single-cell transcriptomic profiles of primary and metastatic LUAD samples were integrated as a whole dataset. Enrichment analysis and pseudotime trajectory analysis were performed to illustrate the cellular origins and changes during the metastatic process. The LUAD metastasis-related genes (LMRGs) molecular cluster and signature was constructed through unsupervised consensus clustering and ten machine-learning algorithms in The Cancer Genome Atlas (TCGA) LUAD cohort using ten machine-learning algorithms. Validation of the signature was conducted using four independent cohorts from the Gene Expression Omnibus (GEO) database. Kaplan-Meier, ROC, univariate and multivariate Cox-regression analyses were performed to test the stability of the signature. The gene CCT6A was subjected to knockdown, followed by validation through western blot analysis, flow cytometry, wound healing and transwell-migration assays to determine its potential significance.

RESULTS

First, the signaling pathway networks remodeling and metabolic reprogramming were demonstrated to be involved in the metastasis of malignant LUAD cells, which facilitate their extravasation and adaptation to other organs. Furthermore, distinct subtypes of malignant LUAD cells exhibit tissue-specific patterns. Then, two distinct molecular patterns of LMRGs were established, which showed diverse prognoses. A LUAD metastasis-related gene signature (LMRGS) was constructed via a multiple machine-learning-based integrative procedure, which possesses distinctly superior accuracy than most common clinical features and 69 published prognostic signatures. The patients stratified by the signature into high-risk group had a significantly poorer prognosis compared to those in the low-risk group, and this was well validated across different clinical subgroups. In addition, the risk score calculated by LMRGS remained an independent prognostic parameter in both univariate and multivariate Cox regression. Notably, knockdown of CCT6A gene promoted cell apoptosis and decelerated the cell migration obviously.

CONCLUSION

LMRGS could serve as a novel and promising tool to improve clinical outcomes for individual LUAD patients.

Construction of lung adenocarcinoma subtype and prognosis model based on fatty acid metabolism-related genes

OBJECTIVE

To explore the role of genes related to fatty acid metabolism in lung adenocarcinoma classification and prognosis.

METHODS

Transcriptome and clinical data from the TCGA database and GEO database were collected, the expression of prognostic fatty acid metabolism-related genes in LUAD patients was analyzed, and key genes related to both fatty acid metabolism and subtype were identified. These key genes were further filtered via the LASSO regression method, and the retained genes were used to construct a risk-scoring model. The biological function of RPS4Y1 was verified by cell viability, colony formation, migration, and flow cytometry assays. Finally, immune infiltration and drug sensitivity were analyzed in the high- and low-risk groups.

RESULTS

31 key FAMGs associated with prognosis were identified in LUAD patients. LUAD cases were divided into 3 subtypes on the basis of the expression of these genes. The DEGs between the different subtypes were associated mainly with amino acid metabolic pathways. In addition, among the 46 DEGs between subtypes, 5 key FAMGs (SCGB3 A2, PGC, ADH7, RPS4Y1, and KRT6 A) were identified as the best prognostic markers via LASSO regression to establish a risk scoring model. Patients with low risk scores had a better prognosis and a greater degree of immune cell infiltration than those with high risk scores. RPS4Y1 is highly expressed in LUAD, and its knockdown significantly inhibits the growth of tumor cells. Moreover, we also analyzed drugs likely to be effective for the high- and low-risk groups.

CONCLUSION

FAMGs play important roles in LUAD, and the key genes identified may be new targets for LUAD treatment.

Exploiting replication stress for synthetic lethality in MYC-driven cancers

The oncoprotein MYC, overexpressed in more than 70% of human cancers, plays a pivotal role in regulating gene transcription and has long been recognized as a promising target for cancer therapy. However, no MYC-targeted drug has been approved for clinical use, largely due to the lack of a well-defined druggable domain and its nuclear localization. MYC-overexpressing cancer cells exhibit increased replication stress, driven by factors such as elevated replication origin firing, nucleotide depletion, replication-transcription conflicts, and heightened reactive oxygen species (ROS) production. Simultaneously, MYC activates compensatory mechanisms, including enhanced DNA repair, checkpoint-mediated cell cycle regulation, and metabolic reprogramming, to mitigate this stress and support cell survival. Interfering with these compensatory pathways exacerbates replication stress, leading to synthetic lethality in MYC-driven cancer cells. In this review, we summarize recent advances in leveraging replication stress to achieve synthetic lethality in MYC-driven cancers. Furthermore, we discuss current strategies targeting replication stress, highlighting new opportunities for the development of therapies against MYC-driven malignancies.

Genome-wide screening and validation of exosome-derived TLN1 as a regulator of epithelial-mesenchymal transition in lung cancer

In addition to embryonic development, wound healing, and tissue fibrosis, epithelial-mesenchymal transition (EMT) is another process that enhances tumor invasiveness and metastatic activity. Exosomes transport a variety of bioactive components between cells and are crucial for cell‒cell communication in multiple complex biological processes, including cancer. Although a few studies have shown that exosomes encapsulate microRNAs that induce a pro-EMT tumor microenvironment, a systematic survey of potential EMT-related regulators in lung cancer exosomes is still lacking. To identify exosome-related EMT signals that could be employed for precise cancer diagnosis, we used a computational approach to generate a list of candidates EMT regulators and performed experimental validation in lung cancer cell lines. Particularly, we focused on exosome-derived differentially expressed genes that were not previously reported to be associated with lung cancer. We identified 25 exosome-derived protein coding regulators associated with EMT with aberrant transcript expression in both lung squamous cell carcinoma and lung adenocarcinoma. By focusing on clinical features such as survival time, smoking status, tumor purity, and primary tumor subtypes, we found that these 25 genes are important for lung cancer development based on a combined cohort of 9781 lung cancer samples from 24 independent genomics studies. By validating two examples of upregulated and downregulated exosome-derived regulators, we confirmed that TLN1 is a potential oncogene in lung cancer progression, which suggests that it may serve as a diagnostic marker. In summary, our results provide a potential exosome-based biomarker for cancer diagnosis that could be used as a therapeutic tool to control the occurrence of EMT and affect cancer progression.

MYCN/MNX1 Axis Drives NSCLC Progression by Inducing Macrophage M2 Polarization and CD8+ T Cell Apoptosis via the Wnt/β-Catenin Pathway

Enhanced macrophage M2 polarization and CD8+ T cell dysfunction contribute to the pathophysiology of non-small cell lung cancer (NSCLC). Motor neuron and pancreatic homeobox 1 (MNX1) has emerged as a potential tumor-promoting player. Here, we clarified the activity of MNX1 in NSCLC. PMA-induced THP-1 M0-like macrophages or CD8+ T cells were co-cultured with NSCLC cells. Cell colony formation, migration, proliferation, apoptosis, and invasiveness were assessed by colony formation, wound healing, CCK-8, flow cytometry, and transwell assays, respectively. The ratio of CD206+ macrophages was analyzed by flow cytometry. Ki-67 expression was tested by immunofluorescence. ChIP and luciferase assays were used to evaluate the relationship between MYCN and MNX1. MNX1 was highly expressed in NSCLC, and its loss-of-function suppressed cell growth, motility, and invasiveness in NSCLC cells. MNX1 depletion also diminished macrophage M2 polarization and CD8+ T cell apoptosis. Mechanistically, MYCN increased MNX1 expression at the transcriptional level. MNX1 increase reversed the impact of MYCN depletion on NSCLC cell malignant behaviors, macrophage M2 polarization, and CD8+ T cell viability. MYCN depletion diminished the in vivo growth of A549 subcutaneous xenografts. Additionally, MNX1 increase counteracted the impact of MYCN depletion on the Wnt/β-catenin pathway. Our findings elucidate the oncogenic role of the MYCN/MNX1/Wnt/β-catenin pathway in NSCLC by driving macrophage M2 polarization and diminishing CD8+ T cell viability. Our study thus uncovers a novel mechanism underlying NSCLC development and highlights potential targets for combating NSCLC.

ANKRD11 as a potential biomarker for brain metastasis from lung adenocarcinoma via cerebrospinal fluid liquid biopsy

Background

Brain metastasis (BM) explains the majority of lung cancer-related mortality, especially lung adenocarcinoma (LUAD). The extensive clinical adoption of liquid biopsy presents a minimally invasive approach for the early detection and treatment management of brain metastasis from lung adenocarcinoma (BM-LUAD). Nonetheless, biomarkers for assessing BM-LUAD remain insufficient. This study aims to reveal novel biomarkers for BM-LUAD through the liquid biopsy of cerebrospinal fluid (CSF).

Methods

Circulating tumor DNA (ctDNA)-based panel sequencing was conducted on CSF samples from seven patients with BM-LUAD. Additionally, single-cell RNA sequencing (scRNA-seq) data from normal lung tissue, primary tumors, and BMs were analyzed using publicly available datasets. Functional assays were performed on cell lines, complemented by in vivo studies in nude mice.

Results

CSF liquid biopsy identified high-frequency mutations in EGFR, BRCA2, and ANKRD11 among patients with BM-LUAD. Further analysis highlighted ANKRD11 as a potential biomarker, showing reduced expression in tumor tissues and significant prognostic implications. ScRNA-seq revealed a progressive decrease in ANKRD11 expression along tumor progression, while in vitro and in vivo assays confirmed its role in suppressing tumor invasion and metastasis.

Conclusions

Our study highlights the potential of ctDNA-based CSF liquid biopsy in identifying BM-LUAD, and the efficacy in revealing novel biomarkers for BM-LUAD. Data analyses and functional assays indicated ANKRD11 as a predictive biomarker for BM-LUAD. This result addresses, to some extent, the existing gap in biomarkers for BM-LUAD.

Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses

BACKGROUND

Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and diagnostic capabilities. The objective of this study is to assess the biological processes underlying severe asthma and to detect key molecular biomarkers.

METHODS

We used Weighted Gene Co-Expression Network Analysis (WGCNA) to detect hub genes in the GSE143303 dataset and indicated their functions and regulatory mechanisms using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) annotations. In the GSE147878 dataset, we used Gene Set Enrichment Analysis (GSEA) to determine the regulatory directions of gene sets. We detected differentially expressed genes in the GSE143303 and GSE64913 datasets, constructed a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and validated the model using the GSE147878 dataset and real-time quantitative PCR (RT-qPCR) to confirm the molecular biomarkers.

RESULTS

Using WGCNA, we discovered modules that were strongly correlated with clinical features, specifically the purple module (r = 0.53) and the midnight blue module (r = -0.65). The hub genes within these modules were enriched in pathways related to mitochondrial function and oxidative phosphorylation. GSEA in the GSE147878 dataset revealed significant enrichment of upregulated gene sets associated with oxidative phosphorylation and downregulated gene sets related to asthma. We discovered 12 commonly regulated genes in the GSE143303 and GSE64913 datasets and developed a LASSO regression model. The model corresponding to lambda.min selected nine genes, including TFCP2L1, KRT6A, FCER1A, and CCL5, which demonstrated predictive value. These genes were significantly upregulated or under expressed in severe asthma, as validated by RT-qPCR.

CONCLUSION

Mitochondrial abnormalities affecting oxidative phosphorylation play a critical role in severe asthma. Key molecular biomarkers like TFCP2L1, KRT6A, FCER1A, and CCL5, are essential for detecting severe asthma. This research significantly enhances the understanding and diagnosis of severe asthma.

CASC8 activates the pentose phosphate pathway to inhibit disulfidptosis in pancreatic ductal adenocarcinoma though the c-Myc-GLUT1 axis

PURPOSE

Glucose starvation induces the accumulation of disulfides and F-actin collapse in cells with high expression of SLC7A11, a phenomenon termed disulfidptosis. This study aimed to confirm the existence of disulfidptosis in pancreatic ductal adenocarcinoma (PDAC) and elucidate the role of Cancer Susceptibility 8 (CASC8) in this process.

METHODS

The existence of disulfidptosis in PDAC was assessed using flow cytometry and F-actin staining. CASC8 expression and its clinical correlations were analyzed using data from The Cancer Genome Atlas (TCGA) and further verified by chromogenic in situ hybridization assay in PDAC tissues. Cells with CASC8 knockdown and overexpression were subjected to cell viability, EdU, transwell assays, and used to establish subcutaneous and orthotopic tumor models. Disulfidptosis was detected by flow cytometry and immunofluorescence assays. RNA sequencing and metabolomics analysis were performed to determine the metabolic pathways which were significantly affected after CASC8 knockdown. We detected the glucose consumption and the NADP+/NADPH ratio to investigate alterations in metabolic profiles. RNA immunoprecipitation combined with fluorescence in situ hybridization assay was used to identify protein-RNA interactions. Protein stability, western blotting and quantitative real-time PCR assays were performed to reveal potential molecular mechanism.

RESULTS

Disulfidptosis was observed in PDAC and could be significantly rescued by disulfidptosis inhibitors. CASC8 expression was higher in PDAC samples compared to normal pancreatic tissue. High CASC8 expression correlated with a poor prognosis for patients with PDAC and contributed to cancer progression in vitro and in vivo. Furthermore, CASC8 was associated with disulfidptosis resistance under glucose starvation conditions in PDAC. Mechanistically, CASC8 interacted with c-Myc to enhance the stability of c-Myc protein, leading to the activation of the pentose phosphate pathway, a reduction of the NADP+/NADPH ratio and ultimately inhibiting disulfidptosis under glucose starvation conditions.

CONCLUSIONS

This study provides evidence for the existence of disulfidptosis in PDAC and reveals the upregulation of CASC8 in this malignancy. Furthermore, we demonstrate that CASC8 acts as a crucial regulator of the pentose phosphate pathway and disulfidptosis, thereby promoting PDAC progression.

[Construction and Validation of A Prognostic Model for Lung Adenocarcinoma Based on Ferroptosis-related Genes]

BACKGROUND

Ferroptosis-related genes play a crucial role in regulating intracellular iron homeostasis and lipid peroxidation, and they are involved in the regulation of tumor growth and drug resistance. The expression of ferroptosis-related genes in tumor tissues can be used to predict patients' future survival times, aiding doctors and patients in anticipating disease progression. Based on the sequencing data of lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database, this study identified genes involved in the regulation of ferroptosis, constructed a prognostic model, and evaluated the predictive performance of the model.

METHODS

A total of 1467 ferroptosis-related genes were obtained from the GeneCards database. Gene expression profiles and clinical data from 541 LUAD patients were collected from the TCGA database. The expression data of all ferroptosis-related genes were extracted, and differentially expressed genes were identified using R software. Survival analysis was performed on these genes to screen for those with prognostic value. Subsequently, a prognostic risk scoring model for ferroptosis-related genes was constructed using LASSO regression model. Each LUAD patient sample was scored, and the patients were divided into high-risk and low-risk groups based on the median score. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves were generated to assess model performance, followed by validation in an external dataset. Finally, univariate and multivariate Cox regression analyses were conducted to evaluate the independent prognostic value and clinical relevance of the model.

RESULTS

Through survival analysis, 121 ferroptosis-related genes associated with prognosis were initially identified. Based on this, a LUAD prognostic risk scoring model was constructed using 12 ferroptosis-related genes (ALG3, C1QTNF6, CCT6A, GLS2, KRT6A, LDHA, NUPR1, OGFRP1, PCSK9, TRIM6, IGF2BP1 and MIR31HG). The results indicated that patients in the high-risk group had significantly shorter survival time than those in the low-risk group (P<0.001), and the model demonstrated good predictive performance in both the training set (1-yr AUC=0.721) and the external validation set (1-yr AUC=0.768). Risk scores were significantly associated with the prognosis of LUAD patients in both univariate and multivariate Cox regression analyses (P<0.001), suggesting that this score is an important prognostic factor for LUAD patients.

CONCLUSIONS

This study successfully established a LUAD risk scoring model composed of 12 ferroptosis-related genes. In the future, this model is expected to be used in conjunction with the tumor-node-metastasis (TNM) staging system for prognostic predictions in LUAD patients.

Liquid-Liquid Phase Separation in the Prognosis of Lung Adenocarcinoma: An Integrated Analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is a highly lethal malignancy. Liquid- Liquid Phase Separation (LLPS) plays a crucial role in targeted therapies for lung cancer and in the progression of lung squamous cell carcinoma. However, the role of LLPS in the progression and prognosis of LUAD remains insufficiently explored.

METHODS

This study employed a multi-step approach to identify LLPS prognosis-related genes in LUAD. First, differential analysis, univariate Cox regression analysis, Random Survival Forest (RSF) method, and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis were utilized to identify five LLPS prognosis-related genes. Subsequently, LASSO Cox regression was performed to establish a prognostic score termed the LLPS-related prognosis score (LPRS). Comprehensive analyses were then conducted based on the LPRS, including survival analysis, clinical feature analysis, functional enrichment analysis, and tumor microenvironment assessment. The LPRS was integrated with additional clinicopathological factors to develop a prognostic nomogram for LUAD patients. Immunohistochemical validation was performed on clinical tissue samples to further validate the findings. Finally, the relationship between KRT6A, one of the identified genes, and epidermal growth factor receptor (EGFR) mutations was investigated.

RESULTS

The LPRS was established using five LLPS-related genes: IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. Higher LPRS was closely associated with poor survival outcomes, gender, progression-free survival (PFS), and advanced TNM stage. Furthermore, LPRS emerged as an independent prognostic factor for LUAD. A nomogram integrating LPRS, TNM stage, and age demonstrated remarkable predictive accuracy for prognosis among patients with LUAD. LLPS likely influences LUAD prognosis through the activity of IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. KRT6A exhibits significant upregulation in LUAD, particularly in patients with EGFR mutations.

CONCLUSION

This study introduces a novel LPRS model that demonstrates high accuracy in predicting the clinical prognosis of LUAD. Moreover, the findings suggest that KRT6A may play a critical role in the LLPS-mediated malignant progression of LUAD.

Metabolic reprogramming, sensing, and cancer therapy

The metabolic reprogramming of tumor cells is a crucial strategy for their survival and proliferation, involving tissue- and condition-dependent remodeling of certain metabolic pathways. While it has become increasingly clear that tumor cells integrate extracellular and intracellular signals to adapt and proliferate, nutrient and metabolite sensing also exert direct or indirect influences, although the underlying mechanisms remain incompletely understood. Furthermore, metabolic changes not only support the rapid growth and dissemination of tumor cells but also promote immune evasion by metabolically "educating" immune cells in the tumor microenvironment (TME). Recent studies have highlighted the profound impact of metabolic reprogramming on the TME and the potential of targeting metabolic pathways as a therapeutic strategy, with several enzyme inhibitors showing promising results in clinical trials. Thus, understanding how tumor cells alter their metabolic pathways and metabolically remodel the TME to support their survival and proliferation may offer new strategies for metabolic therapy and immunotherapy.

Knockdown of Keratin 6 Within Arsenite-Transformed Human Urothelial Cells Decreases Basal/Squamous Expression, Inhibits Growth, and Increases Cisplatin Sensitivity

Urothelial carcinoma (UC) is prevalent, especially in elderly males. The high rate of recurrence, treatment regime, and follow-up monitoring make UC a global health and economic burden. Arsenic is a ubiquitous toxicant that can be found in drinking water, and it is known that exposure to arsenic is associated with UC development. Around 25% of diagnosed UC cases are muscle-invasive (MIUC) which have poor prognosis and develop chemoresistance, especially if tumors are associated with squamous differentiation (SD). The immortalized UROtsa cell line is derived from normal human urothelium and our lab has malignantly transformed these cells using arsenite (As3+). These cells represent a basal subtype model of MIUC and the tumors derived from the As3+-transformed cells histologically and molecularly resemble clinical cases of the basal subtype of MIUC that have focal areas SD and expression of the basal keratins (KRT1, 5, 6, 14, and 16). Our previous data demonstrate that KRT6 protein expression correlates to areas of SD within the tumors. For this study, we performed a lentiviral knockdown of KRT6 in As3+-transformed UROtsa cells to evaluate the effects on morphology, gene/protein expression, growth, colony formation, and cisplatin sensitivity. The knockdown of KRT6 resulted in decreased expression of the basal keratins, decreased growth, decreased colony formation, and increased sensitivity to cisplatin, the standard treatment for MIUC. The results of this study suggest that KRT6 plays a role in UC cell growth and is an exploitable target to increase cisplatin sensitivity for MIUC tumors that may have developed resistance to cisplatin treatment.

Deciphering the Landscape of GATA-Mediated Transcriptional Regulation in Gastric Cancer

Gastric cancer (GC) is an asymptomatic malignancy in early stages, with an invasive and cost-ineffective diagnostic toolbox that contributes to severe global mortality rates on an annual basis. Ectopic expression of the lineage survival transcription factors (LS-TFs) GATA4 and 6 promotes stomach oncogenesis. However, LS-TFs also govern important physiological roles, hindering their direct therapeutic targeting. Therefore, their downstream target genes are particularly interesting for developing cancer-specific molecular biomarkers or therapeutic agents. In this work, we couple inducible knockdown systems with chromatin immunoprecipitation and RNA-seq to thoroughly detect and characterize direct targets of GATA-mediated transcriptional regulation in gastric cancer cells. Our experimental and computational strategy provides evidence that both factors regulate the expression of several coding and non-coding RNAs that in turn mediate for their cancer-promoting phenotypes, including but not limited to cell cycle, apoptosis, ferroptosis, and oxidative stress response. Finally, the diagnostic and prognostic potential of four metagene signatures consisting of selected GATA4/6 target transcripts is evaluated in a multi-cancer panel of ~7000 biopsies from nineteen tumor types, revealing elevated specificity for gastrointestinal tumors. In conclusion, our integrated strategy uncovers the landscape of GATA-mediated coding and non-coding transcriptional regulation, providing insights regarding their molecular and clinical function in gastric cancer.

Fatty acid metabolism prognostic signature predicts tumor immune microenvironment and immunotherapy, and identifies tumorigenic role of MOGAT2 in lung adenocarcinoma

Background

Aberrant fatty acid metabolism (FAM) plays a critical role in the tumorigenesis of human malignancies. However, studies on its impact in lung adenocarcinoma (LUAD) are limited.

Methods

We developed a prognostic signature comprising 10 FAM-related genes (GPR115, SOAT2, CDH17, MOGAT2, COL11A1, TCN1, LGR5, SLC34A2, RHOV, and DKK1) using data from LUAD patients in The Cancer Genome Atlas (TCGA). This signature was validated using six independent LUAD datasets from the Gene Expression Omnibus (GEO). Patients were classified into high- and low-risk groups, and overall survival (OS) was compared by Kaplan-Meier analysis. The signature's independence as a prognostic indicator was assessed after adjusting for clinicopathological features. Receiver operating characteristic (ROC) analysis validated the signature. Tumor immune microenvironment (TIME) was analyzed using ESTIMATE and multiple deconvolution algorithms. Functional assays, including CCK8, cell cycle, apoptosis, transwell, and wound healing assays, were performed on MOGAT2-silenced H1299 cells using CRISPR/Cas9 technology.

Results

Low-risk group patients exhibited decreased OS. The signature was an independent prognostic indicator and demonstrated strong risk-stratification utility for disease relapse/progression. ROC analysis confirmed the signature's validity across validation sets. TIME analysis revealed higher infiltration of CD8+ T cells, natural killers, and B cells, and lower tumor purity, stemness index, and tumor mutation burden (TMB) in low-risk patients. These patients also showed elevated T cell receptor richness and diversity, along with reduced immune cell senescence. High-risk patients exhibited enrichment in pathways related to resistance to immune checkpoint blockades, such as DNA repair, hypoxia, epithelial-mesenchymal transition, and the G2M checkpoint. LUAD patients receiving anti-PD-1 treatment had lower risk scores among responders compared to non-responders. MOGAT2 was expressed at higher levels in low-risk LUAD patients. Functional assays revealed that MOGAT2 knockdown in H1299 cells promoted proliferation and migration, induced G2 cell cycle arrest, and decreased apoptosis.

Conclusions

This FAM-related gene signature provides a valuable tool for prognostic stratification and monitoring of TIME and immunotherapy responses in LUAD. MOGAT2 is identified as a potential anti-tumor regulator, offering new insights into its role in LUAD pathogenesis.

Assessment of Concentration KRT6 Proteins in Tumor and Matching Surgical Margin from Patients with Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinomas (HNSCCs) are one of the most frequently detected cancers in the world; not all mechanisms related to the expression of keratin in this type of cancer are known. The aim of this study was to evaluate type II cytokeratins (KRT): KRT6A, KRT6B, and KRT6C protein concentrations in 54 tumor and margin samples of head and neck squamous cell carcinoma (HNSCC). Moreover, we examined a possible association between protein concentration and the clinical and demographic variables. Protein concentrations were measured using enzyme-linked immunosorbent assay (ELISA). Significantly higher KRT6A protein concentration was found in HNSCC samples compared to surgical margins. An inverse relationship was observed for KRT6B and KRT6C proteins. We showed an association between the KRT6C protein level and clinical parameters T and N in tumor and margin samples. When analyzing the effect of smoking and drinking on KRT6A, KRT6B, and KRT6C levels, we demonstrated a statistically significant difference between regular or occasional tobacco and alcohol habits and patients who do not have any tobacco and alcohol habits in tumor and margin samples. Moreover, we found an association between KRT6B and KRT6C concentration and proliferative index Ki-67 and HPV status in tumor samples. Our results showed that concentrations of KRT6s were different in the tumor and the margin samples and varied in relation to clinical and demographic parameters. We add information to the current knowledge about the role of KRT6s isoforms in HNSCC. We speculate that variations in the studied isoforms of the KRT6 protein could be due to the presence and development of the tumor and its microenvironment. It is important to note that the analyses were performed in tumor and surgical margins and can provide more accurate information on the function in normal and cancer cells and regulation in response to various factors.

Keratin 6A Is Expressed at the Invasive Front and Enhances the Progression of Colorectal Cancer

Keratins (KRTs) are intermediate filament proteins in epithelial cells, and they are important for cytoskeletal organization. KRT6A, classified as a type II KRT, is normally expressed in stratified squamous epithelium and squamous cell carcinomas. Little is known about the expression and role of KRT6A in adenocarcinomas. We investigated the clinicopathologic and molecular biological significance of KRT6A in colorectal adenocarcinoma. Immunostaining of colorectal adenocarcinoma cases treated at our institution demonstrated that KRT6A showed significantly stronger expression at the invasive front than that at the tumor center (P < .0001). The high KRT6A-expression cases (n = 47) tended to have a high budding grade associated with significantly worse prognoses. A multivariate analysis revealed that the KRT6A expression status was an independent prognostic factor for overall survival (P = .0004), disease-specific survival (P = .0097), and progression-free survival (P = .0033). The correlation between KRT6A and patient prognoses was also validated in an external cohort from a published data set. To determine the function of KRT6A in vitro, KRT6A was overexpressed in 3 colon cancer cell lines: DLD-1, SW620, and HCT 116. KRT6A overexpression increased migration and invasion in DLD-1 but did not in SW620 and HCT116. In 3-dimensional sphere-forming culture, KRT6A expression enhanced the irregular protrusion around the spheroid in DLD-1. Our findings in this study indicated that KRT6A expression is a valuable prognostic marker of colorectal cancer and KRT6A may be involved the molecular mechanism in the progression of invasive areas of colorectal cancer.

Identification of subgroups and development of prognostic risk models along the glycolysis-cholesterol synthesis axis in lung adenocarcinoma

Lung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. A dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. We compared the prognosis of different subtypes and identified differentially expressed genes between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation analysis. A risk score model was built based on multivariate Cox analysis. LUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. Patients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients. The model was robust on the training and test datasets and had a great predictive performance.

Energy metabolism as the hub of advanced non-small cell lung cancer management: a comprehensive view in the framework of predictive, preventive, and personalized medicine

Energy metabolism is a hub of governing all processes at cellular and organismal levels such as, on one hand, reparable vs. irreparable cell damage, cell fate (proliferation, survival, apoptosis, malignant transformation etc.), and, on the other hand, carcinogenesis, tumor development, progression and metastazing versus anti-cancer protection and cure. The orchestrator is the mitochondria who produce, store and invest energy, conduct intracellular and systemically relevant signals decisive for internal and environmental stress adaptation, and coordinate corresponding processes at cellular and organismal levels. Consequently, the quality of mitochondrial health and homeostasis is a reliable target for health risk assessment at the stage of reversible damage to the health followed by cost-effective personalized protection against health-to-disease transition as well as for targeted protection against the disease progression (secondary care of cancer patients against growing primary tumors and metastatic disease). The energy reprogramming of non-small cell lung cancer (NSCLC) attracts particular attention as clinically relevant and instrumental for the paradigm change from reactive medical services to predictive, preventive and personalized medicine (3PM). This article provides a detailed overview towards mechanisms and biological pathways involving metabolic reprogramming (MR) with respect to inhibiting the synthesis of biomolecules and blocking common NSCLC metabolic pathways as anti-NSCLC therapeutic strategies. For instance, mitophagy recycles macromolecules to yield mitochondrial substrates for energy homeostasis and nucleotide synthesis. Histone modification and DNA methylation can predict the onset of diseases, and plasma C7 analysis is an efficient medical service potentially resulting in an optimized healthcare economy in corresponding areas. The MEMP scoring provides the guidance for immunotherapy, prognostic assessment, and anti-cancer drug development. Metabolite sensing mechanisms of nutrients and their derivatives are potential MR-related therapy in NSCLC. Moreover, miR-495-3p reprogramming of sphingolipid rheostat by targeting Sphk1, 22/FOXM1 axis regulation, and A2 receptor antagonist are highly promising therapy strategies. TFEB as a biomarker in predicting immune checkpoint blockade and redox-related lncRNA prognostic signature (redox-LPS) are considered reliable predictive approaches. Finally, exemplified in this article metabolic phenotyping is instrumental for innovative population screening, health risk assessment, predictive multi-level diagnostics, targeted prevention, and treatment algorithms tailored to personalized patient profiles-all are essential pillars in the paradigm change from reactive medical services to 3PM approach in overall management of lung cancers. This article highlights the 3PM relevant innovation focused on energy metabolism as the hub to advance NSCLC management benefiting vulnerable subpopulations, affected patients, and healthcare at large.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00357-5.

Microbial Metagenomes and Host Transcriptomes Reveal the Dynamic Changes of Rumen Gene Expression, Microbial Colonization and Co-Regulation of Mineral Element Metabolism in Yaks from Birth to Adulthood

Yaks are the main pillar of plateau animal husbandry and the material basis of local herdsmen's survival. The level of mineral elements in the body is closely related to the production performance of yaks. In this study, we performed a comprehensive analysis of rumen epithelial morphology, transcriptomics and metagenomics to explore the dynamics of rumen functions, microbial colonization and functional interactions in yaks from birth to adulthood. Bacteria, eukaryotes, archaea and viruses colonized the rumen of yaks from birth to adulthood, with bacteria being the majority. Bacteroidetes and Firmicutes were the dominant phyla in five developmental stages, and the abundance of genus Lactobacillus and Fusobacterium significantly decreased with age. Glycoside hydrolase (GH) genes were the most highly represented in five different developmental stages, followed by glycosyltransferases (GTs) and carbohydrate-binding modules (CBMs), where the proportion of genes coding for CBMs increased with age. Integrating host transcriptome and microbial metagenome revealed 30 gene modules related to age, muscle layer thickness, nipple length and width of yaks. Among these, the MEmagenta and MEturquoise were positively correlated with these phenotypic traits. Twenty-two host genes involved in transcriptional regulation related to metal ion binding (including potassium, sodium, calcium, zinc, iron) were positively correlated with a rumen bacterial cluster 1 composed of Alloprevotella, Paludibacter, Arcobacter, Lactobacillus, Bilophila, etc. Therefore, these studies help us to understand the interaction between rumen host and microorganisms in yaks at different ages, and further provide a reliable theoretical basis for the development of feed and mineral element supplementation for yaks at different ages.

Six-Gene Signature for Differential Diagnosis and Therapeutic Decisions in Non-Small-Cell Lung Cancer-A Validation Study

Non-small-cell lung cancer (NSCLC) poses a challenge due to its heterogeneity, necessitating precise histopathological subtyping and prognostication for optimal treatment decision-making. Molecular markers emerge as a potential solution, overcoming the limitations of conventional methods and supporting the diagnostic-therapeutic interventions. In this study, we validated the expression of six genes (MIR205HG, KRT5, KRT6A, KRT6C, SERPINB5, and DSG3), previously identified within a 53-gene signature developed by our team, utilizing gene expression microarray technology. Real-time PCR on 140 thoroughly characterized early-stage NSCLC samples revealed substantial upregulation of all six genes in squamous cell carcinoma (SCC) compared to adenocarcinoma (ADC), regardless of clinical factors. The decision boundaries of the logistic regression model demonstrated effective separation of the relative expression levels between SCC and ADC for most genes, excluding KRT6C. Logistic regression and gradient boosting decision tree classifiers, incorporating all six validated genes, exhibited notable performance (AUC: 0.8930 and 0.8909, respectively) in distinguishing NSCLC subtypes. Nevertheless, our investigation revealed that the gene expression profiles failed to yield predictive value regarding the progression of early-stage NSCLC. Our molecular diagnostic models manifest the potential for an exhaustive molecular characterization of NSCLC, subsequently informing personalized treatment decisions and elevating the standards of clinical management and prognosis for patients.

A Novel High-Dimensional Kernel Joint Non-Negative Matrix Factorization With Multimodal Information for Lung Cancer Study

Judging and identifying biological activities and biomarkers inside tissues from imaging features of diseases is challenging, so correlating pathological image data with genes inside organisms is of great significance for clinical diagnosis. This paper proposes a high-dimensional kernel non-negative matrix factorization (NMF) method based on muti-modal information fusion. This algorithm can project RNA gene expression data and pathological images (WSI) into a common feature space, where the heterogeneous variables with the largest coefficient in the same projection direction form a co-module. In addition, the miRNA-mRNA and miRNA-lncRNA interaction networks in the ceRNA network are added to the algorithm as a priori information to explore the relationship between the images and the internal activities of the gene. Furthermore, the radial basis kernel function is used to calculate the feature proportion between different kinds of genes mapped in the high-dimensional feature space and projected into the common feature space to explore the gene interaction in the high-dimensional situation. The original feature matrix is regularized to improve biological correlation, and the feature factors are sparse by orthogonal constraints to reduce redundancy. Experimental results show that the proposed NMF method is better than the traditional NMF method in stability, decomposition accuracy, and robustness. Through data analysis applied to lung cancer, genes related to tissue morphology are found, such as COL7A1, CENPF and BIRC5. In addition, gene pairs with a correlation degree exceeding 0.8 are found, and potential biomarkers of significant correlation with survival are obtained such as CAPN8. It has potential application value for the clinical diagnosis of lung cancer.

HDAC6-MYCN-CXCL3 axis mediates allergic inflammation and is necessary for allergic inflammation-promoted cellular interactions

Histone deacetylase 6 (HDAC6) has been shown to play an important role in allergic inflammation. This study hypothesized that novel downstream targets of HDAC6 would mediate allergic inflammation. Experiments employing HDAC6 knock out C57BL/6 mice showed that HDAC6 mediated passive cutaneous anaphylaxis (PCA) and passive systemic anaphylaxis (PSA). Antigen stimulation increased expression of N-myc (MYCN) and CXCL3 in an HDAC6-dependent manner in the bone marrow-derived mast cells. MYCN and CXCL3 were necessary for both PCA and PSA. The role of early growth response 3 (EGR3) in the regulation of HDAC6 expression has been reported. ChIP assays showed EGR3 as a direct regulator of MYCN. miR-34a-5p was predicted to be a negative regulator of MYCN. Luciferase activity assays showed miR-34a-5p as a direct regulator of MYCN. miR-34a-5p mimic negatively regulated PCA and PSA. MYCN decreased miR-34a-5p expression in antigen-stimulated rat basophilic leukemia cells (RBL2H3). MYCN was shown to bind to the promoter sequence of CXCL3. In an IgE-independent manner, recombinant CXCL3 protein increased expression of HDAC6, MYCN, and β-hexosaminidase activity in RBL2H3 cells. Mouse recombinant CXCL3 protein enhanced the angiogenic potential of the culture medium of RBL2H3. CXCL3 was necessary for the enhanced angiogenic potential of the culture medium of antigen-stimulated RBL2H3. The culture medium of RBL2H3 was able to induce M2 macrophage polarization in a CXCL3-dependent manner. Recombinant CXCL3 protein also increased the expression of markers of M2 macrophage. Thus, the identification of the novel role of HDAC6-MYCN-CXCL3 axis can help better understand the pathogenesis of anaphylaxis.

Metabolic Signature of Warburg Effect in Cancer: An Effective and Obligatory Interplay between Nutrient Transporters and Catabolic/Anabolic Pathways to Promote Tumor Growth

Aerobic glycolysis in cancer cells, originally observed by Warburg 100 years ago, which involves the production of lactate as the end product of glucose breakdown even in the presence of adequate oxygen, is the foundation for the current interest in the cancer-cell-specific reprograming of metabolic pathways. The renewed interest in cancer cell metabolism has now gone well beyond the original Warburg effect related to glycolysis to other metabolic pathways that include amino acid metabolism, one-carbon metabolism, the pentose phosphate pathway, nucleotide synthesis, antioxidant machinery, etc. Since glucose and amino acids constitute the primary nutrients that fuel the altered metabolic pathways in cancer cells, the transporters that mediate the transfer of these nutrients and their metabolites not only across the plasma membrane but also across the mitochondrial and lysosomal membranes have become an integral component of the expansion of the Warburg effect. In this review, we focus on the interplay between these transporters and metabolic pathways that facilitates metabolic reprogramming, which has become a hallmark of cancer cells. The beneficial outcome of this recent understanding of the unique metabolic signature surrounding the Warburg effect is the identification of novel drug targets for the development of a new generation of therapeutics to treat cancer.

Deciphering head and neck cancer microenvironment: Single-cell and spatial transcriptomics reveals human papillomavirus-associated differences

Human papillomavirus (HPV) is a major causative factor of head and neck squamous cell carcinoma (HNSCC), and the incidence of HPV- associated HNSCC is increasing. The role of tumor microenvironment in viral infection and metastasis needs to be explored further. We studied the molecular characteristics of primary tumors (PTs) and lymph node metastatic tumors (LNMTs) by stratifying them based on their HPV status. Eight samples for single-cell RNA profiling and six samples for spatial transcriptomics (ST), composed of matched primary tumors (PT) and lymph node metastases (LNMT), were collected from both HPV- negative (HPV- ) and HPV-positive (HPV+ ) patients. Using the 10x Genomics Visium platform, integrative analyses with single-cell RNA sequencing were performed. Intracellular and intercellular alterations were analyzed, and the findings were confirmed using experimental validation and publicly available data set. The HPV+ tissues were composed of a substantial amount of lymphoid cells regardless of the presence or absence of metastasis, whereas the HPV- tissue exhibited remarkable changes in the number of macrophages and plasma cells, particularly in the LNMT. From both single-cell RNA and ST data set, we discovered a central gene, pyruvate kinase muscle isoform 1/2 (PKM2), which is closely associated with the stemness of cancer stem cell-like populations in LNMT of HPV- tissue. The consistent expression was observed in HPV- HNSCC cell line and the knockdown of PKM2 weakened spheroid formation ability. Furthermore, we found an ectopic lymphoid structure morphology and clinical effects of the structure in ST slide of the HPV+ patients and verified their presence in tumor tissue using immunohistochemistry. Finally, the ephrin-A (EPHA2) pathway was detected as important signals in angiogenesis for HPV- patients from single-cell RNA and ST profiles, and knockdown of EPHA2 declined the cell migration. Our study described the distinct cellular composition and molecular alterations in primary and metastatic sites in HNSCC patients based on their HPV status. These results provide insights into HNSCC biology in the context of HPV infection and its potential clinical implications.

Sinapine thiocyanate exhibited anti-colorectal cancer effects by inhibiting KRT6A/S100A2 axis

Sinapine thiocyanate (ST), an alkaloid existed extensively in seeds of cruciferous plants, exhibits a number of pharmacological effects, including anti-inflammatory and anti-malignancy properties. However, it is still unknown what effects and molecular mechanisms ST has on colorectal cancer (CRC). In the current study, it was indicated that ST inhibited proliferation, colony formation, and apoptosis in vitro, as well as arrested the G1 phase of CRC cells. There was a significant repressive effects of ST on invasion and migration of CRC cells in vitro. RNA-sequencing indicated that 750 differentially expressed genes existed in CRC cells after ST treatment, and enrichment analysis demonstrated that ST obviously decreased the activation of keratinization pathways. Among DEGs enriched in keratinization, keratin 6A (KRT6A) was decreased the most significant, as well as its target gene S100 calcium-binding protein A2 (S100A2). Low expression of KRT6A and S100A2 signatures indicated a favorable prognosis in CRC patients. Moreover, we found overexpression of KRT6A relieved the inhibitory effects of ST in CRC cells. Furthermore, ST inhibited the CRC cell proliferation in vivo, and reduced KRT6A and KI67 expression in xenograft tumor. Taken together, we demonstrated that ST exhibited anti-CRC properties by inhibiting KRT6A/S100A2 axis. It is possible that ST can be used as a treatment for CRC.

Characterization of genomic instability-related genes predicts survival and therapeutic response in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and is the leading cause of cancer death worldwide. Its progression is characterized by genomic instability. In turn, the level of genomic instability affects the prognosis and immune status of patients with LUAD. However, the impact of molecular features associated with genomic instability on the tumor microenvironment (TME) has not been well characterized. In addition, the effect of the genes related to genomic instability in LUAD on individualized treatment of LUAD is unknown.

METHODS

The RNA-Sequencing, somatic mutation, and clinical data of LUAD patients were downloaded from publicly available databases. A genetic signature associated with genomic instability (GSAGI) was constructed by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Bioinformatics analysis investigated the differences in prognosis, immune characteristics, and the most appropriate treatment strategy among different subtypes of LUAD patients. CCK-8 and colony formation verified the various effects of Etoposide on different subtypes of LUAD cell lines. Cell-to-cell communication analysis was performed using the "CellChat" R package. The expression of the risk factors in the GSAGI was verified using real-time quantitative PCR (qRT-PCR) and Immunohistochemistry (IHC).

RESULTS

We constructed and validated the GSAGI, consisting of five genes: ANLN, RHOV, KRT6A, SIGLEC6, and KLRG2. The GSAGI was an independent prognostic factor for LUAD patients. Patients in the high-risk group distinguished by the GSAGI are more suitable for chemotherapy. More immune cells are infiltrating the tumor microenvironment of patients in the low-risk group, especially B cells. Low-risk group patients are more suitable for receiving immunotherapy. The single-cell level analysis confirmed the influence of the GSAGI on TME and revealed the Mode of action between tumor cells and other types of cells. qRT-PCR and IHC showed increased ANLN, RHOV, and KRT6A expression in the LUAD cells and tumor tissues.

CONCLUSION

This study confirms that genes related to genomic instability can affect the prognosis and immune status of LUAD patients. The GSAGI we identified has the potential to guide clinicians in predicting clinical outcomes, assessing immunological status, and even developing personalized treatment plans for LUAD patients.

MAPK1 promotes the metastasis and invasion of gastric cancer as a bidirectional transcription factor

BACKGROUND

The Mitogen-activated protein kinase 1 (MAPK1) has both independent functions of phosphorylating histones as a kinase and directly binding the promoter regions of genes to regulate gene expression as a transcription factor. Previous studies have identified elevated expression of MAPK1 in human gastric cancer, which is associated with its role as a kinase, facilitating the migration and invasion of gastric cancer cells. However, how MAPK1 binds to its target genes as a transcription factor and whether it modulates related gene expressions in gastric cancer remains unclear.

RESULTS

Here, we integrated biochemical assays (protein interactions and chromatin immunoprecipitation (ChIP)), cellular analysis assays (cell proliferation and migration), RNA sequencing, ChIP sequencing, and clinical analysis to investigate the potential genomic recognition patterns of MAPK1 in a human gastric adenocarcinoma cell-line (AGS) and to uncover its regulatory effect on gastric cancer progression. We confirmed that MAPK1 promotes AGS cells invasion and migration by regulating the target genes in different directions, up-regulating seven target genes (KRT13, KRT6A, KRT81, MYH15, STARD4, SYTL4, and TMEM267) and down-regulating one gene (FGG). Among them, five genes (FGG, MYH15, STARD4, SYTL4, and TMEM267) were first associated with cancer procession, while the other three (KRT81, KRT6A, and KRT13) have previously been confirmed to be related to cancer metastasis and migration.

CONCLUSION

Our data showed that MAPK1 can bind to the promoter regions of these target genes to control their transcription as a bidirectional transcription factor, promoting AGS cell motility and invasion. Our research has expanded the understanding of the regulatory roles of MAPK1, enriched our knowledge of transcription factors, and provided novel candidates for cancer therapeutics.

Cytosolic malic enzyme and glucose-6-phosphate dehydrogenase modulate redox balance in NSCLC with acquired drug resistance

Lung cancer cells often show elevated levels of reactive oxygen species (ROS) and nicotinamide adenine dinucleotide phosphate (NADPH). However, the connections between deregulated redox homeostasis in different subtypes of lung cancer and acquired drug resistance in lung cancer have not yet been fully established. Herein, we analyzed different subtypes of lung cancer data reported in the Cancer Cell Line Encyclopedia (CCLE) database, the Cancer Genome Atlas program (TCGA), and the sequencing data obtained from a gefitinib-resistant non-small-cell lung cancer (NSCLC) cell line (H1975GR). Using flux balance analysis (FBA) model integrated with multiomics data and gene expression profiles, we identified cytosolic malic enzyme 1 (ME1) and glucose-6-phosphate dehydrogenase as the major contributors to the significantly upregulated NADPH flux in NSCLC tissues as compared with normal lung tissues, and gefitinib-resistant NSCLC cell line as compared with the parental cell line. Silencing the gene expression of either of these two enzymes in two osimertinib-resistant NSCLC cell lines (H1975OR and HCC827OR) exhibited strong antiproliferative effects. Our findings not only underscored the pivotal roles of cytosolic ME1 and glucose-6-phosphate dehydrogenase in regulating redox states in NSCLC cells but also provided novel insights into their potential roles in drug-resistant NSCLC cells with disturbed redox states.

Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation

Background

Disulfidptosis is a newly identified variant of cell death characterized by disulfide accumulation, which is independent of ATP depletion. Accordingly, the latent influence of disulfidptosis on the prognosis of lung adenocarcinoma (LUAD) patients and the progression of tumors remains poorly understood.

Methods

We conducted a multifaceted analysis of the transcriptional and genetic modifications in disulfidptosis regulators (DRs) specific to LUAD, followed by an evaluation of their expression configurations to define DR clusters. Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). Patients were subsequently stratified into high and low DS cohorts based on median DS values. We then performed an exhaustive comparison between these cohorts, focusing on somatic mutations, clinical attributes, tumor microenvironment, and treatment responsiveness. Finally, we empirically validated the biological implications of a critical gene, KYNU, through assays in LUAD cell lines.

Results

We identified two DR clusters and there were great differences in overall survival (OS) and tumor microenvironment. We selected the "Least Absolute Shrinkage and Selection Operator (LASSO) + Random Survival Forest (RFS)" algorithm to develop a DS based on the average C-index across different cohorts. Our model effectively stratified LUAD patients into high- and low-DS subgroups, with this latter demonstrating superior OS, a reduced mutational landscape, enhanced immune status, and increased sensitivity to immunotherapy. Notably, the predictive accuracy of DS outperformed the published LUAD signature and clinical features. Finally, we validated the DS expression using clinical samples and found that inhibiting KYNU suppressed LUAD cells proliferation, invasiveness, and migration in vitro.

Conclusions

The DR-based scoring system that we developed enabled accurate prognostic stratification of LUAD patients and provides important insights into the molecular mechanisms and treatment strategies for LUAD.

Identification and comprehensive analysis of super-enhancer related genes involved in epithelial-to-mesenchymal transition in lung adenocarcinoma

Lung adenocarcinoma is a disease with a high mortality rate, and its mechanism is still unclear. Super-enhancers play an important role in gene expression and also affect the occurrence and development of lung adenocarcinoma, so more and more people pay attention to them. In order to explore the influence of super-enhancer related genes on tumor development, we identified super-enhancer regulated genes related to Epithelial-to-mesenchymal transition (EMT). By analyzing the single-cell sequencing data and the TCGA database of lung adenocarcinoma, we suggest that the up-regulation of TMSB10 in lung adenocarcinoma and its association with poor prognosis may be due to the regulation of super-enhancers during tumor cell metastasis. Using the TCGA lung adenocarcinoma data set, the samples were divided into TMSB10 high-expression group and low-expression group, and it was found that there were significant differences in immune infiltration between the high-expression group and the low-expression group. We parted 513 samples into eight TMSB10-related molecular subtypes using differentially expressed genes of high and low TMSB10 expression groups. We concentrated on four molecular subtypes with the most significant clusters, each with its own characteristics in terms of Immune cell infiltration, prognosis, or pathological stages. In order to predict the four molecular subtypes, we established a prediction model using random forest, and the external test results showed that the prediction accuracy of the model was 0.87. This study may provide potential help for the study of the mechanism of metastasis and invasion of lung adenocarcinoma cells and personalized treatment of lung adenocarcinoma.

KRTA6A and FA2H Are Hub Genes Associated With Cgas-STING-related Immunogenic Cell Death in Lung Adenocarcinoma

BACKGROUND/AIM

The immunogenic cell death (ICD) pathway plays a crucial prognostic role in lung adenocarcinoma (LUAD) therapy. The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway is an upstream mechanism that drives ICD activation, but the interaction of hub genes remains unclear. The present study aimed to investigate the hub genes involved in ICD and the cGAS-STING pathway. The prognostic performance for hub genes and related Gene Ontology (GO) terms were also investigated.

MATERIALS AND METHODS

Gene expression data of ICD induction and cGAS-STING pathway activation samples were extracted from the Gene Expression Omnibus (GEO) database, and gene expression as well as clinical data of LUAD patients who received pharmaceutical therapy were extracted from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified, and protein-protein interaction (PPI) analysis was used to identify hub genes. Hazard risk (HR) scores were identified using Kaplan-Meier (K-M) and COX analyses. Gene set enrichment analysis (GSEA) was performed to identify the related GO terms, and receiver operating characteristic (ROC) analysis was used to evaluate the prognosis performance of the related gene sets.

RESULTS

A total of 22 DEGs were identified in the two GEO datasets, and six hub genes were identified by PPI analysis. Keratin 6A (KRTA6A) and fatty acid 2-hydroxylase (FA2H) were selected as the hub genes after survival analysis. GSEA and ROC analysis indicated that there was no difference between the KRTA6A and FA2H gene sets on prognosis performance.

CONCLUSION

KRTA6A and FA2H are hub genes associated with the induction of cGAS-STING-related ICD in LUAD.

Analysis of a large cohort of pancreatic cancer transcriptomic profiles to reveal the strongest prognostic factors

Pancreatic adenocarcinoma remains a leading cause of cancer-related deaths. In order to develop appropriate therapeutic and prognostic tools, a comprehensive mapping of the tumor's molecular abnormalities is essential. Here, our aim was to integrate available transcriptomic data to uncover genes whose elevated expression is simultaneously linked to cancer pathogenesis and inferior survival. A comprehensive search was performed in GEO to identify clinical studies with transcriptome-level gene expression data of pancreatic carcinoma with overall survival data and normal pancreatic tissues. After quantile normalization, the entire database was used to identify genes with altered expression. Cox proportional hazard regression was employed to uncover genes most strongly correlated with survival with a Bonferroni corrected p < 0.01. Perturbed biological processes and molecular pathways were identified to enable the understanding of underlying processes. A total of 16 available datasets were combined. The aggregated database comprised data of 1640 samples for 20,443 genes. When comparing with normal pancreatic tissues, a total of 2612 upregulated and 1977 downregulated genes were uncovered in pancreatic carcinoma. Among these, we found 24 genes with higher expression which significantly correlated with overall survival length also. The most significant genes were ANXA8, FAM83A, KRT6A, MET, MUC16, NT5E, and SLC2A1. These genes remained significant after a multivariate analysis also including grade and stage. Here, we assembled a large-scale database of pancreatic carcinoma samples and used this cohort to identify carcinoma-specific genes linked to altered survival outcomes. As our analysis focused on genes with higher expression, these could serve as future therapy targets.

The role of molecular subtypes and immune infiltration characteristics based on disulfidptosis-associated genes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer which accounts for about 40% of all lung cancers. Early detection, risk stratification and treatment are important for improving outcomes for LUAD. Recent studies have found that abnormal accumulation of cystine and other disulfide occurs in the cell under glucose starvation, which induces disulfide stress and increases the content of disulfide bond in actin cytoskeleton, resulting in cell death, which is defined as disulfidptosis. Because the study of disulfidptosis is in its infancy, its role in disease progression is still unclear. In this study, we detected the expression and mutation of disulfidptosis genes in LUAD using a public database. Clustering analysis based on disulfidptosis gene was performed and differential genes of disulfidptosis subtype were analyzed. 7 differential genes of disulfidptosis subtype were used to construct a prognostic risk model, and the causes of prognostic differences were investigated by immune-infiltration analysis, immune checkpoint analysis, and drug sensitivity analysis. qPCR was used to verify the expression of 7 key genes in lung cancer cell line (A549) and normal bronchial epithelial cell line (BEAS-2B). Since G6PD had the highest risk factor of lung cancer, we further verified the protein expression of G6PD in lung cancer cells by western blot, and confirmed through colony formation experiment that interference with G6PD was able to significantly inhibit the proliferation ability of lung cancer cells. Our results provide evidence for the role of disulfidptosis in LUAD and provide new ideas for individualized precision therapy of LUAD.

Cuproptosis Depicts Immunophenotype and Predicts Immunotherapy Response in Lung Adenocarcinoma

BACKGROUND

Although significant progress has been made in immunotherapy for lung adenocarcinoma (LUAD), there is an urgent need to identify effective indicators to screen patients who are suitable for immunotherapy. Systematically investigating the cuproptosis-related genes (CRGs) in LUAD may provide new ideas for patients' immunotherapy stratification.

METHOD

We comprehensively analyzed the landscape of 12 CRGs in a merged TCGA and GEO LUAD cohort. We investigated the associations between tumor microenvironment and immunophenotypes. We utilized a risk score to predict the prognosis and immunotherapy response for an individual patient. Additionally, we conducted CCK-8 experiments to evaluate the impact of DLGAP5 knockdown on A549 cell proliferation.

RESULT

We utilized an integrative approach to analyze 12 CRGs and differentially expressed genes (DEGs) in LUAD samples, resulting in the identification of two distinct CRG clusters and two gene clusters. Based on these clusters, we generated immunophenotypes and observed that the inflamed phenotype had the most abundant immune infiltrations, while the desert phenotype showed the poorest immune infiltrations. We then developed a risk score model for individual patient prognosis and immunotherapy response prediction. Patients in the low-risk group had higher immune scores and ESTIMATE scores, indicating an active immune state with richer immune cell infiltrations and higher expression of immune checkpoint genes. Moreover, the low-risk group exhibited better immunotherapy response according to IPS, TIDE scores, and Imvigor210 cohort validation results. In addition, our in vitro wet experiments demonstrated that DLGAP5 knockdown could suppress the cell proliferation of A549.

CONCLUSION

Novel cuproptosis molecular patterns reflected the distinct immunophenotypes in LUAD patients. The risk model might pave the way to stratify patients suitable for immunotherapy and predict immunotherapy response.

Molecular Characterization and Prognosis of Lactate-Related Genes in Lung Adenocarcinoma

Objective: To explore the lactate-related genes (LRGs) in lung adenocarcinoma (LUAD) by various methods, construct a prognostic model, and explore the relationship between lactate subtypes and the immune tumor microenvironment (TME). Methods: 24 LRGs were collected. The mutation landscape and the prognosis value of LRGs were explored by using The Cancer Genome Atlas (TCGA) data. Consensus clustering analysis was used for different lactate subtype identification. Based on the lactate subtypes, we explore the landscape of TME cell infiltration. A risk-score was calculated by using the LASSO-Cox analysis. A quantitative real-time PCR assay was utilized to validate the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients. Results: Comparing the normal samples, 18 LRGs were differentially expressed in tumor samples, which revealed that the differential expression of LRGs may be related to Copy Number Variation (CNV) alterations. The two distinct lactate subtypes were defined. Compared to patients in the LRGcluster A group, LUAD patients in the LRGcluster B group achieved better survival. The prognostic model was constructed based on differentially expressed genes (DEGs) via the LASSO-Cox analysis, which showed the accuracy of predicting the prognosis of LUAD patients using the ROC curve. A high-risk score was related to a high immune score, stromal score, and tumor mutation burden (TMB). Patients had better OS with low risk compared with those with high risk. The sensitivities of different risk groups to chemotherapeutic drugs were explored. Finally, the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients was verified via qRT-PCR. Conclusions: The lactate subtypes were independent prognostic biomarkers in LUAD. Additionally, the difference in the lactate subtypes was an indispensable feature for the individual TME. The comprehensive evaluation of the lactate subtypes in the single tumor would help us to understand the infiltration characteristics of TME and guide immunotherapy strategies.

In silico development and in vitro validation of a novel five-gene signature for prognostic prediction in colon cancer

Colon cancer is one of the most common cancers in digestive system, and its prognosis remains unsatisfactory. Therefore, this study aimed to identify gene signatures that could effectively predict the prognosis of colon cancer patients by examining the data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. LASSO-Cox regression analysis generated a five-gene signature (DCBLD2, RAB11FIP1, CTLA4, HOXC6 and KRT6A) that was associated with patient survival in the TCGA cohort. The prognostic value of this gene signature was further validated in two independent GEO datasets. GO enrichment revealed that the function of this gene signature was mainly associated with extracellular matrix organization, collagen-containing extracellular matrix, and extracellular matrix structural constituent. Moreover, a nomogram was established to facilitate the clinical application of this signature. The relationships among the gene signature, mutational landscape and immune infiltration cells were also investigated. Importantly, this gene signature also reliably predicted the overall survival in IMvigor210 anti-PD-L1 cohort. In addition to the bioinformatics study, we also conducted a series of in vitro experiments to demonstrate the effect of the signature genes on the proliferation, migration, and invasion of colon cancer cells. Collectively, our data demonstrated that this five-gene signature might serve as a promising prognostic biomarker and shed light on the development of personalized treatment in colon cancer patients.

LATPS, a novel prognostic signature based on tumor microenvironment of lung adenocarcinoma to better predict survival and immunotherapy response

Background

Clinically, only a minority of patients benefit from immunotherapy and few efficient biomarkers have been identified to distinguish patients who would respond to immunotherapy. The tumor microenvironment (TME) is reported to contribute to immunotherapy response, but details remain unknown. We aimed to construct a prognostic model based on the TME of lung adenocarcinoma (LUAD) to predict the prognosis and immunotherapy efficacy.

Methods

We integrated computational algorithms to describe the immune infiltrative landscape of LUAD patients. With the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses, we developed a LUAD tumor microenvironment prognostic signature (LATPS). Subsequently, the immune characteristics and the benefit of immunotherapy in LATPS-defined subgroups were analyzed. RNA sequencing of tumor samples from 28 lung cancer patients treated with anti-PD-1 therapy was conducted to verify the predictive value of the LATPS.

Results

We constructed the LATPS grounded on four genes, including UBE2T, KRT6A, IRX2, and CD3D. The LATPS-low subgroup had a better overall survival (OS) and tended to have a hot immune phenotype, which was characterized by an elevated abundance of immune cell infiltration and increased activity of immune-related pathways. Additionally, tumor immune dysfunction and exclusion (TIDE) score was markedly decreased in the LATPS-low subgroup, indicating an enhanced opportunity to benefit from immunotherapy. Survival analysis in 28 advanced lung cancer patients treated with an anti-PD-1 regimen at Nanfang hospital revealed that the LATPS-low subgroup had better immunotherapy benefit.

Conclusion

LATPS is an effective predictor to distinguish survival, immune characteristics, and immunotherapy benefit in LUAD patients.

A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.

Methods

Downloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan-Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.

Results

A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.

Conclusions

Collectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.

Identifying a lactic acid metabolism-related gene signature contributes to predicting prognosis, immunotherapy efficacy, and tumor microenvironment of lung adenocarcinoma

Lactic acid, once considered as an endpoint or a waste metabolite of glycolysis, has emerged as a major regulator of cancer development, maintenance, and progression. However, studies about lactic acid metabolism-related genes (LRGs) in lung adenocarcinoma (LUAD) remain unclear. Two distinct molecular subtypes were identified on basis of 24 LRGs and found the significant enrichment of subtype A in metabolism-related pathways and had better overall survival (OS). Subsequently, a prognostic signature based on 5 OS-related LRGs was generated using Lasso Cox hazards regression analysis in TCGA dataset and was validated in two external cohorts. Then, a highly accurate nomogram was cosntructed to improve the clinical application of the LRG_score. By further analyzing the LRG_score, higher immune score and lower stromal score were found in the low LRG_score group, which presented a better prognosis. Patients with low LRG_score also exhibited lower somatic mutation rate, tumor mutation burden (TMB), and cancer stem cell (CSC) index. Three more independent cohorts (GSE126044: anti-PD-1, GSE135222: anti-PD-1, and IMvigor210: anti-PD-L1) were analyzed, and the results showed that patients in the low LRG_score category were more responsive to anti-PD-1/PD-L1 medication and had longer survival times. It was also determined that gefitinib, etoposide, erlotinib, and gemcitabine were more sensitive to the low LRG_score group. Finally, we validated the stability and reliability of LRG_score in cell lines, clinical tissue samples and HPA databases. Overall, the LRG_score may improve prognostic information and provide directions for current research on drug treatment strategies for LUAD patients.

Role of hypoxia in the tumor microenvironment and targeted therapy

Tumor microenvironment (TME), which is characterized by hypoxia, widely exists in solid tumors. As a current research hotspot in the TME, hypoxia is expected to become a key element to break through the bottleneck of tumor treatment. More and more research results show that a variety of biological behaviors of tumor cells are affected by many factors in TME which are closely related to hypoxia. In order to inhibiting the immune response in TME, hypoxia plays an important role in tumor cell metabolism and anti-apoptosis. Therefore, exploring the molecular mechanism of hypoxia mediated malignant tumor behavior and therapeutic targets is expected to provide new ideas for anti-tumor therapy. In this review, we discussed the effects of hypoxia on tumor behavior and its interaction with TME from the perspectives of immune cells, cell metabolism, oxidative stress and hypoxia inducible factor (HIF), and listed the therapeutic targets or signal pathways found so far. Finally, we summarize the current therapies targeting hypoxia, such as glycolysis inhibitors, anti-angiogenesis drugs, HIF inhibitors, hypoxia-activated prodrugs, and hyperbaric medicine.

Correlation of Tryptophan Metabolic Pathway with Immune Activation and Chemosensitivity in Patients with Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer with high malignancy and easy metastasis in the early stage. In this study, we aimed to figure out the role of tryptophan metabolic pathway in LUAD prognosis and treatment. Different molecular subtypes were constructed based on tryptophan metabolism-related genes. Significant prognostic genes and clinical prognostic characteristics, immune infiltration level, and pathway activity in different subtypes were determined by algorithms, such as the least absolute shrinkage and selection operator (Lasso), CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE), and gene set enrichment analysis (GSEA). The effect of different gene mutation types on the prognosis of patients with LUAD was explored. The clinical prognosis model was constructed and its reliability was verified. Of the 40 genes in the tryptophan metabolism pathway, 13 had significant prognostic significance. Based on these 13 genes, three molecular subtypes (C1, C2, and C3) were established. Among them, C1 had the worst prognosis and the lowest enrichment score of tryptophan metabolism. At the same time, C1 had the most genetic variation, the highest level of immune infiltration, and significantly activated pathways related to tumor development. The high-risk and low-risk groups had significant differences in prognosis, immune infiltration and pathway enrichment, which was consistent with the results of subtype analysis. Mutation in tryptophan metabolism-related genes leads to abnormal tryptophan metabolism, immune deficiency, and activation of cancer-promoting pathways. This results in high malignancy, poor prognosis, and failure of traditional clinical treatments. Through the establishment of risk score (RS) clinical prognosis model, we determined that RS could reliably predict the prognosis of patients with LUAD.

The Genomic Landscape in Philadelphia-Negative Myeloproliferative Neoplasm Patients with Second Cancers

Patients with myeloproliferative neoplasms (MPNs) are characterized by systemic inflammation. With the indolent nature of the diseases, second cancers (SCs) have emerged as a challenging issue in afflicted patients. Epidemiological studies have confirmed the excessive risk of SCs in MPNs, but little is known about their molecular basis. To explore further, we used whole exome sequencing to explore the genetic changes in the granulocytes of 26 paired MPN patients with or without SC. We noticed that MPN−SC patients harbor genomic variants of distinct genes, among which a unique pattern of co-occurrence or mutual exclusiveness could be identified. We also found that mutated genes in MPN−SC samples were enriched in immune-related pathways and inflammatory networks, an observation further supported by their increased plasma levels of TGF-β and IL-23. Noteworthily, variants of KRT6A, a gene capable of mediating tumor-associate macrophage activity, were more commonly detected in MPN−SC patients. Analysis through OncodriveCLUST disclosed that KRT6A replaces JAK2V617F as the more prominent disease driver in MPN−SC, whereas a major mutation in this gene (KRT6A c.745T>C) in our patients is linked to human carcinoma and predicted to be pathogenic in COSMIC database. Overall, we demonstrate that inflammation could be indispensable in MPN−SC pathogenesis.

A novel prognostic signature of metastasis-associated genes and personalized therapeutic strategy for lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) is a highly invasive and metastatic malignant tumor with high morbidity and mortality. This study aimed to construct a prognostic signature for LUAD patients based on metastasis-associated genes (MAGs). RNA expression profiles were downloaded from the Cancer Genome Atlas (TCGA) database. RRA method was applied to identify differentially expressed MAGs. A total of 192 significantly robust MAGs were determined among seven GEO datasets. MAGs were initially selected through the Lasso Cox regression analysis and 6 MAGs were included to construct a prognostic signature model. Transcriptome profile, patient prognosis, correlation between the risk score and clinicopathological features, immune cell infiltration characteristics, immunotherapy sensitivity and chemotherapy sensitivity differed between low- and high-risk groups after grouping according to median risk score. The reliability and applicability of the signature were further validated in the GSE31210, GSE50081 and GSE68465 cohort. CMap predicted 62 small molecule drugs on the base of the prognostic MAGs. Targeted drug staurosporine had hydrogen bonding with Gln-172 of SLC2A1, which is one of MAGs. Staurosporine could inhibit cell migration in A549 and H1299. We further verified mRNA and protein expression of 6 MAGs in A549 and H1299. The signature can serve as a promising prognostic tool and may provide a novel personalized therapeutic strategy for LUAD patients.

A Four-Gene Prognostic Signature Based on the TEAD4 Differential Expression Predicts Overall Survival and Immune Microenvironment Estimation in Lung Adenocarcinoma

Background: TEA domain transcription factor 4 (TEAD4) is a member of the transcriptional enhancer factor (TEF) family of transcription factors, which is studied to be linked to the tumorigenesis and progression of various forms of cancers, including lung adenocarcinoma (LUAD). However, the specific function of this gene in the progression of LUAD remains to be explored.

Method: A total of 19 genes related to the Hippo pathway were analyzed to identify the significant genes involved in LUAD progression. The TCGA-LUAD data (n = 585) from public databases were mined, and the differentially expressed genes (DEGs) in patients with the differential level of TEAD4 were identified. The univariate Cox regression, zero LASSO regression coefficients, and multivariate Cox regression were performed to identify the independent prognostic signatures. The immune microenvironment estimation in the two subgroups, including immune cell infiltration, HLA family genes, and immune checkpoint genes, was assessed. The Gene Set Enrichment Analysis (GSEA) and GO were conducted to analyze the functional enrichment of DEGs between the two risk groups. The potential drugs for the high-risk subtypes were forecasted via the mode of action (moa) module of the connectivity map (CMap) database.

Results:TEAD4 was found to be significantly correlated with poor prognosis in LUAD-patients. A total of 102 DEGs in TEAD4-high vs. TEAD4-low groups were identified. Among these DEGs, four genes (CPS1, ANLN, RHOV, and KRT6A) were identified as the independent prognostic signature to conduct the Cox risk model. The immune microenvironment estimation indicated a strong relationship between the high TEAD4 expression and immunotherapeutic resistance. The GSEA and GO showed that pathways, including cell cycle regulation, were enriched in the high-risk group, while immune response-related and metabolism biological processes were enriched in the low-risk group. Several small molecular perturbagens targeting CFTR or PLA2G1B, by the mode of action (moa) modules of the glucocorticoid receptor agonist, cyclooxygenase inhibitor, and NFkB pathway inhibitor, were predicted to be suited for the high-risk subtypes based on the high TEAD4 expression.

Conclusion: The current study revealed TEAD4 is an immune regulation–related predictor of prognosis and a novel therapeutic target for LUAD.

Gut Microbiome Was Highly Related to the Regulation of Metabolism in Lung Adenocarcinoma Patients

Background

Lung adenocarcinoma (LUAD) is one of the most predominant subtypes of lung cancer. The gut microbiome plays a vital role in the pathophysiological processes of various diseases, including cancers.

Methods

In the study, 100 individuals were enrolled. In total 75 stool and blood samples were analyzed with 16s-rRNA gene sequencing and metabolomics (30 from healthy individuals (H); 45 from LUAD patients). In addition, 25 stool samples were analyzed with metagenomics (10 from H; 15 from LUAD). The linear discriminant analysis (LDA) effect size (LefSe) and logistic regression analysis were applied to identify biomarkers’ taxa and develop a diagnostic model. The diagnostic power of the model was estimated with the receiver operating characteristic curve (ROC) by comparing the area under the ROC (AUC). The correlation between biomarker’s taxa and metabolites was calculated using the Spearman analysis.

Results

The α and β diversity demonstrated the composition and structure of the gut microbiome in LUAD patients were different from those in healthy people. The top three abundance of genera were Bacteroides (25.06%), Faecalibacterium (11.00%), and Prevotella (5.94%). The LefSe and logistic regression analysis identified three biomarker taxa (Bacteroides, Pseudomonas, and Ruminococcus gnavus group) and constructed a diagnostic model. The AUCs of the diagnostic model in 16s-rRNA gene sequencing and metagenomics were 0.852 and 0.841, respectively. A total of 102 plasma metabolites were highly related to those three biomarkers’ taxa. Seven metabolic pathways were enriched by 102 plasma metabolites, including the Pentose phosphate pathway, Glutathione metabolism.

Conclusions

In LUAD patients, the gut microbiome profile has significantly changed. We used three biomarkers taxa to develop a diagnostic model, which was accurate and suitable for the diagnosis of LUAD. Gut microbes, especially those three biomarkers’ taxa, may participate in regulating metabolism-related pathways in LUAD patients, such as the pentose phosphate pathway and glutathione metabolism.