Identification of an immune-related genes signature in lung adenocarcinoma to predict survival and response to immune checkpoint inhibitors.

BACKGROUND: Although advances in immune checkpoint inhibitor (ICI) research have provided a new treatment approach for lung adenocarcinoma (LUAD) patients, their survival is still unsatisfactory, and there are issues in the era of response prediction to immunotherapy. METHODS: Using bioinformatics methods, a prognostic signature was constructed, and its predictive ability was validated both in the internal and external datasets (GSE68465). We also explored the tumor-infiltrating immune cells, mutation profiles, and immunophenoscore (IPS) in the low-and high-risk groups. RESULTS: As far as we are aware, this is the first study which introduces a novel prognostic signature model using BIRC5, CBLC, S100P, SHC3, ANOS1, VIPR1, LGR4, PGC, and IGKV4.1. According to multivariate analysis, the 9-immune-related genes (IRGs) signature provided an independent prognostic factor for the overall survival (OS). The low-risk group had better OS, and the tumor mutation burden (TMB) was significantly lower in this group. Moreover, the risk scores were negatively associated with the tumor-infiltrating immune cells, like CD8+ T cells, macrophages, dendritic cells, and NK cells. In addition, the IPS were significantly higher in the low-risk group as they had higher gene expression of immune checkpoints, suggesting that ICIs could be a promising treatment option for low-risk LUAD patients. CONCLUSION: The combination of these 9-IRGs not only could efficiently predict overall survival of LUAD patients but also show a powerful association with the expression of immune checkpoints and response to ICIs based on IPS; hoping this model paves the way for better stratification and management of patients in clinical practice.

Multi-omics profiling and experimental verification of tertiary lymphoid structure-related genes: molecular subgroups, immune infiltration, and prognostic implications in lung adenocarcinoma.

Lung adenocarcinoma (LUAD), characterized by a low 5-year survival rate, is the most common and aggressive type of lung cancer. Recent studies have shown that tertiary lymphoid structures (TLS), which resemble lymphoid structures, are closely linked to the immune response and tumor prognosis. The functions of the tertiary lymphoid structure-related genes (TLS-RGs) in the tumor microenvironment (TME) are poorly understood. Based on publicly available data, we conducted a comprehensive study of the function of TLS-RGs in LUAD. Initially, we categorized LUAD patients into two TLS and two gene subtypes. Subsequently, risk scores were calculated, and prognostic models were constructed using seven genes (CIITA, FCRL2, GBP1, BIRC3, SCGB1A1, CLDN18, and S100P). To enhance the clinical application of TLS scores, we have developed a precise nomogram. Furthermore, drug sensitivity, tumor mutational burden (TMB), and the cancer stem cell (CSC) index were found to be substantially correlated with the TLS scores. Single-cell sequencing results reflected the distribution of TLS-RGs in cells. Finally, we took the intersection of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) prognosis-related genes and then further validated the expression of these genes by qRT-PCR. Our in-depth investigation of TLS-RGs in LUAD revealed their possible contributions to the clinicopathological features, prognosis, and characteristics of TME. These findings underscore the potential of TLS-RGs as prognostic biomarkers and therapeutic targets for LUAD, thereby paving the way for personalized treatment strategies.

Prognostic value of a lactate metabolism gene signature in lung adenocarcinoma and its associations with immune checkpoint blockade therapy response.

Lung adenocarcinoma (LUAD) is a study that examines the prognostic value of lactate metabolism genes in tumor cells, which are associated with clinical prognosis. We analyzed the expression and clinical data for LUAD from The Cancer Genome Atlas database, using the GSE68465 dataset from the Gene Expression Omnibus and the MSigDB database. LASSO Cox regression and stepwise Cox regression were used to identify the optimal lactate metabolism gene signature. Differences in immune infiltration, tumor mutation burden (TMB), and response to immune checkpoint blockade (ICB) therapy were evaluated between groups. LASSO and Cox regression analyses showed an eight-lactate metabolism gene signature for model construction in both TCGA cohort and GSE68465 data, with higher survival outcomes in high-risk groups. The lactate metabolism risk score had an independent prognostic value (hazard ratio: 2.279 [1.652-3.146], P < .001). Immune cell infiltration differed between the risk groups, such as CD8+ T cells, macrophages, dendritic cells, mast cells, and neutrophils. The high-risk group had higher tumor purity, lower immune and stromal scores, and higher TMB. High-risk samples had high tumor immune dysfunction and exclusion (TIDE) scores and low cytolytic activity (CYT) scores, indicating a poor response to ICB therapy. Similarly, most immune checkpoint molecules, immune inhibitors/stimulators, and major histocompatibility complex (MHC) molecules were highly expressed in the high-risk group. The 8-lactate metabolism gene-based prognostic model predicts patient survival, immune infiltration, and ICB response in patients with LUAD, driving the development of therapeutic strategies to target lactate metabolism.

LEF1 is associated with immunosuppressive microenvironment of patients with lung adenocarcinoma.

Wnt/ß-Catenin pathway plays an important role in the occurrence and progression of malignant tumors, especially PD-L1-mediated tumor immune evasion. However, the role of TCF/LEF, an important member of the Wnt/ß-catenin pathway, in the tumor immunosuppressive microenvironment of lung adenocarcinoma (LUAD) remains unknown. LUAD tissue-coding RNA expression data from The Cancer Genome Atlas and TIMER databases were used to analyze the expression of TCF/LEF transcription factors and their correlation with various immune cell infiltration. Immunohistochemistry and immunofluorescence were used to detect tissue protein staining in 105 patients with LUAD. LEF1, TCF7, TCF7L1 and TCF7L2 were all aberrantly expressed in the tumor tissues of LUAD patients with the data from The Cancer Genome Atlas (TCGA) database, tumor immune estimation resource (TIMER) database and results of immunohistochemistry, but only LEF1 expression was associated with 5-year overall survival in LUAD patients. LEF1 protein expression was associated with advanced tumor node metastasis (TNM) stage, lymphatic metastasis and local invasion in 105 cases LUAD patients. At the same time, LEF1 mRNA expression was also associated with immunosuppressive microenvironment in LUAD patients with the data from TCGA database and TIMER database. Results of immunohistochemistry and immunofluorescence in tumor tissues of 105 cases LUAD patients showed that there was a positively correlation between LEF1 protein expression and the infiltration of M2 macrophages and Treg cells. LEF1 was highly expressed in tumor tissues of LUAD patients, and highly expressed LEF1 was associated with the immunosuppressive microenvironment of LUAD patients.

Prognostic factors for resected invasive mucinous lung adenocarcinoma: a systematic review and meta-analysis.

BACKGROUND: Surgery is the optimal choice for early invasive mucinous lung adenocarcinoma (IMA). A systematic review and meta-analysis were conducted to explore the prognostic factors for resected IMA. METHODS: We systematically reviewed the prognostic role of clinicopathological and genomic factors in resected IMA patients. Eligible studies on the treatment of IMA following the systematic search of PubMed, Embase and the Cochrane Library from January 2015 to January 2024 were identified. Outcomes of interest were overall survival (OS) and disease-free survival/recurrence-free survival (DFS/RFS). The hazard ratio (HR) and 95% confidence interval (CI) were used as impact indicators for systematic review and meta-analysis. RESULTS: Sixteen studies involving 3,484 patients with IMA were included. The results of the combined analysis showed that male and smoking were associated with a worse prognosis. Furthermore, advanced clinical stage, poor differentiation grade, presence of visceral pleural invasion (VPI) and spread through air spaces (STAS), and presence of KRAS mutations were also associated with worse prognosis. CONCLUSIONS: Gender, smoking, clinical stage, tumor size, differentiation grading, VPI, STAS and KRAS mutation affect DFS/RFS and OS of IMA patients after surgery. Identifying these factors may aid physicians in developing more individualized treatment plans for resectable IMA patients.

C6 and KLRG2 are pyroptosis subtype-related prognostic biomarkers and correlated with tumor-infiltrating lymphocytes in lung adenocarcinoma.

Pyroptosis plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the pyroptosis-related gene (PRG) expression pattern and to identify promising pyroptosis-related biomarkers to improve the prognosis of LUAD. The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the 44 differentially expressed PRGs (DEPRGs). Then, consensus clustering revealed pyroptosis-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic PRGs. After overlapping DEGs and the Lasso regression analysis-based prognostic genes, the predictive risk model was established and validated. Correlation analysis between PRGs and clinicopathological variables was also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels, the risk score, and clinicopathological variables in the predictive risk model. A total of 52 genes from the PubMed were identified as PRGs, and 44 of the 52 genes were pooled as DEPRGs. The most significant GO term was "collagen trimer" (P = 2.46E-13), and KEGG analysis results indicated that 44 DEPRGs were significantly enriched in Salmonella infection (P < 0.001). Then, consensus clustering analysis divided LUAD patients into two clusters, and a total of 79 DEGs were identified according to these cluster subtypes. Subsequently, univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators and we also performed Lasso regression analysis and screened 23 DEGs. After overlapping 23 DEGs and 12 genes, only 4 (KLRG2, MAPK4, C6 and SFRP5) of 12 genes were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model effectively predicted the prognosis (P < 0.001). Combined with the correlation analysis results between the 4 genes and clinicopathological variables, C6 and KLRG2 were screened as prognostic genes. In this study, we constructed a predictive risk model and identified two pyroptosis subtype-related gene expression patterns to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful for accurately characterizing the LUAD and developing personalized treatment.

In-depth exploration of the focus issues of TKI combined with radiotherapy for EGFR-mutant lung adenocarcinoma patients with brain metastasis: a systematic analysis based on literature metrology, meta-analysis, and real-world observational data.

BACKGROUND: There is a growing interest in utilizing a combination of brain radiotherapy (RT) and tyrosine kinase inhibitors (TKIs) for patients diagnosed with brain metastases (BM) in epidermal growth factor receptor (EGFR) mutation-positive lung adenocarcinoma (LAC). The current status of this treatment strategy remains a subject of debate. METHODS: We initiated our study by conducting a comprehensive literature search using the SCI-expanded database of Web of Science Core Collection (WoSCC). We utilized the VOSPviewer software to analyze various aspects of the research, including the year of publication, authorship, keywords, and country.Subsequently, we performed an extensive and systematic literature search on popular online databases. Our primary outcome measures were overall survival (OS) and intracranial progression-free survival (iPFS), both quantified by hazard ratios (HRs). Additionally, for data verification, we included data from patients in non-small cell lung cancer with brain metastasis who underwent therapeutic intervention at the Cancer Prevention and Treatment Center of Sun Yat-sen University and the Radiotherapy Department of Hanzhong Central Hospital between August 2012 and November 2021. RESULTS: The bibliometric analysis revealed an increasing trend in research focused on the combination of RT and TKIs for the management of lung cancer brain metastases over the previous decade. Then, nine studies consistent with the research direction were included for meta-analysis. The meta-analysis showed that the OS (HR = 0.81, 95% confidence interval: 0.69-0.94; P = 0.007) and iPFS (HR = 0.71, 95% confidence interval: 0.61-0.82; P < 0.001) of the combination therapy were significantly prolonged. Finally, 168 EGFR-mutated BM advanced LAC patients in the real world were verified, and the median iPFS of the combination therapy (n = 88 and EGFR-TKIs alone (n = 80) were 16.0 and 9.0 months, respectively, (P < 0.001). The median OS was 29.0 and 27.0 months, respectively, with no dramatic difference (P = 0.188). CONCLUSIONS: Research on EGFR-mutant LAC brain metastasis has turned towards exploring optimal treatment strategies for this condition. Our meta-analysis and real-world data analysis consistently demonstrate that combination therapy offers a substantial improvement in patient survival compared to EGFR-TKI monotherapy. Notably, among patients undergoing salvage radiotherapy (RT), our subgroup analysis reveals that those initially treated with third-generation TKIs experience more significant benefits than those treated with first- or second-generation TKIs.

Unlocking the Potential of Disulfidptosis-Related LncRNAs in Lung Adenocarcinoma: A Promising Prognostic LncRNA Model for Survival and Immunotherapy Prediction.

OBJECTIVE: Disulfidptosis was stimulated in high SLC7A11 expression cells starving to glucose. We attempted to identify disulfidptosis-related lncRNAs (DRLs), built a prognostic model to predict survival, and analyzed the tumor microenvironment. METHODS: The TCGA database was utilized to procure the pertinent data. By utilizing both Cox regression and the least absolute shrinkage and selection operator (LASSO) method, a risk model based on DRLs was formulated for prognostic evaluation. The ability of survival prediction was validated by multiple approaches. The biological functions were screened through GO, KEGG, and GSEA. Various methods were employed to evaluate the tumor immune environment, which included ESTIMATE, tumor mutation burden (TMB) score, CIBERSORT algorithm, and tumor immune dysfunction and exclusion (TIDE) score. RESULTS: Ninety-one DRLs were recognized, and lncRNA AC092718.4, AL365181.2, AL606489.1, EMSLR, and ENTPD3-AS1 were involved in the risk model. The GEO database was used to verify the influence of these lncRNAs on survival. The following analyses showed that survival could be predicted excellently by the DRLs risk model. The results of enrichment analyses pointed toward the involvement of the cell cycle and IgA production pathways. In the low-risk patient group, there was a notable surge in stromal, immune, and ESTIMATE scores, while the TMB scores took a tumble. Conversely, the high-risk patient group displayed a converse trend. Notably, the group of patients with lower risk scores and higher TMB scores showed the most favorable survival outcomes, underscoring the importance of considering both risk score and TMB in predicting the response to immune checkpoint blockade therapy. Furthermore, patients classified as high-risk might display resistance to both chemotherapy and targeted therapy. Cellular biological experiments proved that lncRNA AC092718.4 promoted invasion, migration, and proliferation abilities in vitro. These results provided valuable insights into the role of DRLs in LUAD and presented a possible effective treatment approach for LUAD. CONCLUSIONS: We developed a disulfidptosis-related risk model with 5 lncRNAs that enables survival prediciton for LUAD patients and aids cilinical decisions by forecasting the TME, TMB, and drug sensitivity, making it a valuable tool for outcomes prediction.

RNA-seq and bulk RNA-seq data analysis of cancer-related fibroblasts (CAF) in LUAD to construct a CAF-based risk signature.

Angiogenesis, metastasis, and resistance to therapy are all facilitated by cancer-associated fibroblasts (CAFs). A CAF-based risk signature can be used to predict patients' prognoses for Lung adenocarcinoma (LUAD) based on CAF characteristics. The Gene Expression Omnibus (GEO) database was used to gather signal-cell RNA sequencing (scRNA-seq) data for this investigation. The GEO and TCGA databases were used to gather bulk RNA-seq and microarray data for LUAD. The scRNA-seq data were analyzed using the Seurat R program based on the CAF markers. Our goal was to use differential expression analysis to discover differentially expressed genes (DEGs) across normal and tumor samples in the TCGA dataset. Pearson correlation analysis was utilized to discover prognostic genes related with CAF, followed by univariate Cox regression analysis. Using Lasso regression, a risk signature based on CAF-related prognostic genes was created. A nomogram model was created based on the clinical and pathological aspects. 5 CAF clusters were identified in LUAD, 4 of which were associated with prognosis. From 2811 DEGs, 1002 genes were found to be significantly correlated with CAF clusters, which led to the creation of a risk signature with 8 genes. These 8 genes were primarily connected with 41 pathways, such as antigen paocessing and presentation, apoptosis, and cell cycle. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for LUAD, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of LUAD. CAF-based risk signatures can be effective in predicting the prognosis of LUAD, and they may provide new strategies for cancer treatments by interpreting the response of LUAD to immunotherapy.

Unveiling the Enigmatic Role of SLC35F3 in Lung Adenocarcinoma.

BACKGROUND: The role of solute carrier family 35 member F3 (SLC35F3) in lung adenocarcinoma (LUAD) remains unclear. To address this gap, we conducted a study employing bioinformatics analysis and experimental validation. METHODS: This study aimed to examine the expression patterns of SLC35F3 in various cancer types, particularly focusing on LUAD, by analyzing data from the Cancer Genome Atlas (TCGA) database to evaluate its clinical relevance. The research also explored potential regulatory mechanisms of SLC35F3, including its interactions with immune infiltration, tumor mutational burden (TMB), and drug sensitivity in LUAD. The investigation included analyzing SLC35F3 expression in single-cell sequencing of LUAD cells, examining genetic variations of SLC35F3 in LUAD, and assessing SLC35F3 expression in cell lines using quantitative real-time PCR (qRT-PCR). RESULTS: The aberrant expression of SLC35F3 was observed in both pan-cancer and LUAD. In LUAD patients, a statistically significant increase in SLC35F3 expression was correlated with gender (p < 0.001) and was associated with poorer overall survival (OS) (p = 0.020). The expression of SLC35F3 was identified as an independent prognostic determinant in patients with LUAD (p = 0.032). SLC35F3 exhibited associations with various pathways, including cell cycle and more. SLC35F3 expression demonstrated correlations with immune infiltration, TMB, and some drugs in LUAD. Results indicated significant upregulation of SLC35F3 in both LUAD tissues and cell lines. CONCLUSIONS: SLC35F3 may serve as a prognostic biomarker and immunotherapeutic target for patients with LUAD. CLINICAL TRIAL REGISTRATION: Not applicable.

Long-term prognostic characteristics of patients with clinical stage IA part-solid lung adenocarcinoma: a conditional survival analysis.

OBJECTIVES: Despite excellent 5-year survival, there are limited data on the long-term prognostic characteristics of clinical stage IA part-solid lung adenocarcinoma. The objective was to elucidate the dynamics of prognostic characteristics through conditional survival analysis. METHODS: Consecutive patients who underwent complete resection for clinical stage IA part-solid lung adenocarcinoma between 2011 and 2015 were retrospectively reviewed. Conditional survival is defined as the probability of surviving further y years, conditional on the patient has already survived x years. The conditional recurrence-free survival (CRFS) and conditional overall survival (COS) were analysed to evaluate prognosis over time, with conditional Cox regression analysis performed to identify time-dependent prognostic factors. RESULTS: A total of 1539 patients were included with a median follow-up duration of 98.4 months, and 80 (5.2%) patients experienced recurrence. Among them, 20 (1.3%) recurrence cases occurred after 5 years of follow-up with 100% intrathoracic recurrence. The 5-year CRFS increased from 95.8% to 97.4%, while the 5-year COS maintained stable. Multivariable Cox analysis revealed that histologic subtype was always an independent prognostic factor for CRFS even after 5 years of follow-up, while the independent prognostic value of consolidation-to-tumour ratio, visceral pleural invasion and lymph node metastasis was observed only within 5 years. Besides, age, pathologic size and lymph node metastasis maintained independent predictive value for COS during long-term follow-up, while consolidation-to-tumour ratio was predictive for COS only within 5 years of follow-up. CONCLUSIONS: The independent prognostic factors for clinical stage IA part-solid lung adenocarcinoma changed over time, along with gradually increasing 5-year CRFS and stable 5-year COS.

Detection of the Fatty Acid Metabolism-Linked Genes in Lung Adenocarcinoma as Biomarkers for Clinical Prognosis and Immunotherapeutic Targets.

BACKGROUND: Lung cancer, on a global scale, leads to the most common cases of cancer mortalities. Novel therapeutic approaches are urgently needed to disrupt this lethal disease. The rapid development of tumor immunology combining breakthroughs involving fatty acid metabolism brings possibilities. Directing fatty acid metabolism is supposed to help discover potential prognostic biomarkers and treatment targets for lung cancer. METHODS: Through searching the GSE140797 dataset, we identified genes related to fatty acid metabolism as well as fatty acid metabolism-related differentially expressed genes (DEGs). We applied various methods to ascertain the independent prognostic value of the DEGs. The methods we utilized entail prognostic analysis, differential expression analysis, as well as univariate and multivariate Cox regression analyses. The lasso Cox regression model was utilized in examining how DEGs correlate with the immune score, immune checkpoint, ferroptosis, methylation, and OCLR score. The expression levels of ACAT1 and ACSL3 in tissues derived from normal lung and lung adenocarcinoma (LUAD) tissues were compared by qRT-PCR. RESULTS: In this study, ACSL3 and ACAT1 were identified as fatty acid metabolism-related genes utilizing independent prognostic value and as a result, the risk prognostic model was built using these factors. qRT-PCR results implied that ACSL3 and ACAT1 expressions were upregulated and downregulated, correspondingly in tumor tissues. Additional evaluations suggested that ACSL3 and ACAT1 were affirmed to be remarkably correlated with the immune score, methylation, immune checkpoint, OCLR score, and ferroptosis. CONCLUSIONS: ACSL3 and ACAT1 were effective prognostic biomarkers and potential immunotherapeutic targets in LUAD.

The impact of postoperative adjuvant therapy on EGFR-mutated stage IA lung adenocarcinoma with micropapillary pathological subtypes.

BACKGROUND: Micropapillary (MPP) adenocarcinoma is considered one of the most aggressive pathological types of lung adenocarcinoma (LADC). This retrospective study aimed to evaluate the prognostic significance and benefit of postoperative adjuvant therapy (PAT) in stage IA LADC patients with different proportions of MPP components. MATERIALS AND METHODS: We retrospectively examined clinical stage IA LADC patients who underwent surgical resection between August 2012 and December 2019. In terms of the proportion of MPP components (TPM), the tumors were reclassified into three categories: MPP patterns absent (TPMN); low proportions of MPP components (TPML); and high proportions of MPP components (TPMH). The dates of recurrence and metastasis were identified based on physical examinations and were confirmed by histopathological examination. RESULTS: Overall, 505 (TPMN, n = 375; TPML, n = 92; TPMH, n = 38) patients harboring EGFR mutations were enrolled in the study. Male sex (P = 0.044), high pathological stage (P < 0.001), and MPP pathological subtype (P < 0.001) were more frequent in the TPM-positive (TPMP) group than in the TPM-negative (TPMN) group. Five-year disease-free survival (DFS) rates were significantly lower in the TPMP group than in the TPMN group (84.5% vs. 93.4%, P = 0.006). In addition, patients with high proportions (greater than 10%) of MPP components had worse overall survival (OS) (91.0% vs. 98.9%, P = 0.025) than those with low proportions (5%≤ TPM ≤ 10%). However, postoperative EGFR tyrosine kinase inhibitors (TKIs) or adjuvant chemotherapy (ACT) cannot improve DFS and OS between EGFR-mutated patients with different proportions of MPP components. CONCLUSION: MPP was related to earlier recurrence and shortened survival time, even in stage IA. Further research needs a larger sample size to clarify that EGFR-mutated stage IA patients with MPP components obtain survival benefits from adjuvant therapy.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

Introduction: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD. Methods: In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD. Results: We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma. Discussion: Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Nomogram predicting overall and cancer specific prognosis for poorly differentiated lung adenocarcinoma after resection based on SEER cohort analysis.

The prognosis of poorly differentiated lung adenocarcinoma (PDLA) is determined by many clinicopathological factors. The aim of this study is identifying prognostic factors and developing reliable nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with PDLA. Patient data from the Surveillance, Epidemiology and End Results (SEER) database was collected and analyzed. The SEER database was used to screen 1059 eligible patients as the study cohort. The whole cohort was randomly divided into a training cohort (n = 530) and a test cohort (n = 529). Cox proportional hazards analysis was used to identify variables and construct a nomogram based on the training cohort. C-index and calibration curves were performed to evaluate the performance of the model in the training cohort and test cohorts. For patients with PDLA, age at diagnosis, gender, tumor size were independent prognostic factors both for overall survival (OS) and cancer-specific survival (CSS), while race and number of nodes were specifically related to OS. The calibration curves presented excellent consistency between the actual and nomogram-predict survival probabilities in the training and test cohorts. The C-index values of the nomogram were 0.700 and 0.730 for OS and CSS, respectively. The novel nomogram provides new insights of the risk of each prognostic factor and can assist doctors in predicting the 1-year, 3-year and 5-year OS and CSS in patients with PDLA.

The prognostic effect of infiltrating immune cells is shaped by proximal M2 macrophages in lung adenocarcinoma.

The spatial arrangement of immune cells within the tumor microenvironment (TME) and their interactions play critical roles in the initiation and development of cancer. Several advanced technologies such as imaging mass cytometry (IMC) providing the immunological landscape of the TME with single-cell resolution. In this study, we develop a new method to quantify the spatial proximity between different cell types based on single-cell spatial data. Using this method on IMC data from 416 lung adenocarcinoma patients, we show that the proximity between different cell types is more correlated with patient prognosis compared to the traditional features such immune cell density and fractions. Consistent with previous reports, our results validate that proximity of T helper (Th) and B cells to cancer cells is associated with survival benefits. More importantly, we discover that the proximity of M2 macrophages to multiple immune cells is associated with poor prognosis. When Th/B cells are stratified into M2-distal and M2-proximal, the abundance of the former but not the latter category of Th/B cells is correlated with enhanced patient survival. Additionally, the abundance of M2-distal and M2-proximal cytotoxic T cells (Tc) is respectively associated with good and poor prognosis. Our results indicate that the prognostic effect of Th, Tc, and B cells in the tumor microenvironment is modulated by the nearby M2 macrophages. The proposed new method proposed can be readily applied to all single-cell spatial data for revealing functional impact of immune cell interactions.

Ferroptosis-related genes DUOX1 and HSD17B11 affect tumor microenvironment and predict overall survival of lung adenocarcinoma patients.

BACKGROUND: Recent studies have found that ferroptosis-related genes (FRGs) have broad applications in tumor therapy. However, the predictive potential of these genes in lung adenocarcinoma (LUAD) remains to be fully characterized. We aimed to investigate the FRGs that might be potential targets for LUAD. METHODS: We screened the RNA sequencing samples from LUAD patients from the GEO database and analyzed the ferroptosis-related differentially expressed genes (DEGs). A functional analysis of DEGs was performed. The risk model was constructed to evaluation and validation FRGs. We explored the immune landscape of LUAD and controls. The value of FRGs in diagnosing LUAD was tested in the GSE30219, GSE37745, GSE0081 datasets, and qPCR was used to verify their diagnostic value in LUAD patients in our hospital. RESULTS: A total of 1327 DEGs in quantitative proteomics were obtained, of which ferroptosis-related DEGs were 259. Enrichment analysis showed significant enrichment in the absorption and metabolism of fatty acids and arachidonic acid. The upregulated genes (GCLC, RRM2, AURKA, SLC7A5, and SLC2A1) and downregulated genes (ANGPTL7, ALOX15, ALOX15B, HSD17B11, IL33, TSC22D3, and DUOX1) were selected as core genes in tissue samples from 62 patients by qPCR. DUOX1 and HSD17B11 were obtained by bioinformatics analysis, both of which showed similar expression trends at the RNA and protein levels. The Kaplan-Meier method showed that DUOX1 and HSD17B11 were closely related to the overall survival (OS) of LUAD patients. CONCLUSION SUBSECTIONS: Ferroptosis-related genes DUOX1 and HSD17B11 are of considerable value in the diagnosis of LUAD patients. Their low expression suggests an increased recurrence rate and leads to a decrease in the patient quality of life.

Significance of novel PANoptosis genes to predict prognosis and therapy effect in the lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the dominant histotype of non-small cell lung cancer. Panoptosis, a comprehensive form of programmed cell death, is central to carcinogenesis. In this study, the expression of PANoptosis-related genes (PRGs) and their impact on the development, prognosis, tumor microenvironment, and treatment response of patients with lung adenocarcinoma (LUAD) were systematically evaluated. PRGs were selected from The Cancer Genome Atlas database and Genecards dataset using differential expression analysis. The signature of included PRGs was identified using univariate Cox regression analysis and LASSO regression analysis. Additionally, a nomogram was developed that includes signature and clinical information. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to assess the predictive validity of these risk models. Finally, functional analysis of the selected PRGs in signature and analysis of immune landscape were also performed. Preliminary identification of 10 genes related to PANoptosis has significant implications for prognosis. Subsequently, seven related genes were integrated to classify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and the TNM stage were as independent prognostic factors and were utilized in creating a nomogram plot. Both the features and the nomogram plot showed accurate performance in predicting the overall survival of LUAD patients. The PRGs were enriched in several biological functions and pathways, and stratified studies were conducted on the differences in immune landscape between high-risk and low-risk groups based on their characteristics. Ultimately, our evaluation focused on the differences in drug treatment efficacy between the high-risk and low-risk groups, providing a foundation for future research directions. Potential associations between PRGs and patient prognosis in LUAD have been identified in this study. Potential biomarkers for clinical application could be considered for the prognostic predictors identified in this study.

Multi­omics identification of a signature based on malignant cell-associated ligand-receptor genes for lung adenocarcinoma.

PURPOSE: Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. METHODS: We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. RESULTS: We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. CONCLUSION: We have identified malignant cell-associated ligand-receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.

An abnormal metabolism-related gene, ALG3, is a potential diagnostic and prognostic biomarker for lung adenocarcinoma.

BACKGROUND: To explore the abnormal metabolism-related genes that affect the prognosis of patients with lung adenocarcinoma (LUAD), and analyze the relationship with immune infiltration and competing endogenous RNA (ceRNA) network. METHODS: Transcriptome data of LUAD were downloaded from the Cancer Genome Atlas database. Abnormal metabolism-related differentially expressed genes in LUAD were screened by the R language. Cox analysis was used to construct LUAD prognostic risk model. Kaplan-Meier test, ROC curve and nomograms were used to evaluate the predictive ability of metabolic related gene prognostic model. CIBERSORT algorithm was used to analyze the relationship between risk score and immune infiltration. The starBase database constructed a regulatory network consistent with the ceRNA hypothesis. IHC experiments were performed to verify the differential expression of ALG3 in LUAD and paracancerous samples. RESULTS: In this study, 42 abnormal metabolism-related differential genes were screened. After survival analysis, the final 5 metabolism-related genes were used as the construction of prognosis model, including ALG3, COL7A1, KL, MST1, and SLC52A1. In the model, the survival rate of LUAD patients in the high-risk subgroup was lower than that in the low-risk group. In addition, the risk score of the constructed LUAD prognostic model can be used as an independent prognostic factor for patients. According to the analysis of CIBERSORT algorithm, the risk score is related to the infiltration of multiple immune cells. The potential ceRNA network of model genes in LUAD was constructed through the starBase database. IHC experiments revealed that ALG3 expression was upregulated in LUAD. CONCLUSION: The prognostic model of LUAD reveals the relationship between metabolism and prognosis of LUAD, and provides a novel perspective for diagnosis and research of LUAD.