Multi‐omic data integration and exploiting metabolic models using systems biology approach increase precision in subtyping and early diagnosis of cancer

Lung cancer is the leading cause of the incidence and mortality of malignant tumors in our country, seriously endangering people's lives and health. The treatment of lung cancer has made great progress in the past 10 years, and the 5-year survival rate of lung cancer in China has also increased from 16.1% to 19.7%, but about 75% of patients are still in advanced stages of lung cancer at the time of diagnosis, missing the best time for radical surgery. Early diagnosis can significantly improve the prognosis and survival of lung cancer patients. From the 5-year survival rate of lung cancer patients, it can be seen that the 5-year survival rate of stage Ⅰ patients was 77%-92%, while that of stage ⅢA-ⅣA patients was only 10%-36%, and there was a significant difference in the 5-year survival rate. Studies have shown that early-diagnosed and completely resected lung adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) have 5-year disease-specific survival rates of 100% and 100%, respectively. Early diagnosis is the key to improving the prognosis of lung cancer. In order to further improve the level of early lung cancer diagnosis in China, especially the standardization in the diagnosis and evaluation of pulmonary nodules and early lung cancer, experts from the Lung Cancer Group of Chinese Thoracic Society formulated the "Chinese Expert Consensus on Diagnosis of Early Lung Cancer (2023 Edition)", on the basis of the actual situation in the field of diagnosis and treatment, with reference to the latest research data and relevant guidelines at home and abroad. Consensus on the application of artificial intelligence, big data and robotics, the Internet of Things and multidisciplinary cooperation in the diagnosis of early lung cancer, the management of pulmonary nodules and follow-up strategies for suspected early lung cancer, etc., were respectively recommended to provide references for clinicians in the diagnosis of early lung cancer, in order to further promote the early diagnosis of lung cancer in China.

Lung cancer is the leading cause of death for neoplasm. Lung cancer mortality is frequently associated with late diagnosis, therefore an early diagnosis is a key factor to significantly improve overall survival in high risk populations of asymptomatic patients. Conventional cancer screenings (low-dose computed tomography or chest x-ray) today offer early detection but are invasive and expensive. Previously these studies evaluated the solid and topographic cancer structure and morphology. Today the concept of tumor has been remodelled, being defined as a disease that has its own genetic, biological and metabolic identity; it is on this new awareness that we should base new screening methods. Recent research has shown great reliability of new tests such as exhaled breath analysis, serum biomarkers and urine analysis in early diagnosis of lung cancer. Analysis of new biomarkers associated with the high specificity of these new screening methods, which are non-invasive, safe, inexpensive and simple to perform, could allow a non-invasive approach to determine a big change in the early diagnosis of cancer and its survival rate. Furthermore, these new techniques put the patient at the core of a non-invasive diagnostic process and ensure a better quality of life during medical diagnosis. In this article, we want to analyze the possible benefits of these new and promising methods, suggesting a possible combination between them to ensure, as soon as possible, an early and effective diagnosis of lung cancer with a special focus on the patient, in a new era of personalized medicine.

BackgroundLung cancer is typically diagnosed at a late stage. Early presentation and detection of lung cancer symptoms is critical to improving survival but can be clinically complicated and as yet a robust screening method for diagnosis is not available in routine practice. Accordingly, the barriers to help-seeking behaviour and diagnosis need to be considered. This review aimed to document the barriers to early presentation and diagnosis of lung cancer, based on patient and carer perspectives.MethodsA systematic review of databases was performed for original, English language articles discussing qualitative research on patient perceived barriers to early presentation and diagnosis of lung cancer. Three major databases were searched: Scopus, PubMed and EBSCOhost. References cited in the selected studies were searched for further relevant articles.ResultsFourteen studies met inclusion criteria for review. Barriers were grouped into three categories: healthcare provider and system factors, patient factors and disease factors.ConclusionsStudies showed that the most frequently reported barriers to early presentation and diagnosis of lung cancer reported by patients and carers related to poor relationships between GPs and patients, a lack of access to services and care for patients, and a lack of awareness of lung cancer symptoms and treatment. Addressing these barriers offers opportunities by which rates of early diagnosis of lung cancer may be improved.

Biotechnology is currently evolving through the era of big data, thanks to advances in the high-throughput technologies for rapid and inexpensive genome sequencing and other genome-wide studies [1]. With the daunting amount of data, it has been possible to put them together into a coherently organized biological network that provides counterintuitive insights on biological systems [2]. Among such biological networks, a genome-scale metabolic network model is expected to play an increasingly important role in the biopharmaceutical industry [3]. Before enumerating their specific strengths, it is important to note that principles underlying genome-scale metabolic network models are consistent with the holistic perspective of systems biology, the aim of which is to unveil hidden factors causing diseases and to find relevant treatment strategies [4]. Despite the importance of metabolism in a biological system, studies on diseases in relation to metabolism were far fewer in number than those performed on signaling and transcriptional regulatory networks [5]. However, metabolism, highly linked with observable phenotypes, is a biological network that is more comprehensively characterized when compared with the other two types of networks [6]. Metabolism is, therefore, amenable to large-scale mathematical modeling and simulation. It is with this motivation that the genome-scale metabolic simulation deserves more attention in drug discovery campaigns and optimization of a host strain for the production of biopharmaceuticals. Reconstruction and application of genome-scale metabolic network models have been forged as a major research strategy of systems biology. Over the last decade, genome-scale metabolic models have been built for almost all biologically important organisms across the domains of archea, bacteria and eukaryotes [3]. They range from simple micro organisms such as Escherichia coli [7] and Saccharomyces cerevisiae [8] to higher organisms including Chinese hamster ovary (CHO) cells [9,10] and a generic human cell [11,12]. It should be noted that all these organisms that have been subjected to metabolic modeling are important cellular hosts for biopharmaceutical production or medically meaningful organisms that need to be cured (e.g., specific cancer cells) or destroyed (e.g., pathogens). A recent notable development of importance in the genomescale metabolic modeling would be the newly updated human metabolic network Recon 2 [12]. Recon 2 is a result of efforts from a group of researchers, going over a vast amount of literature and biochemical data and reconciling conflicting information. Scope of the hitherto reconstructed genome-scale metabolic models manifest high expectations for their potential contributions to biopharmaceutical industry. Genome-scale metabolic network models are not just a simple pileup of biochemical reactions, but allow mathematical simulation under precisely defined conditions of constraints [13]. Once the experimentally Applications of genome-scale metabolic network models in the biopharmaceutical industry

The objective of this research was to document the barriers to early presentation and diagnosis of lung cancer within primary healthcare, identified by Māori whānau (families) and primary healthcare providers in the Midland region of Aotearoa New Zealand. This project used a kaupapa Māori approach. Nine community hui (focus groups) and nine primary healthcare provider hui were carried out in five rural localities in the Midland region. Each community hui included cancer patients, whānau, and other community members. Each healthcare provider hui comprised staff members at the local primary healthcare centre, including General Practitioners and nurses. Hui data were thematically analysed. Barriers and enablers to early diagnosis of lung cancer were categorised into three key themes: GP relationship and position in the community, health literacy and pathways to diagnosis. This study demonstrates that culturally responsive, patient-centred healthcare, and positive GP-patient relationships are significant factors for Māori patients and whānau serving as barriers and enablers to early diagnosis of lung cancer.

肺癌是世界范围内发病率和死亡率较高的恶性肿瘤之一，严重威胁着国民的生命安全与健康。肺癌的早期诊断是肺癌预防和治疗过程中的关键环节，对肺癌进行早期诊断有利于提高患者的生存率。外泌体(exosomes)与肿瘤的侵袭与转移过程密切相关，在肺癌的发生发展过程中，外泌体发挥着重要的调控作用。近年来，以外泌体为载体的生物标记物成为肺癌强有力的诊断工具。外泌体是一种由细胞分泌的由膜包裹的大小均一、直径约为30 nm-200 nm的脂质双分子层结构小囊泡。外泌体的内容物包含不同类型的核酸和蛋白质，这些核酸和蛋白质来源于其亲本细胞(包括亲本癌细胞)，具有广泛的生理功能，包括参与免疫调节、细胞间联络等。外泌体中的生物大分子物质，如单链RNA、长非编码RNA、微小RNA(microRNA, miRNA)、蛋白质以及脂类，可以为肺癌的早期临床诊断提供有价值的信息。因此，本文就外泌体的来源、结构特点、提取方法、生物学特性和在肺癌早期诊断中的作用研究进展做简要阐述。

Early diagnosis of lung cancer is critically important to reduce disease severity and improve overall survival. Newer, minimally invasive biopsy procedures often fail to provide adequate specimens for accurate tumor subtyping or staging which is necessary to inform appropriate use of molecular targeted therapies and immune checkpoint inhibitors. Thus newer approaches to diagnosis and staging in early lung cancer are needed. This exploratory pilot study obtained peripheral blood samples from 139 individuals with clinically evident pulmonary nodules (benign and malignant), as well as ten healthy persons. They were divided into three cohorts: original cohort (n = 99), control cohort (n = 10), and validation cohort (n = 40). Average RNAseq sequencing of leukocytes in these samples were conducted. Subsequently, data was integrated into artificial intelligence (AI)-based computational approach with system-wide gene expression technology to develop a rapid, effective, non-invasive immune index for early diagnosis of lung cancer. An immune-related index system, IM-Index, was defined and validated for the diagnostic application. IM-Index was applied to assess the malignancies of pulmonary nodules of 109 participants (original + control cohorts) with high accuracy (AUC: 0.822 [95% CI: 0.75–0.91, p < 0.001]), and to differentiate between phases of cancer immunoediting concept (odds ratio: 1.17 [95% CI: 1.1–1.25, p < 0.001]). The predictive ability of IM-Index was validated in a validation cohort with a AUC: 0.883 (95% CI: 0.73–1.00, p < 0.001). The difference between molecular mechanisms of adenocarcinoma and squamous carcinoma histology was also determined via the IM-Index (OR: 1.2 [95% CI 1.14–1.35, p = 0.019]). In addition, a structural metabolic behavior pattern and signaling property in host immunity were found (bonferroni correction, p = 1.32e − 16). Taken together our findings indicate that this AI-based approach may be used for “Super Early” cancer diagnosis and amend the current immunotherpay for lung cancer.

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a “VA-ELCAP” version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.

e21020 Background: Early diagnosis of lung cancer is critically important to reduce disease severity and improve overall survival. For patients with suspicious lung lesions, a definitive diagnosis of malignant cancer currently requires surgical biopsy. Newer, minimally invasive biopsy procedures often fail to provide adequate specimens for accurate tumor subtyping or staging which is necessary to inform appropriate use of molecular targeted therapies and immune checkpoint inhibitors. Thus newer approaches to diagnosis and staging in early lung cancer are needed. Methods: In order to address this need, peripheral blood samples from 99 individuals with clinically evident pulmonary nodules (benign and malignant), as well as ten healthy persons, were obtained. Average RNA sequencing (RNAseq) data from these samples were integrated into an artificial intelligence (AI) model in order to perform a pathway flux analysis at the system level. The AI model utilized the biological knowledge derived from the literature and publicly available databases and possessed four layers with following relationship: (1) gene→ (2) RNA→ (3) protein/complex/compound → (4) pathway. The last layer (pathway) was connected back to the first layer (gene) via diverse feedback mechanisms. Based on these results, an immune index system, IM-Index, was defined and tested for early lung cancer diagnosis and staging. Results: The leukocyte-based IM-Index correctly identified the malignancy status of lung cancer patients with high accuracy (specificity 93.5%, sensitivity 70.5%), and was able to differentiate between the three accepted phases of the cancer immunoediting concept (i.e., elimination, equilibrium, escape) (odds ratio: 1.17 [95% CI: 1.1-1.25, p&lt; 0.001). Further, the IM-Index clearly differentiated between adenocarcinoma and squamous cell carcinoma (OR: 1.2 [95% CI 1.14-1.35, p = 0.019]). In addition, tumor- and host-specific signaling pathways and metabolic profiles were identified that fully distinguished between tumor and host immunity (p &lt; 0.05). Conclusions: Taken together, these findings suggest that our rapid, non-invasive peripheral blood leukocyte-based AI approach may be useful for early definitive cancer diagnosis and staging.

Introduction:Lung cancer (LC) is the most common cancer in the world.Well known causes are long term smoking, environmental influences and genetic variations. LC is divided into two main types based on their histological phenotypes; small cell lung cancer (SCLC), and non-small cell lung cancer (NSCLC). The high specificity of these new screening methods, which are non-invasive, safe, inexpensive and simple to perform, is important in the early diagnosis and prognosis of cancer. MicroRNAs are significant biomarkers on the diagnosis metastasis and targeted therapies of NSCLC. In our study, we aimed to investigate the potential of using microRNAs as a biomarker in the early diagnosis of lung cancer.Patients and methods:Twenty patients diagnosed with lung cancer and twenty healthy individuals of the same age and gender were selected as the control group. Sixteen microRNAs were studied from blood samples.Results:Sixteen miRNAs (Let -7c, Let-7g, miR-1, miR-21, miR-29a, miR-31, miR-34a, miR 103a, miR-141, miR-155, miR-193b, miR-200b, miR-205, miR-340, miR-486, miR-708) were selected for tests and MiR 181 and miR 192 were used as the endogenous control group in line with their binding potentials and gene expression levels. The most specific and sensitive miRNAs were mirR-29a, miR-103a, and miR486 according to endogen controls in patients and healthy volunteer subjects.Discussion:A meta-analysis study showed that circulating miRNAs could be promising biomarkers for early diagnosis of lung cancer. Overall, 17 studies were included evaluating 35 miRNA markers and 19 miRNA panels in serum or plasma.Conclusion:In conclusion, there is a need for further validation studies for the use of three miRNAs as a biomarker in the early diagnosis and prognosis of lung cancer. (www.actabiomedica.it)

Early diagnosis of cancer – the role of general practice

Lung cancer, the leading cause of cancer-related deaths globally, presents significant challenges in early detection and diagnosis. The effective analysis of pulmonary medical imaging, particularly computed tomography (CT) scans, is critical in this endeavor. Traditional diagnostic methods, which are manual and time-intensive, underscore the need for innovative, efficient, and accurate detection approaches. To address this need, we introduce the Adaptive Range Slice Grouping Network (ARSGNet), a novel deep learning framework that enhances early lung cancer diagnosis through advanced segmentation and classification techniques in CT imaging. ARSGNet synergistically integrates the strengths of EfficientNet and Transformer architectures, leveraging their superior feature extraction and contextual processing capabilities. This hybrid model proficiently handles the complexities of 3D CT images, ensuring precise and reliable lung nodule detection. The algorithm processes CT scans using short slice grouping (SSG) and long slice grouping (LSG) techniques to extract critical features from each slice, culminating in the generation of nodule probabilities and the identification of potential nodular regions. Incorporating shapley additive explanations (SHAP) analysis further enhances model interpretability by highlighting the contributory features. Our extensive experimentation demonstrated a significant improvement in diagnostic accuracy, with training accuracy increasing from 0.9126 to 0.9817. This advancement not only reflects the model’s efficient learning curve but also its high proficiency in accurately classifying a majority of training samples. Given its high accuracy, interpretability, and consistent reduction in training loss, ARSGNet holds substantial potential as a groundbreaking tool for early lung cancer detection and diagnosis.

肺癌的早期诊断有利于提高患者的生存率。应用影像学方法对肺癌高风险人群进行筛查，可以起到早发现、早诊断的作用。越来越多的研究显示，液体活检（liquid biopsy）可以对该方法进行替代和补充。检测肺癌患者外周血中的循环肿瘤细胞（circulating tumor cells, CTCs）、循环肿瘤DNA（circulating tumor DNA, ctDNA）、微小核糖核酸（microRNA, miRNA）、外泌体（exosomes）、肿瘤血小板（tumor educated platelets, TEPs）可以用于肺癌的早期诊断，并且可能为影像学检查阴性的高风险人群提供相应的诊疗建议。全文就以上标志物的检测手段、在肺癌早期诊断中的价值以及存在优势与局限性进行综述，以期促进液体活检在肺癌早期诊断、与其他筛查手段相结合方面的应用。

Lung cancer is a malignant tumor with rapid progression and high fatality rate. According to histological morphology and cell behaviours of cancerous tissues, lung cancer can be classified into a variety of subtypes. Since different cancer subtype corresponds to distinct therapies, the early and accurate diagnosis is critical for following treatments and prognostic managements. In clinical practice, the pathological examination is regarded as the gold standard for cancer subtypes diagnosis, while the disadvantage of invasiveness limits its extensive use, leading the non-invasive and fast-imaging computed tomography (CT) test a more commonly used modality in early cancer diagnosis. However, the diagnostic results of CT test are less accurate due to the relatively low image resolution and the atypical manifestations of cancer subtypes. In this work, we propose a novel automatic classification model to offer the assistance in accurately diagnosing the lung cancer subtypes on CT images. Inspired by the findings of cross-modality associations between CT images and their corresponding pathological images, our proposed model is developed to incorporate general histopathological information into CT imagery-based lung cancer subtypes diagnostic by omitting the invasive tissue sample collection or biopsy, and thereby augmenting the diagnostic accuracy. Experimental results on both internal evaluation datasets and external evaluation datasets demonstrate that our proposed model outputs more accurate lung cancer subtypes diagnostic predictions compared to existing CT-based state-of-the-art (SOTA) classification models, by achieving significant improvements in both accuracy (ACC) and area under the receiver operating characteristic curve (AUC).Clinical Relevance- This work provides a method for automatically classifying the lung cancer subtypes on CT images.