Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

HGH1 and the immune landscape: a novel prognostic marker for immune-desert tumor microenvironment identification and immunotherapy outcome prediction in human cancers.

HGH1 homolog, a protein-coding gene, plays a crucial role in human growth and development. However, its role in human cancer remains unclear. For the first time, this study comprehensively evaluated the potential involvement of HGH1 in cancer prognosis and immunological function. To achieve this, data from various databases, including The Cancer Genome Atlas, Genotype Tissue Expression, Cancer Cell Lineage Encyclopedia, Human Protein Atlas, cBioPortal, Tumor Immune Estimation Resource and Immune Cell Abundance Identifier, were collated, as well as from one large clinical study, three immunotherapy cohorts and in vitro experiments. This study aims to elucidate the role of HGH1 expression in cancer prognosis and immune response. Our findings revealed a significant association between increased HGH1 expression and a worse prognosis across various cancer types. Predominantly, copy number variations were identified as the most common genetic mutations. Additionally, HGH1 was observed to not only regulate cell cycle-related functions to promote cell proliferation but also influence autoimmunity-related functions within both the innate and adaptive immune systems, along with other relevant immune-related signaling pathways. Gene set enrichment analysis and gene set variation analysis were used to substantiate these findings. HGH1 overexpression contributed to an immune-deficient (immune-desert) tumor microenvironment, which was characterized by a significant expression of immune-related features such as immune-related gene and pathway expression and the number of immune-infiltrating cells. Furthermore, the correlation between HGH1 expression and tumor mutational burden in four cancers and microsatellite instability in eight cancers was observed. This suggests that HGH1 has potential as an immunotherapeutic target. Immunotherapy data analysis supports this notion, demonstrating that patients with low HGH1 expression treated with immune checkpoint inhibitors exhibit improved survival rates and a higher likelihood of responding to immunotherapy than patients with high HGH1 expression. Collectively, these findings highlight the significant role of HGH1 in human cancers, illuminating its involvement in tumorigenesis and cancer immunity. Elevated HGH1 expression was identified to be indicative of an immune-desert tumor microenvironment. Consequently, the targeting of HGH1, particularly in combination with immune checkpoint inhibitor therapy, holds promise for enhancing therapeutic outcomes in patients with cancer.

Unraveling the molecular crosstalk and immune landscape between COVID-19 infections and ischemic heart failure comorbidity: New insights into diagnostic biomarkers and therapeutic approaches.

BACKGROUND: Coronavirus disease 2019 (COVID-19), resulting from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), remains a persistent global health concern. Evidence has highlighted a significant association between COVID-19 and ischemic heart failure (IHF), contributing to disease progression and increased mortality. This study identified diagnostic biomarkers for these comorbidities and elucidated disease progression's molecular mechanisms. METHODS: We retrieved differentially expressed gene (DEG) data for COVID-19 and IHF from publicly available microarray and RNA-Seq datasets to investigate the underlying mechanisms and potential pathways associated with the co-occurrence of COVID-19 and IHF. By intersecting the results from the two diseases, we obtained diagnostic biomarkers using SVM-RFE and LASSO algorithms. Animal experiments and immunological analyses were conducted to help understand the association between SARS-CoV-2 and IHF in patients, enabling early diagnosis of disease progression. Finally, we analyzed the regulatory network of critical genes and identified potential drug compounds that could target the genetic links identified in our study. RESULTS: 1974 common DEGs were identified between COVID-19 and IHF, contributing to disease progression and potential cancer risk by participating in immune and cancer-related pathways. In addition, we identified six hub genes (VDAC3, EIF2AK2, CHMP5, FTL, VPS4A, and CHMP4B) associated with the co-morbidity, and their diagnostic potential was confirmed through validation using relevant datasets and a mouse model. Functional enrichment analysis and examination of immune cell infiltration revealed immune dysregulation after disease progression. The comorbid hub genes exhibited outstanding immunomodulatory capacities. We also constructed regulatory networks tightly linked to both disorders, including transcription factors (TFs), miRNAs, and genes at both transcriptional and post-transcriptional levels. Finally, we identified 92 potential drug candidates to enhance the precision of anti-comorbidity treatment strategies. CONCLUSION: Our study reveals a shared pathogenesis between COVID-19 and IHF, demonstrating that their coexistence exacerbates disease severity. By identifying and consolidating hub genes as pivotal diagnostic biomarkers for COVID-19 and IHF comorbidity, we have made significant advancements in understanding the underlying mechanisms of these conditions. Moreover, our study highlights dysregulated immunity and increased cancer risk in the advanced stages of disease progression. These findings offer novel perspectives for diagnosing and treating IHF progression during SARS-CoV-2 infection.

Signal sequence receptor subunit 3: A novel indicator of immunosuppressive tumor microenvironment and clinical benefits from immunotherapy.

BACKGROUND: Signal sequence receptor subunit 3 (SSR3), a translocation-associated protein complex, plays a vital role in various diseases. However, its involvement in human cancers remains unclear. METHODS: We conducted a comprehensive analysis by integrating data from multiple sources, including the Cancer Genome Atlas, Cancer Cell Lineage Encyclopedia, Genotype Tissue Expression, Human Protein Atlas, cBioPortal, TIMER, and ImmuCellAI. Additionally, we incorporated data from a clinical trial, two immunotherapy cohorts, and in vitro experiments to investigate SSR3's impact on cancer prognosis and immune response. RESULTS: Our findings revealed a significant correlation between elevated SSR3 expression and unfavorable prognosis across various cancer types. Amplification is the most common genetic alteration in SSR3. Furthermore, functional enrichment analysis highlighted SSR3's regulatory role in promoting proliferation. In addition, SSR3 also serves as a pivotal mediator bridging the innate and adaptive immune systems and several related signaling pathways. Moreover, the correlation of SSR3 expression with tumor mutation burden in five cancer types, as well as with microsatellite instability in nine cancer types, suggests the potential of SSR3 as a predictive marker for immunotherapy response. To validate this hypothesis, we examined data from patients who underwent immunotherapy treatment. Our analysis revealed that individuals with low SSR3 expression demonstrated higher response rates to immune checkpoint inhibitors and longer overall survival compared to those with high SSR3 expression. CONCLUSIONS: Our study identifies SSR3 as a potential oncogene in humans, implicated in both tumorigenesis and cancer immunity. Elevated SSR3 expression is indicative of an immunosuppressive tumor microenvironment. Therefore, SSR3 holds promise as a potential prognostic biomarker and a target for immunotherapy in cancer treatment.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

The circadian rhythm key gene ARNTL2: a novel prognostic biomarker for immunosuppressive tumor microenvironment identification and immunotherapy outcome prediction in human cancers.

Background: Aryl hydrocarbon receptor nuclear translocator-like 2 (ARNTL2) belongs to the b HLH- PAS domain transcription factor family and is one of the key clock genes that control the circadian rhythm. ARNTL2 plays an important role in human biological functions. However, its role in various tumors, especially in the tumor immune microenvironment (TIME) and immunotherapy, remains unclear. Methods: We integrated data from cancer patients from multiple databases, including the Cancer Genome Atlas, Cancer Cell Lineage Encyclopedia, Genotype Tissue Expression, Human Protein Atlas, cBioPortal, TIMER, and ImmuCellAI, with data from a large clinical study, three immunotherapy cohorts, and in vitro experiments to investigate the involvement of ARNTL2 expression in cancer prognosis and immune response. Results: ARNTL2 displayed abnormal expression within most malignant tumors, and is significantly associated with poorer survival and pathologic staging. Through gene-set enrichment analysis (GSEA) and gene-set variation analysis (GSVA), we found that ARNTL2 not only regulates cell cycle-related functions to promote cell proliferation but also regulates autoimmunity-related functions of the innate and adaptive immune systems, and other immune-related signaling pathways. In addition, ARNTL2 overexpression contributes to an immunosuppressive tumor microenvironment that plays a key role in immunosuppression-related features, such as the expression of immunosuppression-related genes and pathways and the number of immunosuppressive-infiltrating cells, including regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and cancer-associated fibroblasts (CAFs). The group of patients with low ARNTL2 expression who received immune checkpoint inhibitors (ICI) therapy had better response rates and longer survival when compared to those with high ARNTL2 expression. Conclusion: The findings of this study suggest that ARNTL2 is a potential human oncogene that plays an important role in tumorigenesis and cancer immunity. Elevated ARNTL2 expression indicates an immunosuppressive tumor microenvironment. Targeting ARNTL2 in combination with ICI therapy could bring more significant therapeutic benefits to patients with cancer. Our study sheds light on the remarkable potential of ARNTL2 in tumor immunity and provides a novel perspective for anti-tumor strategies.

CT-based decision tree model for predicting EGFR mutation status in synchronous multiple primary lung cancers.

Background: The current study aimed to construct a computed tomography (CT)-based decision tree algorithm (DTA) model to predict the epidermal growth factor receptor (EGFR) mutation status in synchronous multiple primary lung cancers (SMPLCs). Methods: The demographic and CT findings of 85 patients with molecular profiling for surgically resected SMPLCs were reviewed retrospectively. Least absolute shrinkage and selection operator (LASSO) regression was used to select the potential predictors of EGFR mutation, and a CT-DTA model was developed. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to assess the performance of this CT-DTA model. Results: The CT-DTA model was applied to predict the EGFR mutant that had ten binary split, of which eight parameters to accurately categorize the lesions as follows: the presence of bubble-like vacuole sign (19.4% importance in the development of the model), presence of air bronchogram sign (17.4% importance), smoking status (15.7% importance), types of the lesions (14.8% importance), histology (12.6% importance), presence of pleural indentation sign (7.6% importance), gender (6.9% importance), and presence of lobulation sign (5.6% importance). The ROC analysis achieved an area under the curve (AUC) of 0.854. Multivariate logistic regression analysis demonstrated that this CT-DTA model was an independent predictor of EGFR mutation (P<0.001). Conclusions: CT-DTA model is a simple tool to predict the status of EGFR mutation in SMPLC patients and could be considered for treatment decision-making.

Shedding light on macrophage immunotherapy in lung cancer.

The search for therapeutic options for lung cancer continues to advance, with rapid advances in the search for therapies to improve patient prognosis. At present, systemic chemotherapy, immune checkpoint inhibitor therapy, antiangiogenic therapy, and targeted therapy for driver gene positivity are available in the clinic. Common clinical treatments fail to achieve desired outcomes due to immunosuppression of the tumor microenvironment (TME). Tumor immune evasion is mediated by cytokines, chemokines, immune cells, and other cells such as vascular endothelial cells within the tumor immune microenvironment. Tumor-associated macrophages (TAMs) are important immune cells in the TME, inducing tumor angiogenesis, encouraging tumor cell proliferation and migration, and suppressing antitumor immune responses. Thus, TAM targeting becomes the key to lung cancer immunotherapy. This review focuses on macrophage phenotype, polarization mechanism, role in lung cancer, and advances in macrophage centric immunotherapies.

Cuproptosis-related signature predicts prognosis, immunotherapy efficacy, and chemotherapy sensitivity in lung adenocarcinoma.

Background: Cuproptosis is a novel form of programmed cell death that disrupts the tricarboxylic acid (TCA) cycle and mitochondrial function. The mechanism of cuproptosis is quite different from that of common forms of cell death such as apoptosis, pyroptosis, necroptosis, and ferroptosis. However, the potential connection between cuproptosis and tumor immunity, especially in lung adenocarcinoma (LUAD), is poorly understood. Methods: We used machine learning algorithms to develop a cuproptosis-related scoring system. The immunological features of the scoring system were investigated by exploring its association with clinical outcomes, immune checkpoint expression, and prospective immunotherapy response in LUAD patients. The system predicted the sensitivity to chemotherapeutic agents. Unsupervised consensus clustering was performed to precisely identify the different cuproptosis-based molecular subtypes and to explore the underlying tumor immunity. Results: We determined the aberrant expression and prognostic relevance of cuproptosis-related genes (CRGs) in LUAD. There were significant differences in survival, biological function, and immune infiltration among the cuproptosis subtypes. In addition, the constructed cuproptosis scoring system could predict clinical outcomes, tumor microenvironment, and efficacy of targeted drugs and immunotherapy in patients with LUAD. After validating with large-scale data, we propose that combining the cuproptosis score and immune checkpoint blockade (ICB) therapy can significantly enhance the efficacy of immunotherapy and guide targeted drug application in patients with LUAD. Conclusion: The Cuproptosis score is a promising biomarker with high accuracy and specificity for determining LUAD prognosis, molecular subtypes, immune cell infiltration, and treatment options for immunotherapy and targeted therapies for patients with LUAD. It provides novel insights to guide personalized treatment strategies for patients with LUAD.

A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator?

BACKGROUND: There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. PATIENTS AND METHODS: This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. RESULTS: A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). CONCLUSIONS: The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.

Cutting edges and therapeutic opportunities on tumor-associated macrophages in lung cancer.

Lung cancer is a disease with remarkable heterogeneity. A deep understanding of the tumor microenvironment (TME) offers potential therapeutic strategies against this malignant disease. More and more attention has been paid to the roles of macrophages in the TME. This article briefly summarizes the origin of macrophages, the mutual regulation between anti-tumoral immunity and pro-tumoral statuses derived from macrophage polarization, and the therapeutic opportunities targeting alternately activated macrophages (AAM)-type macrophage polarization. Among them, cellular components including T cells, as well as acellular components represented by IL-4 and IL-13 are key regulators driving the polarization of AAM macrophages. Novel treatments targeting macrophage-associated mechanisms are mainly divided into small molecule inhibitors, monoclonal antibodies, and other therapies to re-acclimate AMM macrophages. Finally, we paid special attention to an immunosuppressive subgroup of macrophages with T cell immunoglobulin and mucin domain-3 (TIM-3) expression. Based on cellular interactions with cancer cells, TIM3+ macrophages facilitate the proliferation and progression of cancer cells, yet this process exposes targets blocking the ligand-receptor recognition. To sum up, this is a systematic review on the mechanism of tumor-associated macrophages (TAM) polarization, therapeutic strategies and the biological functions of Tim-3 positive macrophages that aims to provide new insights into the pathogenesis and treatment of lung cancer.

LncRNA in tumorigenesis of non-small-cell lung cancer: From bench to bedside.

Lung cancer has been one of the leading causes of cancer-related death worldwide, and non-small-cell lung cancer (NSCLC) accounts for the majority of lung cancer morbidity, yet the pathogenesis of NSCLC has not been fully elucidated. Recently, long-chain non-coding RNA (lncRNA) has attracted widespread attention. LncRNA is a type of non-coding RNA whose transcript length exceeds 200 nucleotides. After constant research, academics updated their understanding of lncRNA, especially its role in the biological processes of cancer cells, including epigenetic regulation, cell proliferation, and cell differentiation. Notably, examination of lncRNAs could serve as potential hallmarks for clinicopathological features, long-term prognosis, and drug sensitivity. Therefore, it is necessary to explore the functions of lncRNA in NSCLC and innovate potential strategies against NSCLC based on lncRNA-related research. Herein, we reviewed the functions of lncRNA in the occurrence, diagnosis, treatment, and prognosis of NSCLC, which not only help promote a comprehensive view of lncRNA in NSCLC, but also shed light on the potential of lncRNA-based diagnosis and treatment of NSCLC.

Cuproptosis predicts the risk and clinical outcomes of lung adenocarcinoma.

Copper is an essential microelement for the body and a necessary coregulator for enzymatic reactions, yet an unbalanced copper level promotes reactive oxidation and cytotoxicity, which ultimately induces cell death. Several small molecules targeting copper-induced cell death have been investigated, yet few showed promising therapeutic effects in clinical trials. In March 2022, Science first introduced the concept and mechanisms of cuproptosis, suggesting that copper-induced cell death targets the tricarboxylic acid (TCA) cycle via protein lipoylation. Does this novel form of cell death take part in tumorigenesis or tumor progression? Is cuproptosis related to clinical outcomes of diseases? Is there a cuproptosis-related panel for clinical practice in cancer treatment? Herein, based on 942 samples of lung adenocarcinoma (LUAD), we analyzed on gene set level the existence and predictive value of cuproptosis in disease diagnosis and treatment. We screened out and identified the "cupLA" panel which indicates the risk of LUAD occurrence, clinicopathological features of LUAD patients, and could guide clinicians to refine LUAD subtypes and make treatment choices.

Inducible Co-Stimulator ICOS Expression Correlates with Immune Cell Infiltration and Can Predict Prognosis in Lung Adenocarcinoma.

Background: Inducible co-stimulator (ICOS) is a cell-enhanced co-stimulatory receptor that has shown great potential in the regulation of innate and adaptive immunity. However, the role of ICOS in lung adenocarcinoma (LUAD) remains unclear. Methods: We used data from the Cancer Genome Atlas(TCGA) database to identify the expression and prognostic role of ICOS in LUAD. The results were validated using Gene Expression Omnibus(GEO) and Kaplan-Meier plotter databases. A model with predictive performance for overall survival of LUAD patients was constructed using fitted ICOS expression and other clinical parameters. We explored the biological function of ICOS. Subsequently, we further analysed and validated the effect of ICOS expression on tumour immune microenvironment (TIME) and survival. Finally, the CellMiner database was used to determine the relationship between ICOS expression and drug sensitivity. Results: ICOS expression is significantly associated with poor prognosis in multiple cancers, especially LUAD, and is a good predictor of overall survival in LUAD patients. The biological function is to promote autoimmunity and inhibit cell proliferation. ICOS-related survival prediction model developed to more accurately predict 1-, 3- and 5-year survival probabilities for LUAD patients. In addition, we can use the expression of ICOS to effectively assess patient malignancy, prognosis, TIME status and clinical combination of drugs. Conclusion: Our results suggest that ICOS is correlated with prognosis and immune infiltrating levels in LUAD. Higher ICOS expression predicts better TIME. This study provides a novel strategy for the development of immunotherapeutic and prognostic markers in LUAD.