Prognosis In Lung Squamous Cell Carcinoma Research Articles

Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning

Lung squamous cell carcinoma (LUSC), a subtype of non–small cell lung cancer, represents a significant portion of lung cancer cases with distinct histologic patterns impacting prognosis and treatment. The current pathological assessment methods face limitations such as interobserver variability, necessitating more reliable techniques. This study seeks to predict lymph node metastasis in LUSC using deep learning models applied to histopathology images of primary tumors, offering a more accurate and objective method for diagnosis and prognosis. Whole slide images (WSIs) from the Outdo-LUSC and the cancer genome atlas cohorts were used to train and validate deep learning models. Multiinstance learning was applied, with patch-level predictions aggregated into WSI-level outcomes. The study employed the ResNet-18 network, transfer learning, and rigorous data preprocessing. To represent WSI features, innovative techniques like patch likelihood histogram and bag of words were used, followed by training of machine learning classifiers, including the ExtraTrees algorithm. The diagnostic model for lymph node metastasis showed strong performance, particularly using the ExtraTrees algorithm, as demonstrated by receiver operating characteristic curves and gradient-weighted class activation mapping visualizations. The signature generated by the ExtraTrees algorithm, named lymph node status-related in situ LUSC histopathology (LN_ISLUSCH), achieved an area under the curve of 0.941 (95% CI: 0.926-0.955) in the training set and 0.788 (95% CI: 0.748-0.827) in the test set. Kaplan-Meier analyses confirmed that the LN_ISLUSCH model was a significant prognostic factor (P = .02). This study underscores the potential of artificial intelligence in enhancing diagnostic precision in pathology. The LN_ISLUSCH model stands out as a promising tool for predicting lymph node metastasis and prognosis in LUSC. Future studies should focus on larger and more diverse cohorts and explore the integration of additional omics data to further refine predictive accuracy and clinical utility.

Regulator of nonsense transcripts 3B is a prognostic biomarker and associated with immune cell infiltration in lung squamous cell and hepatocellular carcinoma

PurposeThe characteristic of RENT3B in cancer remains ambiguous. We aimed to study the relationship between RENT3B and immune infiltration in liver hepatocellular carcinoma (LIHC) and lung squamous cell carcinoma (LUSC).Patients and methodsWe investigated the expression levels of RENT3B using ONCOMINE and TIMER databases, and assessed the interrelationship between RENT3B expression and survival using PrognoScan, GEPIA, and Kaplan–Meier plotter. Additionally, we examined the association between RENT3B and immune cells in the tumor microenvironment (TME), as well as markers of immune cells, using TIMER. Subsequently, we performed prognostic analysis based on the expression level of RENT3B within specific immune cell subgroups. Furthermore, we evaluated the promoter methylation profile of RENT3B between tumor and normal tissues in LIHC and LUSC using the DNMIVD database.ResultsRENT3B exhibited increased levels in both in LIHC and LUSC. High RENT3B expression was associated with unfavorable prognosis in LIHC, whereas it indicated a beneficial prognosis in LUSC. In LIHC, the expression of RENT3B positively correlated with immune infiltration levels of B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells. However, in LUSC, the expression of RENT3B showed a negative correlation with immune infiltration levels of B cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells. RENT3B exhibited positive correlations with 42 immune markers in LIHC, while it displayed negative associations with 10 immune markers in LUSC. Despite variations in immune cell enrichment and reduction subgroups, high RENT3B expression consistently indicated poor prognosis in LIHC, whereas it remained favorable in LUSC. Additionally, there were no significant differences observed in RENT3B promoter methylation between tumor and normal tissues in both LIHC and LUSC.ConclusionRENT3B can affect the overall tumor prognosis and is associated with immune infiltration, especially in LIHC and LUSC. Consequently, RENT3B can become a prognostic biomarker for LIHC and LUSC.

A novel histopathological feature of spatial tumor-stroma distribution predicts lung squamous cell carcinoma prognosis.

We used a mathematical approach to investigate the quantitative spatial profile of cancer cells and stromain lung squamous cell carcinoma tissues and its clinical relevance. The study enrolled 132 patients with 3-5 cm peripheral lung squamous cell carcinoma, resected at the National Cancer Center Hospital East. We utilized machine learning to segment cancer cells and stroma on cytokeratin AE1/3 immunohistochemistry images. Subsequently, a spatial form of Shannon's entropy was employed to precisely quantify the spatial distribution of cancer cells and stroma. This quantification index was defined as the spatial tumor-stroma distribution index (STSDI). The patients were classified as STSDI-low and -high groups for clinicopathological comparison. The STSDI showed no significant association with baseline clinicopathological features, including sex, age, pathological stage, and lymphovascular invasion. However, the STSDI-low group had significantly shorter recurrence-free survival (5-years RFS: 49.5% vs. 76.2%, p < 0.001) and disease-specific survival (5-years DSS: 53.6% vs. 81.5%, p < 0.001) than the STSDI-high group. In contrast, the application of Shannon's entropy without spatial consideration showed no correlation with patient outcomes. Moreover, low STSDI was an independent unfavorable predictor of tumor recurrence and disease-specific death (RFS; HR = 2.668, p < 0.005; DSS; HR = 3.057, p < 0.005), alongside the pathological stage. Further analysis showed a correlation between low STSDI and destructive growth patterns of cancer cells within tumors, potentially explaining the aggressive nature of STSDI-low tumors. In this study, we presented a novel approach for histological analysis of cancer tissues that revealed the prognostic significance of spatial tumor-stroma distribution in lung squamous cell carcinoma.

Immunogenic cell death-related genes predict prognosis and response to immunotherapy in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a malignancy with limited therapeutic options. Immunogenic cell death (ICD) has the potential to enhance the efficacy of cancer therapy by triggering immune responses. We aimed to explore the potential of ICD-based classification in predicting prognosis and response to immunotherapy for LUSC. RNA-seq information and clinical data of LUSC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. ICD-related gene expressions in LUSC samples were analyzed by consensus clustering. Subsequently, differentially expressed genes (DEGs) between different ICD-related subsets were analyzed. Tumor mutation burden, immune cell infiltration, and survival analyses were conducted between different ICD subsets. Finally, an ICD-related risk signature was constructed and evaluated in LUSC patients, and the immunotherapy responses based on the gene expressions were also forecasted. ICD-high and ICD-low groups were defined, and 1466 DEGs were identified between the two subtypes. These DEGs were mainly enriched in collagen-containing extracellular matrix, cytokine-cytokine receptor interaction, the PI3K-Akt signaling pathway, and neuroactive ligand-receptor interaction. Furthermore, the ICD-low group exhibited a favorable prognosis, enhanced TTN and MUC16 mutation frequencies, increased infiltrating immune cells, and downregulated immune checkpoint expressions. Furthermore, we demonstrated that an ICD-related model (based on CD4, NLRP3, NT5E, and TLR4 genes) could forecast the prognosis of LUSC, and ICD risk scores were lower in the responder group. In summary, the predicted values of ICD-related genes (CD4, NLRP3, NT5E, and TLR4) for the prognosis and response to immunotherapy in LUSC were verified in the study, which benefits immunotherapy-based interventions for LUSC patients.

Specific association of MTHFD1 expressions with small cell lung cancer development and chemoradiotherapy outcome.

To identify biomarkers that can discriminated small cell lung cancer (SCLC) from non-SCLC (NSCLC), and explore their association with the prognosis of SCLC under chemoradiotherapy. The GSE40275 dataset was used to identify potential targets in SCLC. There were 196 patients of lung cancer (LC) in cohort 1 of this study. MTHFD1 levels in tissues were determined by immunohistochemistry assay in cohort 1. Lung cancer patients who were all underwent local chemoradiotherapy (CRT) were included in cohort 2, and the association of MTHFD1 levels with CRT treatment outcome were determined in cohort 2. Cell experiments were used to determine the function of MTHFD1 on the radio-sensitivity of SCLC and NSCLC cells. The MTHFD1 levels in LC tissues were increased, and could discriminate SCLC from both lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Small cell lung cancer patients with MTHFD1 high phenotype had a poorer prognosis after CRT treatment, whereas no significant correlation was found between MTHFD1 levels and prognosis in LUSC and LUAD group. Cell experiments demonstrated that overexpression of MTHFD1 increases radio-resistance in both SCLC and NSCLC in vitro. MTHFD1 expressions might be a novel specifically prognostic biomarker for SCLC and the CRT treatment outcome.

Impacts of cytoplasmic p53 aggregates on the prognosis and the transcriptome in lung squamous cell carcinoma.

The tumor suppressor TP53 gene, the most frequently mutated gene in human cancers, produces the product tumor protein p53, which plays an essential role in DNA damage. p53 protein mutations may contribute to tumorigenesis by loss of tumor suppressive functions and malignancy of cancer cells via gain-of-oncogenic functions. We previously reported that mutant p53 proteins form aggregates and that cytoplasmic p53 aggregates were associated with poor prognosis in human ovarian cancer. However, the prognostic impact of p53 aggregation in other tumors including lung squamous cell carcinoma (SCC) is poorly understood. Here, we demonstrated that lung SCC cases with cytoplasmic p53 aggregates had a significantly poor clinical prognosis. Analysis via patient-derived tumor organoids (PDOs) established from lung SCC patients and possessing cytoplasmic p53 aggregates showed that eliminating cytoplasmic p53 aggregates suppressed cell proliferation. RNA sequencing and transcriptome analysis of p53 aggregate-harboring PDOs indicated multiple candidate pathways involved in p53 aggregate oncogenic functions. With lung SCC-derived cell lines, we found that cytoplasmic p53 aggregates contributed to cisplatin resistance. This study thus shows that p53 aggregates are a predictor of poor prognosis in lung SCC and suggests that detecting p53 aggregates via p53 conventional immunohistochemical analysis may aid patient selection for platinum-based therapy.

The Comprehensive Characterization of B7-H3 Expression in the Tumor Microenvironment of Lung Squamous Cell Carcinoma: A Retrospective Study.

Lung squamous cell carcinoma (LSCC) is refractory to various therapies for non-small cell cancer; therefore, new therapeutic approaches are required to improve the prognosis of LSCC. Although immunotherapies targeting B7 family molecules were explored as treatments for several cancer types, the expression and significance of B7-H3 in the tumor microenvironment (TME) and its relationship with other immune checkpoint molecules have not yet been investigated in detail. We used high-throughput quantitative multiplex immunohistochemistry to examine B7-H3 expression in the TME. We investigated the relationship between B7-H3 expression and prognosis as well as changes in the TME with B7-H3 expression using 110 surgically resected pathological specimens retrospectively. We examined the correlation between B7-H3 and programmed cell death-ligand 1 (PD-L1) expression in single cells. High B7-H3 expression in tumor cells was associated with a better prognosis and a significant increase in the number of CD163+PD-L1+ macrophages. Quantitative analysis revealed that there is a positive correlation between B7-H3 and PD-L1 expression in tumor and stromal cells, as well as in intratumoral tumor-infiltrating lymphocytes and tumor-associated macrophages in the same cells. CD68+, CD163+, and CK+ cells with PD-L1+ phenotypes had higher B7-H3 expression compared to PD-L1- cells. Our findings demonstrate a correlation between B7-H3 and PD-L1 expression in the same cells, indicating that therapies targeting B7-H3 could provide additional efficacy in patients refractory to PD-L1-targeting therapies.

THY1 is a prognostic-related biomarker via mediating immune infiltration in lung squamous cell carcinoma (LUSC).

Thymus cell antigen 1 (THY1) has been proven to play pivotal roles in many diseases. However, we do not fully understand its functional mechanism, especially in lung squamous cell carcinoma (LUSC). Here, we aimed to perform a comprehensive analysis to explore the expression and prognostic values of THY1 in LUSC using bioinformatic technology. Some online public databases (e.g., ONCOMINE, PrognoScan, TIMER, Kaplan-Meier plotter, STRING, LinkedOmics, and GEPIA) were used to explore the expression, prognostic significance, and potential molecular mechanism of THY1. The analysis indicated that THY1 was significantly up-regulated and closely correlated with poor prognosis in many malignant tumors, including LUSC. Further analysis revealed that over-expression of THY1 was significantly correlated with clinicopathological parameters (e.g., individual cancer stage, age, smoking habits, nodal metastasis status, and TP53 mutation status) in LUSC. The CpG islands methylation of THY1 was negatively correlated with THY1 mRNA expression in The Cancer Genome Atlas Program (TCGA). Further enrichment analysis of THY1 correlated genes revealed that they were mainly correlated with the formation of extracellular matrix (ECM), and got involved in the pathway of epithelial mesenchymal transition (EMT). Furthermore, differentially expressed THY1 was significantly correlated with immune cell infiltrations and poor prognosis in LUSC. In summary, bioinformatic analysis demonstrated that THY1 was significantly over-expressed and closely correlated with unfavorable prognosis in LUSC, which may apply as a promising diagnostic and therapeutic biomarker for LUSC in the future.

Different types of tumor microvessels in stage I-IIIA squamous cell lung cancer and their clinical significance.

Lung cancer (LC) is the leading cause of morbidity and mortality among malignant neoplasms. Improving the diagnosis and treatment of LC remains an urgent task of modern oncology. Previously, we established that in gastric, breast and cervical cancer, tumor microvessels (MVs) differ in morphology and have different prognostic significance. The connection between different types of tumor MVs and the progression of LC is not well understood. To evaluate the morphological features and clinical significance of tumor MVs in lung squamous cell carcinoma (LUSC). A single-center retrospective cohort study examined medical records and archival paraffin blocks of 62 and 180 patients with stage I-IIIA LUSC in the training and main cohorts, respectively. All patients underwent radical surgery (R0) at the Orenburg Regional Cancer Clinic from May/20/2009 to December/14/2021. Tumor sections were routinely processed, and routine Mayer's hematoxylin and eosin staining and immunohistochemical staining for cluster of differentiation 34 (CD34), podoplanin, Snail and hypoxia-inducible factor-1 alpha were performed. The morphological features of different types of tumor MVs, tumor parenchyma and stroma were studied according to clinicopathological characteristics and LUSC prognosis. Statistical analysis was performed using Statistica 10.0 software. Univariate and multivariate logistic regression analyses were performed to identify potential risk factors for LUSC metastasis to regional lymph nodes (RLNs) and disease recurrence. Receiver operating characteristic curves were constructed to discriminate between patients with and without metastases in RLNs and those with and without disease recurrence. The effectiveness of the predictive models was assessed by the area under the curve. Survival was analyzed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between patient subgroups. A value of P < 0.05 was considered to indicate statistical significance. Depending on the morphology, we classified tumor vessels into the following types: normal MVs, dilated capillaries (DCs), atypical DCs, DCs with weak expression of CD34, "contact-type" DCs, structures with partial endothelial linings, capillaries in the tumor solid component and lymphatic vessels in lymphoid and polymorphocellular infiltrates. We also evaluated the presence of loose, fine fibrous connective tissue (LFFCT) and retraction clefts in the tumor stroma, tumor spread into the alveolar air spaces (AASs) and fragmentation of the tumor solid component. According to multivariate analysis, the independent predictors of LUSC metastasis in RLNs were central tumor location (P < 0.00001), the presence of retraction clefts (P = 0.003), capillaries in the tumor solid component (P = 0.023) and fragmentation in the tumor solid component (P = 0.009), whereas the independent predictors of LUSC recurrence were tumor grade 3 (G3) (P = 0.001), stage N2 (P = 0.016), the presence of LFFCT in the tumor stroma (P < 0.00001), fragmentation of the tumor solid component (P = 0.0001), and the absence of tumor spread through the AASs (P = 0.0083). The results obtained confirm the correctness of our previously proposed classification of different types of tumor vessels and may contribute to improving the diagnosis and treatment of LUSC.

Significance of NKX2-1 as a biomarker for clinical prognosis, immune infiltration, and drug therapy in lung squamous cell carcinoma.

This study was performed to determine the biological processes in which NKX2-1 is involved and thus its role in the development of lung squamous cell carcinoma (LUSC) toward improving the prognosis and treatment of LUSC. Raw RNA sequencing (RNA-seq) data of LUSC from The Cancer Genome Atlas (TCGA) were used in bioinformatics analysis to characterize NKX2-1 expression levels in tumor and normal tissues. Survival analysis of Kaplan-Meier curve, the time-dependent receiver operating characteristic (ROC) curve, and a nomogram were used to analyze the prognosis value of NKX2-1 for LUSC in terms of overall survival (OS) and progression-free survival (PFS). Then, differentially expressed genes (DEGs) were identified, and Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were used to clarify the biological mechanisms potentially involved in the development of LUSC. Moreover, the correlation between the NKX2-1 expression level and tumor mutation burden (TMB), tumor microenvironment (TME), and immune cell infiltration revealed that NKX2-1 participates in the development of LUSC. Finally, we studied the effects of NKX2-1 on drug therapy. To validate the protein and gene expression levels of NKX2-1 in LUSC, we employed immunohistochemistry(IHC) datasets, The Gene Expression Omnibus (GEO) database, and qRT-PCR analysis. NKX2-1 expression levels were significantly lower in LUSC than in normal lung tissue. It significantly differed in gender, stage and N classification. The survival analysis revealed that high expression of NKX2-1 had shorter OS and PFS in LUSC. The multivariate Cox regression hazard model showed the NKX2-1 expression as an independent prognostic factor. Then, the nomogram predicted LUSC prognosis. There are 51 upregulated DEGs and 49 downregulated DEGs in the NKX2-1 high-level groups. GO, KEGG and GSEA analysis revealed that DEGs were enriched in cell cycle and DNA replication.The TME results show that NKX2-1 expression was positively associated with mast cells resting, neutrophils, monocytes, T cells CD4 memory resting, and M2 macrophages but negatively associated with M1 macrophages. The TMB correlated negatively with NKX2-1 expression. The pharmacotherapy had great sensitivity in the NKX2-1 low-level group, the immunotherapy is no significant difference in the NKX2-1 low-level and high-level groups. The analysis of GEO data demonstrated concurrence with TCGA results. IHC revealed NKX2-1 protein expression in tumor tissues of both LUAD and LUSC. Meanwhile qRT-PCR analysis indicated a significantly lower NKX2-1 expression level in LUSC compared to LUAD. These qRT-PCR findings were consistent with co-expression analysis of NKX2-1. We conclude that NKX2-1 is a potential biomarker for prognosis and treatment LUSC. A new insights of NKX2-1 in LUSC is still needed further research.

Diagnostic Value of GDF10 for the Tumorigenesis and Immune Infiltration in Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (LUSC) is associated with a low survival rate. Evidence suggests that bone morphogenetic proteins (BMPs) and their receptors (BMPRs) play crucial roles in tumorigenesis and progression. However, a comprehensive analysis of their role in LUSC is lacking. Our study aimed to explore the relationship between BMPs/BMPRs expression levels and the tumorigenesis and prognosis of LUSC. The "R/Limma" package was utilized to analyze the differential expression characteristics of BMPs/BMPRs in LUSC, using data from TCGA, GTEx, and GEO databases. Concurrently, the "survminer" packages were employed to investigate their prognostic value and correlation with clinical features in LUSC. The core gene associated with LUSC progression was further explored through weighted gene correlation network analysis (WGCNA). LASSO analysis was conducted to construct a prognostic risk model for LUSC. Clinical specimens were examined by immunohistochemical analysis to confirm the diagnostic value in LUSC. Furthermore, based on the tumor immune estimation resource database and tumor-immune system interaction database, the role of the core gene in the tumor microenvironment of LUSC was explored. GDF10 had a significant correlation only with the pathological T stage of LUSC, and the protein expression level of GDF10 decreased with the tumorigenesis of LUSC. A prognostic risk model was constructed with GDF10 as the core gene and 5 hub genes (HRASLS, HIST1H2BH, FLRT3, CHEK2, and ALPL) for LUSC. GDF10 showed a significant positive correlation with immune cell infiltration and immune checkpoint expression. GDF10 might serve as a diagnostic biomarker reflecting the tumorigenesis of LUSC and regulating the tumor immune microenvironment to guide more effective treatment for LUSC.

Tumor microenvironment characteristics and prognostic role of m6A modification in lung squamous cell carcinoma

BackgroundIt has recently been determined that N6-methyladenosine (m6A) RNA methylation regulators have prominent effects on several cancers. However, the potential role of m6A modification in lung squamous cell carcinoma (LUSC) remains unclear. MethodsWe evaluated the modification pattern of m6A and studied the biological function of m6A regulators in LUSC. Then, we constructed the m6Ascore to predict the prognosis of LUSC and analyzed the relationship between the m6Ascore and tumor mutation burden, immune cell infiltration, and immunotherapy. ResultIn the unsupervised consensus cluster analysis, three different m6Aclusters were identified, which correspond to an immune activation state, a moderate immune activation state, and an immune tolerance state. Forty-two genes related to the m6A phenotype were used to construct the m6Ascore; subsequently, multiple validations of the m6Ascore were carried out to determine the relationship between the score and immune cell infiltration and response to CTLA-4/PD-1 inhibitor treatment. Further analysis revealed that the m6Ascore could effectively predict the prognosis of LUSC and that the m6A phenotype-related genes, FAM162A and LOM4, might be potential biomarkers. ConclusionThese findings highlight the potential role of m6A modification in the prognosis, TME, and immunotherapy of LUSC and have profound implications for developing more effective personalized treatment strategies for LUSC.

A novel gene signature related to focal adhesions for distinguishing and predicting the prognosis of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a devastating and difficult-to-treat type of lung cancer, and the prognosis of LUSC is the worst. The functional roles of focal adhesion-related genes were explored in LUSC based on data from The Cancer Genome Atlas (TCGA). RNA sequencing data and clinical characteristics of LUSC patients in TCGA-LUSC were obtained from the TCGA database. Through systematic analysis, we screened the prognostic genes and determined the focal adhesion-related pathways closely associated with LUSC. We identified 444 prognostic genes and focal adhesion-related pathways intimately associated with LUSC. According to the focal adhesion-related genes, TCGA-LUSC patients were well divided into two groups: the low-risk group (G1) and the high-risk group (G2). A differential expression analysis identified 44 differentially expressed genes (DEGs) upregulated in the low-risk G1 group and 379 DEGs upregulated in the high-risk G2 group. The upregulated DEGs in the G1 group were primarily related to tyrosine metabolism, steroid hormone biosynthesis, retinol metabolism, platinum drug resistance, pentose and glucuronate interconversions, and metabolism of xenobiotics by cytochrome P450, while the downregulated DEGs in the G1 group were primarily related to ECM-receptor interaction, focal adhesion, proteoglycans in cancer, small cell lung cancer, cytokine-cytokine receptor interaction, and TGF-beta signaling pathway. The immune activity of the G1 group was lower than that of the G2 group, and the half-maximal inhibitory concentration (IC50) of five chemotherapy drugs (i.e., gemcitabine, methotrexate, vinorelbine, paclitaxel, and cisplatin) was significantly different between the G1 and G2 groups. Furthermore, a 10-gene prognostic model was constructed to predict the prognosis for LUSC patients: ITGA3, VAV2, FLNC, FLT4, HGF, MYL2, ITGB1, PDGFRA, CCND2, and PPP1CB. The status of focal adhesion-related genes has a close relationship with tumor classification and immunity in LUSC patients. A novel focal adhesion-related signature had good prognostic and predictive performance for LUSC. Our findings may provide new insight into the diagnosis and treatment of LUSC.

Prediction of Prognostic Features Based on Neutrophil-Related Genes for Lung Squamous Cell Carcinoma Reveals Immune Landscape and Drug Candidates.

Background: Since to the prognosis of lung squamous cell carcinoma is generally poor, there is an urgent need to innovate new prognostic biomarkers and therapeutic targets to improve patient outcomes. Objectives: Our goal was to develop a novel multi-gene prognostic model linked to neutrophils for predicting lung squamous cell carcinoma prognosis. Methods: We utilized messenger RNA expression profiles and relevant clinical data of lung squamous cell carcinoma patients from the Cancer Genome Atlas database. Through K-means clustering, least absolute shrinkage and selection operator regression, and univariate/multivariate Cox regression analyses, we identified 12 neutrophil-related genes strongly related to patient survival and constructed a prognostic model. We verified the stability of the model in the Cancer Genome Atlas database and gene expression omnibus validation set, demonstrating the robust predictive performance of the model. Results: Immunoinfiltration analysis revealed remarkably elevated levels of infiltration for natural killer cells resting and monocytes in the high-risk group compared to the low-risk group, while macrophages had considerably lower infiltration in the high risk group. Most immune checkpoint genes, including programmed cell death protein 1 and cytotoxic T-lymphocyte-associated antigen 4, exhibited high expression levels in the high risk group. Tumor immune dysfunction and exclusion scores and immunophenoscore results suggested a potential inclination toward immunotherapy in the "RIC" version V2 revised high risk group. Moreover, prediction results from the CellMiner database revealed great correlations between drug sensitivity (e.g., Vinorelbine and PKI-587) and prognostic genes. Conclusion: Overall, our study established a reliable prognostic risk model that possessed significant value in predicting the overall survival of lung squamous cell carcinoma patients and may guide personalized treatment strategies. (Rev Invest Clin. 2024;76(2):116-31).

Identification of tumor-suppressor genes in lung squamous cell carcinoma through integrated bioinformatics analyses.

Lung cancer is a prevalent malignancy, and fatalities of the disease exceed 400,000 cases worldwide. Lung squamous cell carcinoma (LUSC) has been recognized as the most common pathological form of lung cancer. The comprehensive understanding of molecular features related to LUSC progression has great significance in LUSC prognosis assessment and clinical management. In this study, we aim to identify a panel of signature genes closely associated with LUSC, which can provide novel insights into the progression of LUSC. Gene expression profiles were retrieved from public resources including gene expression omnibus (GEO) and the cancer genome atlas (TCGA) database. Differentially expressed genes (DEGs) between LUSC specimens and normal lung tissues were identified by bioinformatics analyses. A total of 66 DEGs were identified based on two cohorts of data. CytoHubba plugin of Cytoscape software was utilized for the further analyses of the top 10 candidate hub genes including OGN, ABI3BP, MAMDC2, FGF7, FAM107A, SPARCL1, DCN, COL14A1, and MFAP4 and CHRDL1, which showed significant downregulation in LUSC. Two LUSC cell lines were used to validate the functions of CHRDL1 and FAM107A through overexpression experiment. Together, our data revealed novel candidate tumor-suppressor genes in LUSC, suggesting previously unappreciated mechanisms in the progression of LUSC.

Establishment of a prognosis prediction model for lung squamous cell carcinoma related to PET/CT: basing on immunogenic cell death-related lncRNA

BackgroundImmunogenic cell death (ICD) stimulates adaptive immunity and holds significant promise in cancer therapy. Nevertheless, the influence of ICD-associated long non-coding RNAs (lncRNAs) on the prognosis of patients with lung squamous cell carcinoma (LUSC) remains unexplored.MethodsWe employed data from the The Cancer Genome Atlas (TCGA)database to identify ICD-related lncRNAs associated with the prognosis of LUSC using univariate Cox regression analysis. Subsequently, we utilized the LOSS regression model to construct a predictive risk model for assessing the prognosis of LUSC patients based on ICD-related lncRNAs. Our study randomly allocated187 TCGA patients into a training group and 184 patients for testing the predictive model. Furthermore, we conducted quantitative polymerase chain reaction (qPCR) analysis on 43 tumor tissues from LUSC patients to evaluate lncRNA expression levelsPearson correlation analysis was utilized to analyze the correlation of risk scores with positron emission tomography/computed tomography (PET/CT) parameters among LUSC patients.ResultsThe findings from the univariate Cox regression revealed 16 ICD-associated lncRNAs linked to LUSC prognosis, with 12 of these lncRNAs integrated into our risk model utilizing the LOSS regression. Survival analysis indicated a markedly higher overall survival time among patients in the low-risk group compared to those in the high-risk group. The area under the Receiver operating characteristic (ROC) curve to differentiate high-risk and low-risk patients was 0.688. Additionally, the overall survival rate was superior in the low-risk group compared to the high-risk group. Correlation analysis demonstrated a positive association between the risk score calculated based on the ICD-lncRNA risk model and the maximum standard uptake value (SUVmax) (r = 0.427, P = 0.0043) as well as metabolic volume (MTV)of PET-CT (r = 0.360, P = 0.0177) in 43 LUSC patients.ConclusionWe have successfully developed a risk model founded on ICD-related lncRNAs that proves effective in predicting the overall survival of LUSC patients.

Systematic analysis of apoptosis-related genes in the prognosis of lung squamous cell carcinoma: a combined single-cell RNA sequencing study.

Lung squamous cell carcinoma (LUSC) has a poor prognosis and lacks appropriate diagnostic and treatment strategies. Apoptosis dysregulation is associated with tumor occurrence and drug resistance, but the prognostic value of apoptosis-related genes (ARGs) in LUSC remains unclear. Using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis based on differentially expressed ARGs, we constructed an ARG-related prognostic model for LUSC survival rates. We conducted correlation analysis of prognostic ARGs by incorporating the dataset of normal lung tissue from the Genotype-Tissue Expression (GTEx) database. We then constructed a risk model, and the predictive ability of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Non-small cell lung cancer (NSCLC) single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, these data were subjected to single-cell analysis. Cell subgroups were determined and annotated by dimensionality reduction clustering, and the cell subgroups in disease development were identified via pseudotemporal analysis with the Monocle 2 algorithm. We identified four significantly prognostic ARGs and constructed a stable prognostic risk model. Kaplan-Meier curve analysis showed that the high-risk group had a poorer prognosis (P<0.05). Furthermore, the ROC analysis of 3-, 5- and 7-year survival rates confirmed that the model had good predictive value for patients with LUSC. Single-cell RNA sequencing showed the prognostic ARGS were enriched in epithelial cells, smooth muscle cells, and T cells. Pseudotime analysis was used to infer the differentiation process and time sequence of cells. This study identified ARGs that are associated with prognosis in LUSC, and a risk model based on these prognostic genes was constructed that could accurately predict the prognosis of LUSC. Single-cell sequencing analysis provided new insights into the cellular-level development of tumors. These findings provide more guidance for the diagnosis and treatment of patients with LUSC.

NPLOC4 is a potential target and a poor prognostic signature in lung squamous cell carcinoma

Few prognostic biomarkers exist for lung squamous cell carcinoma (LUSC), which has a poor five-year survival rate. Using bioinformatics, this study evaluated NPLOC4 as a prognostic marker for patients with lung squamous cell carcinoma. Shorter survival periods and tumor growth were linked to high NPLOC4 expression.Disulfiram (DSF) combined with copper (Cu) targets NPLOC4 to achieve antitumor effects in lung squamous cell carcinoma. Thus, we investigated the effects of DSF with Cu in LUSC. Gene-set enrichment analysis identified ubiquitin-mediated proteolysis as the NPLOC4-associated mechanism influencing LUSC prognosis. In SK-MES-1 cell lines, DSF + Cu increased K48-linked ubiquitinated protein expression and apoptosis. This study identified NPLOC4 as a prognostic biomarker and a potential therapeutic target for LUSC.

Perilipin1 Expression as a Prognostic Factor in Patients with Squamous Cell Carcinoma of the Lung.

Perilipin (PLIN) is a major structural protein located on the surface of lipid droplets. PLIN plays an important role in human metabolism and is associated with metabolic diseases, such as obesity, diabetes, hypertension, and endocrine disorders. The dysregulation of lipid metabolism is one of the most prominent metabolic changes observed in cancers. Therefore, the PLIN protein family has recently attracted attention owing to its role in lipid metabolism and cancer. To date, no studies have addressed the association between the prognosis of lung cancer and PLIN1 expression. For the first time, we found that high PLIN1 expression was significantly correlated with worse disease-free survival (DFS) in lung squamous cell carcinoma (SCC). We examined PLIN1 expression by the immunohistochemical analysis of surgical lung SCC specimens obtained from 94 patients. We analyzed the correlation between PLIN1 expression, clinicopathological data, and patient survival, using a chi-squared test, Kaplan-Meier analysis with log-rank tests, and the multivariate Cox proportional hazards regression test. High PLIN1 expression was significantly correlated with lower DFS in the Kaplan-Meier analysis and the multivariate Cox proportional hazards regression model. High PLIN1 expression was significantly correlated with worse prognosis in lung SCC.

Identification of a new risk score model based on hypoxia and EMT-related genes for predicting lung squamous cell carcinoma prognosis.

A complicated analysis of the prognostic characteristics of lung squamous cell carcinoma (LUSC) is needed. The aim of this study was to develop a risk score model to predict immunotherapeutic response and prognosis for patients with LUSC. A hypoxia and epithelial-mesenchymal transition-related risk score model was developed for prediction of LUSC. The correlation between risk score and clinical characteristics was determined. The single sample gene set enrichment analysis algorithm was utilized to determine the abundance of cell infiltration in tumor immune microenvironment in LUSC. The predictive value of risk score model in response to immunotherapy was evaluated. A hypoxia and epithelial-mesenchymal transition-related risk score model was constructed. This risk score model was correlated with the overall survival of LUSC. Patients with low-risk presented a high survival possibility. The high-risk group was involved in ECM receptor interaction, complement and coagulation cascades, intestinal immune network for IgA production. Finally, patients with low-risk score had significant clinical benefit. The risk score model was constructed to predict immunotherapeutic response and prognosis for patients with LUSC. In addition to identifying LUSC patients with poor survival, the results provide more information for the immune immunotherapy and microenvironment for LUSC.