Overall Survival In Lung Adenocarcinoma Research Articles

NECTIN2 is a prognostic biomarker and potential therapeutic target in lung adenocarcinoma

IntroductionNECTINs are transmembrane proteins mediating cell-to-cell adhesion. NECTINs interact with integrins or other membrane receptors to trigger multiple cellular functions. Aberrant NECTIN expression is associated with cancer progression and poor outcomes. While NECTIN2 is overexpressed in various cancer types, its role in lung cancer is not well understood. Material and methodsWe investigated the function of NECTIN2 in lung adenocarcinoma (LUAD) using the Cancer Genome Atlas (TCGA) dataset and clinical samples of 105 LUAD patients who had undergone surgical resection. Cell proliferation, apoptosis, migration and invasion were investigated using human lung adenocarcinoma cell lines. ResultsWe found that high NECTIN2 expression correlated with reduced overall survival in LUAD in TCGA database. In clinical samples, high NECTIN2 expression was associated with lower recurrence-free survival in all patients (P < 0.001) and in stage I patients (P = 0.001). Functional analyses demonstrated that NECTIN2 knockout promoted cell apoptosis and diminished cell proliferation and migration capacity. NECTIN2 overexpression did not significantly affect cellular functions. DiscussionOur results suggest that NECTIN2 plays a significant role in cell apoptosis and cancer cell migration, leading to increased postoperative recurrence. Furthermore, NECTIN2 serves as a prognostic indicator and a potential therapeutic target in LUAD. ConclusionsHigh NECTIN2 expression in LUAD was found to be associated with postoperative recurrence, and was observed to play an important role in cell apoptosis and migration.

Pan-cancer analysis of SLC2A family genes as prognostic biomarkers and therapeutic targets

BackgroundThe major facilitator superfamily glucose transporters (GLUTs), encoded by solute carrier 2A (SLC2A) genes, mediate the transmembrane movement and uptake of glucose. To satisfy the improved energy demands, glycolysis flux is increased in cancers compared with healthy tissues. Multiple diseases, including cancer, have been associated with GLUTs. Nevertheless, not much research has been done on the functions of SLC2As in pan-cancer prognosis or their clinical treatment potential. MethodsThe SLC2A family genes' level of expression and prognostic values were analyzed in relation to pan-cancer. We then examined the association among SLC2As expression and TME, Stemness score, clinical characteristics, immune subtypes, and drug sensitivity. We merged bioinformatics analysis techniques with up-to-date public databases. Additionally, SLC2As from the KOBAS database were subjected to enrichment analysis. ResultsWe discovered that SLC2As' gene expression differed significantly between normal tissues and many malignancies. A number of tumors from various databases demonstrate a relationship between prognosis and SLC2A family gene expression. For instance, SLC2A2 and SLC2A5 were associated with the overall survival (OS) of hepatocellular carcinoma. SLC2A1 was associated with the OS of lung adenocarcinoma and pancreatic adenocarcinoma. Moreover, the SLC2A family gene expression is significantly correlated with the pan-cancer stromal and immune scores, and the RNA and DNA stemness scores. Furthermore, we found that the majority of SLC2As had a strong correlation with the tumor stages in KIRC. The immunological subtypes and all members of the SLC2A gene family exhibited a substantial correlation. Moreover, pathways containing insulin resistance and adipocytokine signaling pathway may influence the progression of some cancers. Finally, there is a significant positive or negative connection between drug sensitivity and SLC2A1 expression. ConclusionOur research highlights the significant promise of SLC2As as prognostic indicators and offers insightful approaches for upcoming exploration of SLC2As as putative therapeutic targets in malignancies.

SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer.

Abstract 7640: Prognostic significance of S100A16 expression in patients with resected lung adenocarcinoma

Abstract Background: Lung cancer is the leading cause of cancer death worldwide. Tumor recurrence is the most common cause of treatment failure after surgical resection. The S100 protein family is a multigenic group of cytoplasmic calcium-binding proteins. S100A16 has to be reported to be an independent prognostic indicator of poor overall survival in lung adenocarcinoma recently. However, the prognostic value of S100A16 in recurrence-free survival (RFS) in patients with resected lung adenocarcinoma has not been well demonstrated. The relationship between S100A16 expression and the histological subtypes of lung adenocarcinoma remained unknown. Methods: A total of 79 patients with resected lung adenocarcinoma were included in the study. S100A16 expression was determined by immunohistochemistry in tumor specimens. The prognostic value of S100A16 overexpression and its relationship with clinicopathological variables, including histology subtypes, were investigated. Results: S100A16 overexpression tended to be significantly associated with order age (P = 0.074). There was no significant association between S100A16 overexpression and predominant pattern group (lepidic/acinar/papillary vs. micropapillary/solid) (P = 0.447). Univariate analysis indicated that pathologic stage (II or III vs. I) (P = 0.001) and S100A16 overexpression (P = 0.005) were significant prognostic factors for worse recurrence-free survival (RFS). Predominant pattern group (lepidic/acinar/papillary vs. micropapillary/solid) (P = 0.064) tended to be a significant prognostic factor for worse RFS. In multivariate analysis, pathologic stage (II or III vs. I) (HR, 27.847; 95% CI, 4.173 to 185.831; P = 0.001), and S100A16 overexpression (HR, 21.740; 95% CI, 2.516 to 187.870; P = 0.005) were still significant prognostic factors for worse RFS. Conclusions: S100A16 overexpression was a significant prognostic factor for worse RFS in patients with resected lung adenocarcinoma. This information is useful to stratify high-risk patients of recurrence after resection of lung adenocarcinoma. Citation Format: Jung-Jyh Hung. Prognostic significance of S100A16 expression in patients with resected lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7640.

Nuclear protein NOP2 serves as a poor-prognosis predictor of LUAD and aggravates the malignancy of lung adenocarcinoma cells.

Recent studies have shown that NOP2, a nucleolar protein, is up-regulated in various cancers, suggesting a potential link to tumor aggressiveness and unfavorable outcomes. This study examines NOP2's role in lung adenocarcinoma (LUAD), a context where its implications remain unclear. Utilizing bioinformatics, we assessed 513 LUAD and 59 normal tissue samples from The Cancer Genome Atlas (TCGA) to explore NOP2's diagnostic and prognostic significance in LUAD. Additionally, in vitro experiments compared NOP2 expression between Beas-2b and A549 cells. Advanced databases and analytical tools, including LINKEDOMICS, STRING, and TISIDB, were employed to further elucidate NOP2's association with LUAD. Our findings indicate a significantly higher expression of NOP2 mRNA and protein in A549 cells compared to Beas-2b cells (P < 0.001). In LUAD, elevated NOP2 levels were linked to decreased Overall Survival (OS) and advanced clinical stages. Univariate Cox analysis revealed that high NOP2 expression correlated with poorer OS in LUAD (P < 0.01), a finding independently supported by multivariate Cox analysis (P < 0.05). The relationship between NOP2 expression and LUAD risk was presented via a Nomogram. Additionally, Gene Set Enrichment Analysis (GSEA) identified seven NOP2-related signaling pathways. A focal point of our research was the interplay between NOP2 and tumor-immune interactions. Notably, a negative correlation was observed between NOP2 expression and the immune infiltration levels of macrophages, neutrophils, mast cells, Natural Killer (NK) cells, and CD8 + T cells in LUAD. Moreover, the expression of NOP2 was related to the sensitivity of various chemotherapeutic drugs. In vitro, we found that downregulating NOP2 can decrease the proliferation, migration and invasion of A549 cells. Furthermore, NOP2 can regulate Caspase3-mediated apoptosis. Collectively, particularly regarding prognosis, immune infiltration and vitro experiments, these findings suggest NOP2's potential of serving as a poor-prognostic biomarker for LUAD and aggravating the malignancy of lung adenocarcinoma cells.

Elucidating the anti-cancer potential of Cinnamomum tamala essential oil against non-small cell lung cancer: A multifaceted approach involving GC-MS profiling, network pharmacology, and molecular dynamics simulations

Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., or Indian Bay Leaf, is a well-known traditional ayurvedic medicine used to treat various ailments. However, the molecular mechanism of action of Cinnamomum tamala essential oil (CTEO) against non-small cell lung cancer (NSCLC) remains elusive. The present study aims to decipher the molecular targets and mechanism of CTEO in treating NSCLC. GC-MS analysis detected 49 constituents; 44 successfully passed the drug-likeness screening and were identified as active compounds. A total of 3961 CTEO targets and 4588 anti-NSCLC-related targets were acquired. JUN, P53, IL6, MAPK3, HIF1A, and CASP3 were determined as hub genes, while cinnamaldehyde, ethyl cinnamate and acetophenone were identified as core compounds. Enrichment analysis revealed that targets were mainly involved in apoptosis, TNF, IL17, pathways in cancer and MAPK signalling pathways. mRNA expression, pathological stage, survival analysis, immune infiltrate correlation and genetic alteration analysis of the core hub genes were carried out. Kaplan-Meier overall survival (OS) curve revealed that HIF1A and CASP3 are linked to worse overall survival in Lung Adenocarcinoma (LUAD) cancer patients compared to normal patients. Ethyl cinnamate and cinnamaldehyde showed high binding energy with the MAPK3 and formed stable interactions with MAPK3 during the molecular dynamic simulations for 100 ns. The MM/PBSA analysis revealed that van der Waals (VdW) contributions predominantly account for a significant portion of the compound interactions within the binding pocket of MAPK3. Density functional theory analysis showed cinnamaldehyde as the most reactive and least stable compound. CTEO exhibited selective cytotoxicity by inhibiting the proliferation of A549 cells while sparing normal HEK293 cells. CTEO triggered apoptosis by arresting the cell cycle, increasing ROS accumulation, causing mitochondrial depolarisation, and elevating caspase-3, caspase-8 and caspase-9 levels in A549 cells. The above study provides insights into the pharmacological mechanisms of action of Cinnamomum tamala essential oil against non-small cell lung cancer treatment, suggesting its potential as an adjuvant therapy.

Exploring the molecular and immune-landscape of lung cancer associated with cystic airspaces

To explore the molecular biological characteristics of lung cancer associated with cystic airspaces (LCCA) and its potential roles on prognosis. A total of 165 LCCAs and 201 non-LCCAs were enrolled in this study. Bulk RNA sequencing was implemented in eight LCCAs and nine non-LCCAs to explore the differentially expressed genes. TCGA data were used to analyze LCCA-specific genes that associated with overall survival (OS). The median age was 60 (IQR 53 to 65) years in LCCA cohort. We found LCCA were predominant in men and had less visceral pleura invasion (VPI) or lympho-vascular invasion (LVI). Moreover, LCCA presented with higher histological heterogeneity. Kaplan-Meier analysis showed that patients of age more than 60 and positive VPI had significantly less PFS in LCCA. Cox regression suggested that LCCA, micropapillary subtype proportion and VPI were the independent risk factors for PFS. LCCA had up-regulated pathways associated with EMT, angiogenesis and cell migration. In addition, LCCA displayed higher levels of immunosuppressor infiltration (M2 macrophages, CAFs and MDSCs) and distinct cell death and metabolic patterns. BCR/TCR repertoire analysis revealed less BCR richness, clonality and high-abundance shared clonotypes in LCCA. Finally, Cox regression analysis identified that four cystic-specific genes, KCNK3, NRN1, PARVB and TRHDE-AS1, were associated with OS of lung adenocarcinoma (LUAD). And cystic-specific risk scores (CSRSs) were calculated to construct a nomogram, which performance well. Our study for the first time indicated significantly distinct molecular biological and immune characteristics between LCCA and non-LCCA, which provide complementary prognostic values in early-stage non-small cell lung cancer (NSCLC).

Prognostic value of tumor‑associated CD177+ neutrophils in lung adenocarcinoma.

The aim of the present study was to detect CD177+ neutrophils in tumor tissues and analyze their association with the clinical characteristics and prognosis of patients with lung adenocarcinoma (LUAD). Immunohistochemistry was used to detect CD177+ neutrophils in tumors and adjacent tissues of 16 patients with LUAD who underwent curative surgical resection. A total of 120 patients with LUAD were recruited, and their clinical data were collected; survival follow-up was performed. CD177+ neutrophils in tumor tissues were detected via immunohistochemistry, and the association between CD177+ neutrophils and clinical characteristics was analyzed. The density of CD177+ neutrophils in tumor tissues and adjacent tissues of patients with LUAD was analyzed using t-test, and the association between CD177+ neutrophils and clinical characteristics was analyzed through the Chi-square test. Survival was calculated using the Kaplan-Meier survival rate curve. Finally, the association between these indicators and the survival of LUAD patients was evaluated using Cox regression analysis. CD177+ neutrophil infiltration was significantly higher in LUAD tumor tissues, and the high density of CD177+ neutrophils was associated with the clinical characteristics of TNM stage, tumor differentiation and poor progression-free and overall survival in LUAD. In conclusion, tumor-associated CD177+ neutrophils associated with malignant progression and poor prognosis may be independent and unfavorable prognostic biomarkers for LUAD.

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma.

Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD). Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements. The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson's R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS. Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.

A novel disulfidptosis-related prognostic gene signature and experimental validation identify ACTN4 as a novel therapeutic target in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies. To investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD. We investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD. Ten differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P< 0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes. Our study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.

SFXN1 as a potential diagnostic and prognostic biomarker of LUAD is associated with 18F-FDG metabolic parameters

BackgroundSideroflexin 1 (SFXN1) has been discovered as a novel tumor marker for lung adenocarcinoma, but data on its importance in the development of lung adenocarcinoma is still limited. This study evaluated the correlation between SFXN1 and parameters related to 18F-flurodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), and further explored the role of SFXN1 in the value-added and glycolytic processes of LUAD. MethodThe expression and prognostic value of SFXN1 mRNA in LUAD were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data base. Retrospective analysis of 18F-FDG PET imaging and metabolic parameters in 42 patients to explore the relationship between the expression of SFXN1 and glucose metabolism levels in lung adenocarcinoma and its clinical significance. H1975 cells were selected as the in vitro research object, and the biological effects of SFXN1 on LUAD were further elucidated through Edu proliferation assay, CCK8 activity assay, wound healing experiment, and cell flow cytometry. ResultSFXN1 is highly expressed in various tumors, including LUAD, and its high expression can serve as an independent predictor of overall survival in lung adenocarcinoma. In addition, the expression of SFXN1 in LUAD was significantly correlated with 18F-FDG PET/CT parameters: maximum and average standardized uptake values (SUVmax and SUVmean), as well as total lesion glycolysis (TLG) (rho = 0.574, 0.589, and 0.338, p < 0.05), which can predict the expression of SFXN1 with an accuracy of 0.934. In vitro functional experiments have shown that knocking down SFXN1 inhibits the proliferation and migration of LUAD cells, promotes cell apoptosis, and may inhibit tumor activity by regulating the expression of glycolytic related genes SLC2A1, HK2, GPI, ALDOA, GAPDH, ENO1, PKM, and LDHA. ConclusionThe overexpression of SFXN1 is closely related to FDG uptake, and SFXN1, as a promising prognostic biomarker, may mediate the development of LUAD through the glycolytic pathway.

Comprehensive analysis of m6A modification patterns and m6A-related lncRNAs as potential biomarkers in lung adenocarcinoma.

N6-methyladenosine (m6A) methylation is considered to induce tumor cell proliferation, migration, and apoptosis. Understanding the mechanism of m6A-related lncRNAs in the development of lung adenocarcinoma (LUAD) may help predict prognosis. m6A-related lncRNAs related to lung cancer were identified and combined with the MeRIP-Seq dataset. The consensus clustering method was utilized to divide LUAD patients, and prognostic model was constructed using the Lasso Cox algorithm. The cluster profiler package was used for gene ontology and KEGG enrichment. The proportion of immune infiltration was estimated using the CIBERSORT algorithm. The decision tree was constructed by the rpart package, and nomograms were built by the rms package. The Connectivity Map database was analyzed for the therapeutic effects of small molecule drugs for LUAD. In addition, qPCR, colony formation and transwell assays were performed to validate functions of m6A-associated lncRNAs. Nineteen m6A-modified lncRNAs in LUAD were identified. LUAD patients were divided into two categories based on the expression of 19 m6A-related lncRNAs. Cluster 2 patients had better antigen production and expression, while naive B cells, plasma cells, and activated NK cells were lower in cluster 1. Nine m6A-related lncRNAs were selected to establish a risk model for evaluating the prognosis of LUAD patients. The high-risk group had higher tumor mutational burden and lower TIDE scores with more gamma delta T cells and neutrophils. Nomograms showed that the prognostic model had predominant predictive ability for LUAD patients based on the risk score analyzed by the decision tree model. Benzo(a)pyrene and neurodazine might improve the prognosis of LUAD patients. The qRT-PCR results confirmed the reliability of the analytical results. The establishment of a prognostic model of m6A-related lncRNAs can independently predict overall survival in LUAD and may help to develop personalized immunotherapy strategies.

Prognostic and immunological values of SKA3 for overall survival in lung adenocarcinoma and its RNA binding protein involved mechanisms

This article aimed to investigate the correlations among SKA3 expression and prognosis, clinical relevance, tumor immunity, and RNA-binding protein (RBP)-involved mechanisms for overall survival (OS) in lung adenocarcinoma (LUAD). To explore the SKA3 expression level in LUAD by analyzing the genomic data as well as related clinical characteristics from the database of TCGA. Nomogram and gene set enrichment analysis (GSEA) were applied, respectively, to evaluate the performance of SKA3 in LUAD. Correlations between SKA3 and immunity and RBP-involved mechanisms were also performed. SKA3 had a higher expression level in LUAD samples than in adjacent normal lung samples, with shorter survival times in the high-SKA3-expressed LUAD subgroup (P < 0.05). qRT-PCR results remained consistent (P < 0.05). Uni-/multivariate Cox analyses revealed that SKA3 could have independent prognostic ability for LUAD (both P < 0.05). The nomogram model constructed with clinical pathological parameters and SKA3 expression levels predicted OS rates for LUAD and GSEA revealed SKA3-related pathways. In aspects of tumor immunity, SKA3 was significantly involved with tumor neoantigen burden, tumor mutational burden, immune cell pathways, and immune checkpoint inhibitor (ICI) molecules (all P < 0.05). The CellMiner database also found significant correlations between SKA3 and the antitumor drug sensitivity of chemotherapy, fenretinide, and PX-316. Besides, a total of nine LncRNA/RBP/SKA3 networks were revealed in LUAD for their RBP-involved mechanisms. SKA3 could serve as a potential biomarker for OS prognosis and immunotherapy in LUAD. LncRNA/RBP/SKA3 networks were identified in LUAD for their RBP-involved mechanisms, paving the way for further experimental verifications.

Identification of metabolism-related gene signature in lung adenocarcinoma.

Lung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases. TCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established. A 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature. In this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

The mitotic cell cycle-associated nomogram predicts overall survival in lung adenocarcinoma.

This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) associated with mitotic cell cycle. The model will predict the probability of survival at different time points and serve as a reference tool to evaluate the effectiveness of LUAD treatment. A cohort of 442 patients with LUAD from the gene expression omnibus (GEO) database was randomly divided into a training group (n = 299) and a validation group (n = 99). The least absolute shrinkage and selection operator (LASSO)-COX algorithm was used to reduce the number of predictors based on the clinicopathological and RNA sequencing data to establish mutant characteristics that could predict patient survival. Additionally, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) analyses were conducted on the mutant characteristics. The performance of the developed nomogram was evaluated using calibration curves and the C-index. The mutant characteristics had prognostic value for LUAD and acted as an independent prognostic factor. The mutant characteristics profile derived from the LASSO-COX algorithm demonstrated a significant association with overall survival in patients with LUAD. Functional annotation based on the mutant score, its involvement in the phase transition of the mitotic cell cycle, and its regulatory processes. The nomogram, which combined the mutant score with clinical factors associated with prognosis, showed robust accuracy in both the training and validation groups. This study presents the first individualized model that establishes a mutant score for predicting survival in LUAD. This model can be used as a predictive tool for determining 1-, 2-, 3-, and 5-year survival probabilities in patients with LUAD.

Dipeptidyl peptidase 4 inhibition sensitizes radiotherapy by promoting T cell infiltration

ABSTRACT Radiotherapy could regulate systemic antitumor immunity, while the immune state in the tumor microenvironment (TME) also affects the efficacy of radiotherapy. We have found that higher CD8+ T cell infiltration is associated with longer overall survival of lung adenocarcinoma and melanoma patients receiving radiotherapy. 8-Gray radiation increased the transcriptional levels of chemokines in tumor cells in vitro. However, it was not sufficient to induce significant lymphocyte infiltration in vivo. Dipeptidyl peptidase 4 (DPP4) has been reported to inactivate chemokines via post-translational truncation. Single-cell sequencing revealed that dendritic cells (DCs) had a higher DPP4 expression among other cells in the TME and upregulated DPP4 expression after radiation. Combining a DPP4 inhibitor with radiotherapy could promote chemokines expression and T cell infiltration in the TME, enhancing the antitumor effect of radiotherapy. Moreover, this therapy further enhanced the therapeutic efficacy of anti-PD-1. In this study, we demonstrated the underlying mechanism of why radiotherapy failed to induce sufficient T cell infiltration and proposed an effective strategy to promote T cell infiltration and sensitize radiotherapy. These findings demonstrate the translational value of DPP4 inhibition as a complementary approach to enhance the efficacy of radiotherapy and the combination of radiotherapy with immunotherapy.

Construction and validation of a novel prognostic nomogram for predicting overall survival in lung adenocarcinoma patients with different patterns of metastasis

ObjectiveMetastasis of lung cancer is an important factor affecting survival. The present study proposed to establish and verify a nomogram for predicting overall survival (OS) in lung adenocarcinoma (LUAD) patients with different patterns of metastasis.MethodsA total of 9727 patients diagnosed with metastatic LUAD patients from 2010 to 2015 were enrolled based on surveillance, epidemiology and end results (SEER) Database and then randomly divided into training and validation cohorts, and 136 patients in our Cancer Center were enrolled as the external validation cohort. Univariate and multivariate analyses were performed to evaluate the prognostic impact on OS. A prognostic nomogram was constructed and evaluated by C-index, calibration curve, decision curve analysis (DCA), and risk stratification system.ResultsUltimately, 6809 and 2918 patients diagnosed with metastatic LUAD in the training and validation cohorts were enrolled in the study, respectively. A male sex, a later T and N stage, a larger tumor size, treatment including no surgery, no chemotherapy and no radiotherapy, metastasis sites were found to be independent risk factors in LUAD patients for worse OS, and then incorporated into the nomogram. The frequency of bone metastasis was the highest, and in single site metastasis, the prognosis of liver metastasis was the worst. Two-site metastasis is more common than three-site and four-site metastasis, and co-metastasis eventually leads to a worse survival outcome. The C-index value of nomogram for predicting OS were 0.798, 0.703 and 0.698 in the internal training, validation and external validation cohorts, separately. The calibration curves for the 6-months, 1-year and 2-year showed significant agreement between nomogram models and actual observations. The DCA curves indicated nomogram was more beneficial than the AJCC TNM stage. Patients were further divided into low-risk and high-risk groups according to nomogram predicted scores and developed a survival risk classification system.ConclusionsOur prognostic nomogram is expected to be an accurate and individualized clinical predictive tool for predicting OS in LUAD patients with different patterns of metastasis.

AHSG, a Gene Promoting Tumour Proliferation, Migration and Invasion, is an Independent Prognostic Factor for Poor Overall Survival in Lung Adenocarcinoma.

Recent studies have shown that the metabolic process-related gene AHSG is involved in multiple pathological processes of tumours. This study will explore the relationship between AHSG and lung adenocarcinoma. Expression analysis, survival analysis and co-expression analysis of AHSG were performed using a public database, and cytological and molecular biology assays were performed to explore the role of AHSG in lung adenocarcinoma. Compared with normal tissues, AHSG expression was significantly higher in cancer tissues in the TCGA-LUAD database, and pan-cancer analysis revealed abnormal AHSG expression in different kinds of tumours. Survival analysis revealed that compared with the low expression group, the patients in the high expression group had a significantly worse overall survival duration in the TCGA-LUAD database, and a subsequent study confirmed that AHSG expression could be an independent prognostic factor of overall survival in lung adenocarcinoma. AHSG-related genes are involved in multiple physiological and pathophysiological pathways. In subsequent cytological and molecular biology experiments, inhibition of AHSG expression suppressed proliferation, migration and invasion in lung adenocarcinoma cell lines, and the EMT process was blocked after knockdown of AHSG. AHSG could be used as a prognostic factor for OS in patients with lung adenocarcinoma. It can promote the biological behaviour of lung adenocarcinoma and may become a potential target for treatment, which is worthy of further study.

Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer (NSCLC) with a highly heterogeneous tumor microenvironment. Immune checkpoint inhibitors (ICIs) are more effective in tumors with a pre-activated immune status. However, the potential of the immune activation-associated gene (IAG) signature for prognosis prediction and immunotherapy response assessment in LUAD has not been established. Therefore, it is critical to explore such gene signatures. RNA sequencing profiles and corresponding clinical parameters of LUAD were extracted from the TCGA and GEO databases. Unsupervised consistency clustering analysis based on immune activation-related genes was performed on the enrolled samples. Subsequently, prognostic models based on genes associated with prognosis were built using the last absolute shrinkage and selection operator (LASSO) method and univariate Cox regression. The expression levels of four immune activation related gene index (IARGI) related genes were validated in 12 pairs of LUAD tumor and normal tissue samples using qPCR. Using the ESTIMATE, TIMER, and ssGSEA algorithms, immune cell infiltration analysis was carried out for different groups, and the tumor immune dysfunction and rejection (TIDE) score was used to evaluate the effectiveness of immunotherapy. Based on the expression patterns of IAGs, the TCGA LUAD cohort was classified into two clusters, with those in the IAG-high pattern demonstrating significantly better survival outcomes and immune cell infiltration compared to those in the IAG-low pattern. Then, we developed an IARGI model that effectively stratified patients into different risk groups, revealing differences in prognosis, mutation profiles, and immune cell infiltration within the tumor microenvironment between the high and low-risk groups. Notably, significant disparities in TIDE score between the two groups suggest that the low-risk group may exhibit better responses to ICIs therapy. The IARGI risk model was validated across multiple datasets and demonstrated exceptional performance in predicting overall survival in LUAD, and an IARGI-integrated nomogram was established as a quantitative tool for clinical practice. The IARGI can serve as valuable biomarkers for evaluating the tumor microenvironment and predicting the prognosis of LUAD patients. Furthermore, these genes probably provide valuable guidance for establishing effective immunotherapy regimens for LUAD patients.

Prognostic and immunological potential of PPM1G in lung adenocarcinoma.

Lung cancer is one of the most common types of cancer worldwide, with the highest incidence and mortality rates. Protein phosphatase, Mg2+/Mn2+ dependent 1G (PPM1G) is a serine/threonine phosphatase, which is involved in the proliferation, invasion and metastasis of tumor cells. However, there are few reports on the role of PPM1G in lung adenocarcinoma (LUAD). The present study used publicly available data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to evaluate the expression of PPM1G in LUAD, and to assess the relationship between PPM1G expression and the prognosis of patients with LUAD. Protein expression data of PPM1G obtained by immunohistochemical staining were collected from the Human Protein Atlas database. The correlation between PPM1G and immune cell infiltration and immune checkpoints was analyzed by single‑sample gene set enrichment analysis of TCGA data. The Kaplan‑Meier method was used for survival analysis, and univariate and multivariate Cox regression were used to analyze the effect of PPM1G on prognosis with data from TCGA database. The results showed that PPM1G was highly expressed in LUAD cancer tissues. The high expression of PPM1G was associated with poor clinical stage, T stage, N stage and overall survival in LUAD. The present study screened 29 genes related to PPM1G and closely related to the cell cycle in patients with LUAD. The expression of PPM1G was positively correlated with γδ‑Τ cells, T helper 2 cells and natural killer CD56dim cells, and was negatively correlated with B cells, mast cells, plasmacytoid dendritic cells, T helper cells, macrophages, T cells, CD8 T cells, central memory T cells, effector memory T cells, neutrophils and T follicular helper cells. In addition, PPM1G was positively correlated with immune detection points. In conclusion, PPM1G may be involved in the control of the lung cancer cell cycle, and could be associated with prognosis and immune infiltration in patients with LUAD.