Biomarkers For Lung Cancer Prognosis Research Articles

Deciphering the role of cuproptosis-related lncRNAs in shaping the lung cancer immune microenvironment: A comprehensive prognostic model.

Cuproptosis plays an important role in cancer, but its role in lung cancer remains unknown. Transcriptional profiles, clinical details and mutation data were acquired from the Cancer Genome Atlas database through a variety of methods. The analysis of this publicly available data was comprehensively performed using R software along with its relevant packages, ensuring a thorough examination of the information. In this study, we conducted a detailed analysis of cuproptosis-related genes and lncRNA co-expression, identifying 129 relevant lncRNAs and establishing a prognostic model with four key lncRNAs (LINC00996, RPARP-AS1, SND1-IT1, TMPO-AS1). Utilizing data from TCGA and GEO databases, the model effectively categorized patients into high- and low-risk groups, showing significant survival differences. Correlation analysis highlighted specific relationships between individual lncRNAs and cuproptosis genes. Our survival analysis indicated a higher survival rate in the low-risk group across various cohorts. Additionally, the model's predictive accuracy was confirmed through independent prognostic analysis and ROC curve evaluations. Functional enrichment analysis revealed distinct biological pathways and immune functions between risk groups. Tumour mutation load analysis differentiated high- and low-risk groups by their mutation profiles. Drug sensitivity analysis and immune infiltration studies using the CIBERSORT algorithm further elucidated the potential treatment responses in different risk groups. This comprehensive evaluation underscores the significance of lncRNAs in cuproptosis and their potential as biomarkers for lung cancer prognosis and immune microenvironment.

The role of leptin, resistin, and irisin levels as prognostic indicators of lung cancer in Iraqi patients

ABSTRACT Lung cancer is the most lethal malignancy and is often associated with a poor prognosis because of a lack of prognostic markers. Thus, this study aimed to determine whether irisin, resistin, and leptin could be useful biomarkers for lung cancer prognosis. The study is designed on 100 lung cancer patients, (66) patients with non-small cell lung cancer (NSCLC) and (34) patients with small cell lung cancer (SCLC). For the purpose of comparison, (66) samples as control group. Using the ELISA technique to estimate leptin, resistin, and irisin levels in patients and controls. The results indicate a significant increase in mean leptin in NSCLC and SCLC groups (10.71 ± 0.30 and 10.13 ± 0.51) ng/ml, respectively, in contrast to the control (8.26 ± 0.47) ng/ml. The mean of irisin and resistin had been significantly increased in SCLC group (5.86 ± 0.13 pg/ml and 7.25 ± 0.38 ng/ml, respectively) and NSCLC group (5.08 ± 0.09 pg/ml and 6.35 ± 0.13 ng/ml, respectively) in contrast to the control (4.13 ± 0.09 pg/ml and 3.96 ± 0.17 ng/ml, respectively). The higher levels of leptin in NSCLC patients could serve as a prognostic marker for NSCLC. The variations in Resistin and Irisin levels across different stages of lung cancer suggest that they might be useful in predicting the prognosis of lung cancer.

Prognostic Value of the Glasgow Prognostic Score in Non-Small Cell Lung Cancer Patients Receiving Immunotherapy: A Meta-Analysis

Background The Glasgow Prognostic Score (GPS) has proven to be a good biomarker for lung cancer prognosis. However, its usefulness in lung cancer patients receiving checkpoint inhibitor immunotherapy remains controversial. Therefore, we performed a meta-analysis to explore the prognostic value of the GPS in non-small cell lung cancer patients receiving immunotherapy. Methods PubMed, Web of Science, Scopus, and Embase were systematically searched for relevant studies up to May 31, 2023, and hazard ratios (HRs) with 95% confidence intervals (95% CIs) were merged to investigate the prognostic value of the GPS for overall survival (OS) and progression-free survival (PFS). Results Seven studies comprising 833 patients were included in the primary analysis, and the pooled results indicated that a higher baseline GPS was associated with poorer OS and PFS in non-small cell lung cancer patients treated with immune checkpoint inhibitors (ICIs) (OS: HR = 1.95, 95% CI: 1.47-2.58, p < 0.01; PFS: HR = 1.63, 95% CI: 1.26-2.11, p < 0.01). These findings were robust after subgroup and sensitivity analyses. Conclusions The GPS can serve as a biomarker in non-small cell lung cancer patients receiving immunotherapy with significant prognostic value; however, these findings require more prospective evidence for validation.

Abstract 3839: RASSF1C and tumor microenvironment

Abstract Introduction: Tumor microenvironment (TME) plays a vital role in tumor invasion and metastasis and provides a rich environment for identifying novel therapeutic targets. The TME landscape consists of extracellular matrix (ECM) and stromal cells. ECM is a major component of TME that mediates interaction between cancer cells and stromal cells to promote invasion and metastasis. We have shown in published work that RASSF1C promotes cancer stem cell development, migration, and drug resistance, in part, by promoting EMT through a mechanism that involves up-regulation of the PIWIL1-piRNA gene axis. Interestingly, one of the target genes identified by our microarray study to be upregulated by RASSF1C is P4HA2. In cancer, P4H2A is vital for collagen posttranslational modification and folding. This leads to formation of a stiff ECM and induction of cancer stem cell marker gene expression, resulting in metastatic dissemination. High expression of P4HA2 is associated with significantly lower survival of lung cancer patients. Thus, RASSF1C could promote tumor cell ECM remodeling to induce lung cancer cell stemness, invasion and metastasis by up-regulating a novel PIWIL1-P4HA2 gene axis expression. Methods: Analysis of a microarray study using H1299 cells over-expressing RASSF1C or control vector backbone identified P4HA2 as a RASSF1C gene target. RT-PCR, immunoblots, and immunofluorescence techniques were used to confirm P4HA2 gene expression using specific primers and antibodies in cells over-expressing RASSF1C or PIWIL1 along with control cells. Kaplan-Meier analysis was performed to determine the relationship of P4HA2 expression to lung adenocarcinoma patient survival using data from The Cancer Genome Atlas (TCGA). Results: Analysis of microarray data from H1299 cells over-expressing RASSF1C or control vector backbone shows that RASSF1C up-regulates P4HA2 gene expression 2-fold. The up-regulation of P4HA2 expression by RASSF1C was confirmed by RT-PCR, immunoblots, and immunofluorescence using P4HA2 gene specific primers and antibodies. Further, H1299 cells over-expressing PIWIL1 show increased P4HA2 gene expression on immunoblots. Kaplan-Meier analysis shows that high P4HA2 expression in lung adenocarcinoma patients negatively correlates with patient survival. Thus, our findings suggest the hypothesis that RASSF1C may promote lung cancer cell ECM remodeling to induce lung cancer cell stemness, invasion and metastasis, in part, by up-regulating a novel pathway involving a PIWIL1-P4HA2 gene axis. Conclusions: Investigating the role of a RASSF1C-PIWIL1-P4HA2 gene axis in ECM remodeling in lung cancer cells will provide important mechanistic information that could, in turn, lead to identification of useful biomarkers for lung cancer prognosis and targets for therapy. Citation Format: Yousef G. Amaar, Mark E. Reeves. RASSF1C and tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3839.

A study of diagnostic utility of annexin A1 in bronchoalveolar lavage fluid of patients with bronchogenic carcinoma

Background Annexin A1 is implicated in multiple types of cancer, but few researches have studied its job in lung cancer. Aim The aim of the study was to enumerate annexin A1 levels in bronchoalveolar lavage fluid (BALF) and sera of lung cancer patients and patients with benign lung diseases and to evaluate its role as a diagnostic aid for lung cancer. Patients and methods This prospective analytic controlled case study was performed in Chest Department, Tanta University Hospitals, on 40 patients. The patients were split into two groups: group 1 included 20 patients with bronchogenic carcinoma, group 2 included 20 patients having nonmalignant lung diseases of known etiologies. Clinical assessment, demographic data, and routine investigations were done to all patients. Annexin A1 expression in BALF and serum was examined using enzyme-linked immunosorbent assay. Results Annexin A1 levels were higher in BALF and sera of lung carcinoma patients than those of the control group. Positive correlation was present between annexin A1 level in BALF and serum and advanced clinical stages of lung cancer and between BALF annexin A1 level and its level in serum. Conclusion High annexin A1 level in BALF was related to advanced clinical stage of lung carcinoma and in a positive correlation with serum annexin A1. High sensitivity and specificity of BALF annexin A1 for detection of lung carcinoma showed that it deemed to be a molecular biomarker for lung cancer prognosis and diagnosis.

18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer.

We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non–small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56–2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction (P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66–0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67–0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.

PPARGC1A is upregulated and facilitates lung cancer metastasis

Lung cancer remains a leading cause of cancer-related mortality, with metastatic progression remaining the single largest cause of lung cancer mortality. Hence it is imperative to determine reliable biomarkers for lung cancer prognosis. We performed quantitative real-time PCR (qRT-PCR) analysis to explore epithelial-mesenchymal transition (EMT) inducers that regulate EMT process in three patients with advanced lung cancer disease. Peroxisome proliferator-activated receptor gamma (PPARGC1A) was uniformly the topmost overexpressed gene in all three human non-small cell lung cancer (NSCLC) patient samples. Further evaluation in human normal lung and metastatic lung cancer cell lines revealed that the expression of PPARGC1A was upregulated in metastatic lung cancer cell lines. Metagenomic analysis revealed direct correlation among PPARGC1A, zinc-finger transcription factor snail homolog 1 (SNAI1), and metastatic lung disease. Upregulation of PPARGC1A transcript expression was independent of a differential upregulation of the upstream AMP-dependent protein kinase (AMPK) activation or steady state expression of the silent mating type information regulation 2 homolog 1 (SIRT1). Xenograft tail vein colonization assays proved that the high expression of PPARGC1A was a prerequisite for metastatic progression of lung cancer to brain. Our results indicate that PPARGC1A might be a potential biomarker for lung cancer prognosis.

Transcriptome Sequencing Reveals Key Pathways and Genes Associated with Cisplatin Resistance in Lung Adenocarcinoma A549 Cells.

Acquired resistance to cisplatin-based chemotherapy frequently occurs in patients with non-small cell lung cancer, and the underlying molecular mechanisms are not well understood. The aim of this study was to investigate whether a distinct gene expression pattern is associated with acquired resistance to cisplatin in human lung adenocarcinoma. Whole-transcriptome sequencing was performed to compare the genome-wide gene expression patterns of the human lung adenocarcinoma A549 cisplatin-resistant cell line A549/DDP with those of its progenitor cell line A549. A total of 1214 differentially expressed genes (DEGs) were identified, 656 of which were upregulated and 558 were downregulated. Functional annotation of the DEGs in the Kyoto Encyclopedia of Genes and Genomes database revealed that most of the identified genes were enriched in the PI3K/AKT, mitogen-activated protein kinase, actin cytoskeleton regulation, and focal adhesion pathways in A549/DDP cells. These results support previous studies demonstrating that the pathways regulating cell proliferation and invasion confer resistance to chemotherapy. Furthermore, the results proved that cell adhesion and cytoskeleton regulation is associated with cisplatin resistance in human lung cancer. Our study provides new promising biomarkers for lung cancer prognosis and potential therapeutic targets for lung cancer treatment.

MicroRNA-490 regulates lung cancer metastasis by targeting poly r(C)-binding protein 1

Lung cancer remains a leading cause of cancer-related mortality, with metastatic progression remaining the single largest cause of lung cancer mortality. Hence, it is imperative to determine reliable biomarkers of lung cancer prognosis. MicroRNA-490-3p has been previously reported to be a positive prognostic biomarker for hepatocellular cancer. However, its role in human lung cancer has not yet been elucidated. Here, we report that hsa-miR-490-3p expression is significantly higher in human lung cancer tissue specimens and cell line. Gain- and loss-of-function studies of hsa-miR-490-3p showed that it regulates cell proliferation and is required for induction of in vitro migration and invasion-the latter being a hallmark of epithelial to mesenchymal transition. In situ analysis revealed that hsa-miR-490-3p targets poly r(C)-binding protein 1 (PCBP1), which has been previously shown to be a negative regulator of lung cancer metastasis. Reporter assays confirmed PCBP1 as a bona fide target of miR-490-3p, and metagenomic analysis revealed an inverse relation between expression of miR-490-3p and PCBP1 in metastatic lung cancer patients. In fact, PCBP1 expression, as detected by immunohistochemistry, was undetectable in advanced stages of lung cancer patients' brain and lymph node tissues. Xenograft tail vein colonization assays proved that high expression of miR-490-3p is a prerequisite for metastatic progression of lung cancer. Our results suggest that hsa-miR-490-3p might be a potential biomarker for lung cancer prognosis. In addition, we can also conclude that the lung cancer cells have evolved refractory mechanisms to downregulate the expression of the metastatic inhibitor, PCBP1.

Statistical completion of a partially identified graph with applications for the estimation of gene regulatory networks.

We study the estimation of a Gaussian graphical model whose dependent structures are partially identified. In a Gaussian graphical model, an off-diagonal zero entry in the concentration matrix (the inverse covariance matrix) implies the conditional independence of two corresponding variables, given all other variables. A number of methods have been proposed to estimate a sparse large-scale Gaussian graphical model or, equivalently, a sparse large-scale concentration matrix. In practice, the graph structure to be estimated is often partially identified by other sources or a pre-screening. In this paper, we propose a simple modification of existing methods to take into account this information in the estimation. We show that the partially identified dependent structure reduces the error in estimating the dependent structure. We apply the proposed method to estimating the gene regulatory network from lung cancer data, where protein-protein interactions are partially identified from the human protein reference database. The application shows that proposed method identified many important cancer genes as hub genes in the constructed lung cancer network. In addition, we validated the prognostic importance of a newly identified cancer gene, PTPN13, in four independent lung cancer datasets. The results indicate that the proposed method could facilitate studying underlying lung cancer mechanisms and identifying reliable biomarkers for lung cancer prognosis.

Abstract A4: Integrative genomic analysis of lung cancer: Focus on health disparities

Abstract Racial differences in the incidence and mortality of lung cancer are well documented. Many factors are known to influence the risk and outcome of this most deadly disease, including exposure, behavior, access to health care, as well as inherent susceptibility. There is a paucity of data regarding the existence, if any, of biological differences among tumors that reflect inherent susceptibilities to known etiological factors and may contribute to some of the disparities observed among African Americans and Caucasians. Our laboratory employs a multipronged strategy to study the interaction of genetic, molecular, and etiological factors leading to lung cancer risk and survival, that encompasses mRNA and microRNA (miRNA) expression, DNA methylation, metabolomics, as well as genetic susceptibility in the form of Single Nucleotide Polymorphisms (SNPs). In this study, a cohort of sixty paired cancerous/non-cancerous lung tissues excised from stage I lung cancer patients (16 African Americans and 48 Caucasians) recruited through our longstanding Case-Control Study at University of Maryland, Baltimore, are being characterized by miRNA quantitation (Nanostring nCounter Human miRNA Expression Assay), mRNA expression profiling (Illumina HumanRef-8 v3 Expression Beadchip arrays), and methylation profiling (Illumina Infinium Human Methylation27 Beadchip arrays). When gene expression profiles on cancerous tissues from African Americans and Caucasians were compared, three genes were differentially expressed (FDR&amp;lt;10%), namely CRYBB2, PPIL3, and COMMD5. A similar comparison on non-cancerous tissues yielded 12 genes (FDR&amp;lt;10%), which included CRYBB2 and PPIL3. CRYBB2, Crystalline Beta B2, has previously been associated with prostate cancer in African Americans, and is upregulated in this sample cohort. We are currently analyzing and integrating these different data types with the purpose of finding biomarkers for lung cancer prognosis, associating DNA methylation, mRNA and miRNA abundance with clinical features of lung cancer (including race, gender, histology, and smoking), and correlating methylation, mRNA and miRNA patterns. Our goal is to delineate the most important transcriptional networks operational in lung cancer and specifically focus on those that may illuminate racial disparities associated with this disease. Citation Information: Cancer Epidemiol Biomarkers Prev 2011;20(10 Suppl):A4.