A pan-cancer long non-coding RNA (lncRNA) signature defines oncogene activity in blood serum of cancer patients

Tumor-derived material in blood samples is informative concerning molecular alterations in respective tumor tissues. Thus, biomarkers detected by liquid biopsy can guide clinicians in designing personalized therapies and moreover may serve as a proxy for treatment response and success. However, robust biomarkers for the detection of aberrant oncogene activity in tumor cells and the identification of druggable target structures are difficult to define. To illustrate the applicability of RNA signatures as cancer-spanning biomarkers, I established an in vitro screening approach integrating RNA-seq data from siRNA screens, publicly available ChIP-seq data as well as patient expression data from different tumor entities. As exemplified for the Hippo pathway, a 4-gene long non-coding RNA (lncRNA) signature consisting of CYTOR, MIR4435-2HG, SNHG1, and SNHG17 was defined that is transcriptionally controlled by the YAP/TAZ/TEAD complex. This 4-lncRNA signature represents a robust predictor of YAP activity in several tumor types such as liver or lung cancer and its overexpression is statistically associated with poor clinical outcome. In vitro experiments showed that lncRNA signature constituents themselves contribute to the tumor-promoting properties of the Hippo/YAP/TAZ pathway. Furthermore, murine orthologues of these lncRNAs were overexpressed in YAPS127A transgenic mouse livers and lncRNA signature levels were elevated in a subgroup of human cancer tissues and serum samples. Importantly, nuclear YAP accumulation in human liver cancer tissues is significantly associated with YAP-dependent lncRNA abundance in the serum of these patients. Moreover, the signature defines responsiveness of tumor cells to YAP-directed-pharmacological inhibition. These results let me draw the following conclusions: First, lncRNA-based approaches broaden previous liquid biopsy concepts by allowing the detection of potential druggable oncogene activity in the tumor. Second, lncRNAs represent robust and sensitive biomarkers to identify patients eligible for specific oncogene-directed therapies and to monitor treatment response. Third, the lncRNA signature constituents themselves support tumorigenesis in a multi-modal manner. Fourth, the YAP/TAZ/TEAD complex represents a promising target structure to inhibit YAP/TAZ activity. Lastly, YAP and TAZ may play different roles in regulating lncRNA expression depending on the cellular context. Thus, my data underline that liquid biopsy-based detection of pan-cancer lncRNA signatures can define oncogene activity in tumor cells. I therefore conclude that serum lncRNA signatures represent novel and powerful tools for diagnostics, therapy design, as well as for monitoring treatment success

Analysis of Exosomal Cargo Provides Accurate Clinical, Histologic and Mutational Information in Non-Small Cell Lung Cancer

Lung cancer is a malignant disease with high mortality and poor prognosis, frequently diagnosed at advanced stages. Nowadays, immense progress in treatment has been achieved. However, the present scenario continues to be critical, and a full comprehension of tumor progression mechanisms is required, with exosomes being potentially relevant players. Exosomes are membranous vesicles that contain biological information, which can be transported cell-to-cell and modulate relevant processes in the hallmarks of cancer. The present research aims to characterize the exosomes' cargo and study their role in NSCLC to identify biomarkers. We analyzed exosomes secreted by primary cultures and cell lines, grown in monolayer and tumorsphere formations. Exosomal DNA content showed molecular alterations, whereas RNA high-throughput analysis resulted in a pattern of differentially expressed genes depending on histology. The most significant differences were found in XAGE1B, CABYR, NKX2-1, SEPP1, CAPRIN1, and RIOK3 genes when samples from two independent cohorts of resected NSCLC patients were analyzed. We identified and validated biomarkers for adenocarcinoma and squamous cell carcinoma. Our results could represent a relevant contribution concerning exosomes in clinical practice, allowing for the identification of biomarkers that provide information regarding tumor features, prognosis and clinical behavior of the disease. Keywords: non-small cell lung cancer; liquid biopsy; exosomes; extracellular vesicles; cell cultures; adenocarcinoma; squamous cell carcinoma; biomarker; tumorsphere

Identification of a Potentially Functional microRNA-mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis

Background Lung adenocarcinoma (LUAD) is a common lung cancer with a high mortality, for which microRNAs (miRNAs) play a vital role in its regulation. Multiple messenger RNAs (mRNAs) may be regulated by miRNAs, involved in LUAD tumorigenesis and progression. However, the miRNA-mRNA regulatory network involved in LUAD has not been fully elucidated. Methods Differentially expressed miRNAs and mRNA were derived from the Cancer Genome Atlas (TCGA) dataset in tissue samples and from our microarray data in plasma (GSE151963). Then, common differentially expressed (Co-DE) miRNAs were obtained through intersected analyses between the above two datasets. An overlap was applied to confirm the Co-DEmRNAs identified both in targeted mRNAs and DEmRNAs in TCGA. A miRNA-mRNA regulatory network was constructed using Cytoscape. The top five miRNA were identified as hub miRNA by degrees in the network. The functions and signaling pathways associated with the hub miRNA-targeted genes were revealed through Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The key mRNAs in the protein-protein interaction (PPI) network were identified using the STRING database and CytoHubba. Survival analyses were performed using Gene Expression Profiling Interactive Analysis (GEPIA). Results The miRNA-mRNA regulatory network consists of 19 Co-DEmiRNAs and 760 Co-DEmRNAs. The five miRNAs (miR-539-5p, miR-656-3p, miR-2110, let-7b-5p, and miR-92b-3p) in the network were identified as hub miRNAs by degrees (>100). The 677 Co-DEmRNAs were targeted mRNAs from the five hub miRNAs, showing the roles in the functional analyses of the GO analysis and KEGG pathways (inclusion criteria: 836 and 48, respectively). The PPI network and Cytoscape analyses revealed that the top ten key mRNAs were NOTCH1, MMP2, IGF1, KDR, SPP1, FLT1, HGF, TEK, ANGPT1, and PDGFB. SPP1 and HGF emerged as hub genes through survival analysis. A high SPP1 expression indicated a poor survival, whereas HGF positively associated with survival outcomes in LUAD. Conclusion This study investigated a miRNA-mRNA regulatory network associated with LUAD, exploring the hub miRNAs and potential functions of mRNA in the network. These findings contribute to identify new prognostic markers and therapeutic targets for LUAD patients in clinical settings.Peer reviewe

Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection. Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TCGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients. Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients. Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods

Liquid Biopsy in non-small cell lung cancer: exosomes as a tool for the study of biomarkers.

[ES] A pesar de los nuevos avances en el tratamiento del cáncer de pulmón, su tasa de incidencia y mortalidad siguen en cabeza en todo mundo. Concretamente, el cáncer de pulmón no microcítico (CPNM) representa casi el 85% de todos los cánceres de pulmón, siendo su supervivencia a 5 años muy reducida. En base a dicho escenario, el objetivo principal de este trabajo es el de caracterizar de manera exhaustiva los exosomas secretados por las células del CPNM. Se sabe que estas microvesículas están involucradas en números procesos celulares, por lo que pueden contener gran cantidad de información acerca de las características moleculares del tumor. Para ello se han empleado cultivos primarios y líneas comerciales crecidas en diferentes condiciones, así como muestras de sangre periférica obtenida de los pacientes con CPNM. Un primer screening llevado a cabo en los exosomas secretados in vitro, ha permitido obtener un gran número de mRNAs y miRNAs relacionados con diferentes procesos biológicos y vías de señalización. Además, algunos genes como FDFT1 y SNAI1 han destacado por su sobreexpresión en exosomas procedentes de las células crecidas en formación de tumoresferas (modelos 3D), las cuales están enriquecidas en población de células madre tumorales. A su vez, otros marcadores presentes en el interior de estas microvesículas, se han mostrado relacionados con dos de los subtipos histológicos más frecuentes: adenocarcinoma (LUAD) y carcinoma escamoso (LUSC). Posteriormente, para validar los hallazgos obtenidos en exosomas, los marcadores más significativos fueron analizados in silico en una cohorte de muestras de tejido, compuesta por 661 pacientes con CPNM (TCGA database). Estos resultados han revelado una asociación entre la expresión del gen SNAI1 y la supervivencia de estos pacientes (OS y RFS p<0.05). Además, los genes XAGE1B, SEPP1 y TTF-1 (previamente determinados en exosomas), mantienen una relación significativa con el grupo de pacientes LUAD; mientras que CABYR, RIOK3 y CAPRIN1 se mantienen sobrexpresados en LUSC (Mann-Whitney test p<0.05). Estos marcadores también se han analizado en una cohorte de 186 pacientes con CPNM procedentes del Hospital General Universitario de Valencia, donde se corroboró la asociación de SNAI1 con la supervivencia de los pacientes en estadios tempranos (RFS en pacientes LUAD, p<0.05), así como la sobreexpresión de CABYR y RIOK3 en pacientes LUSC, y de XAGE1B y TTF-1 en LUAD. Por otra parte, el aislamiento de los exosomas presentes en la sangre periférica de pacientes en estadios avanzados, ha permitido identificar otros marcadores asociados a caracterísiticas clínico-patológicas relevantes. A su vez, el contenido de estas microvesículas ha sido empleado para la detección de mutaciones génicas ligadas al manejo clínico del CPNM. En resumen, los resultados obtenidos en este trabajo ponen de manifiesto el potencial de los exosomas como fuente de biomarcadores para el estudio de las diferentes etapas de desarrollo del CPNM. Estas microvesículas ofrecen una visión completa y en tiempo real, de las características de la enfermedad, pudiendo ser aisladas de forma repetida y mediante técnicas mínimamente invasivas.[CA] A pesar dels avanços recents en el tractament del càncer de pulmó, les seues taxes d'incidència i mortalitat continuen sent altes a nivell mundial. Concretament, el càncer de pulmó de cèl·lules no petites (CPNM) representa gairebé el 85% de tots els càncers de pulmó, amb una taxa de supervivència a 5 anys molt limitada. Donat aquest escenari, l'objectiu principal d'aquest estudi és caracteritzar de manera exhaustiva els exosomes secretats per les cèl·lules de CPNM. Aquestes microvesícules estan involucrades en nombrosos processos tumorals i poden contenir una gran quantitat d'informació sobre les característiques moleculars de la malaltia. Per aconseguir-ho, es van utilitzar cultius primaris i línies cel·lulars (cultiu en diferents condicions), juntament amb mostres de sang perifèrica obtingudes de pacients amb CPNM. Un cribratge inicial en exosomes secrets in vitro va permetre identificar una quantitat significativa de mARNs i miARNs relacionats amb diversos processos biològics i vies de senyalització. A més, alguns gens com FDFT1 i SNAI1 van destacar per la seua sobreexpressió en exosomes derivats de cèl·lules crescuts en formació de tumorsferes (models 3D), que estan enriquides en poblacions de cèl·lules mare tumorals. A més, s'han trobat marcadors en aquestes microvesícules associats amb dos dels subtipus histològics més comuns: adenocarcinoma (LUAD) i carcinoma escamós (LUSC). Posteriorment, per validar els resultats obtinguts en exosomes, es van analitzar in silico els marcadors més significatius en una cohort de teixit de CPNM de la base de dades TCGA. Aquests resultats van revelar una associació entre l'expressió del gen SNAI1 i la supervivència dels pacients (OS i RFS, p <0,05). A més, l'expressió dels gens XAGE1B, SEPP1 i TTF-1 (prèviament identificats en exosomes) va mantenir una relació significativa amb el grup LUAD, mentre que CABYR, RIOK3 i CAPRIN1 van continuar sobreexpressats en els pacients de LUSC (prova de Mann-Whitney, p <0,05). Aquests marcadors també es van analitzar en una cohort de 186 pacients amb CPNM de l'Hospital General Universitari de València, on es va confirmar l'associació de l'expressió de SNAI1 i la supervivència dels pacients en estadi precoç (RFS en pacients de LUAD, p <0,05), així com la sobreexpressió de CABYR i RIOK3 en pacients de LUSC, i de XAGE1B i TTF-1 en LUAD. D'altra banda, els exosomes presents en mostres de sang de la cohort d'estadis avançats van permetre la identificació d'altres biomarcadors associats a característiques clíniques rellevants dels pacients. A més, la càrrega exosomàtica també es va utilitzar per detectar mutacions genètiques relacionades amb el tractament clínic del CPNM. En resum, els resultats obtinguts en aquesta tesi destaquen el potencial dels exosomes com a font de biomarcadors per a l'estudi de les diferents etapes del desenvolupament del CPNM. Aquestes microvesícules ofereixen una visió completa i en temps real de les característiques moleculars de la malaltia i poden ser obtingudes de manera repetida i amb una mínima invasió.[EN] Despite recent advancements in lung cancer treatment, its incidence and mortality rates remain high worldwide. Specifically, non-small cell lung cancer (NSCLC) accounts for nearly 85% of all lung cancers, with a 5-year survival rate of 20%. Given this scenario, the primary objective of this study is to comprehensively characterize the exosomes secreted by NSCLC cells. These microvesicles are known to be involved in numerous tumoral processes, potentially containing a wealth of information about the molecular characteristics of the disease. To achieve this, primary cultures and cell lines, along with peripheral blood samples obtained from NSCLC patients were used. An initial screening in exosomes secreted in vitro allowed the identification of a significant number of mRNAs and miRNAs, related to various biological processes and signaling pathways. Moreover, some genes such as FDFT1 and SNAI1 stood out due to their overexpression in exosomes derived from cells grown in tumorspheres formation (3D models), which are enriched in cancer stem cell population. Additionally, markers found within these microvesicles were associated with two of the most common histological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Subsequently, to validate the findings seen in exosomes, the most significant markers were analyzed in silico in an NSCLC tissue cohort from the TCGA database. These results revealed an association between the expression of SNAI1 and patient survival (OS and RFS, p<0.05). Furthermore, XAGE1B, SEPP1, and TTF-1 expression (previously identified in exosomes) maintained a significant relationship with the LUAD group, while CABYR, RIOK3, and CAPRIN1 remained overexpressed in LUSC patients (Mann-Whitney test, p<0.05). These markers were also analyzed in a cohort of 186 NSCLC patients from the University General Hospital of Valencia. The association of SNAI1 expression and the survival of early-stage patients (RFS in LUAD patients, p<0.05) was confirmed, as well as the overexpression of CABYR and RIOK3 in LUSC patients, and of XAGE1B and TTF-1 in LUAD. Furthermore, exosomes present in blood samples of the advanced-stage cohort, allowed the identification of other biomarkers associated with clinically relevant characteristics of the patients. Moreover, exosomal cargo was also used to detect gene mutations related to the clinical management of NSCLC. In summary, the results obtained in this thesis highlight the potential of exosomes as a source of biomarkers for the study of the different stages of NSCLC development. These microvesicles offer a comprehensive and real-time view of the disease's molecular features and can be obtained repeatedly and in a minimally invasive way.Duréndez Sáez, ME. (2024). Liquid Biopsy in non-small cell lung cancer: exosomes as a tool for the study of biomarkers [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/20343

Autoencoder-based multimodal prediction of non-small cell lung cancer survival

The ability to accurately predict non-small cell lung cancer (NSCLC) patient survival is crucial for informing physician decision-making, and the increasing availability of multi-omics data offers the promise of enhancing prognosis predictions. We present a multimodal integration approach that leverages microRNA, mRNA, DNA methylation, long non-coding RNA (lncRNA) and clinical data to predict NSCLC survival and identify patient subtypes, utilizing denoising autoencoders for data compression and integration. Survival performance for patients with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) was compared across modality combinations and data integration methods. Using The Cancer Genome Atlas data, our results demonstrate that survival prediction models combining multiple modalities outperform single modality models. The highest performance was achieved with a combination of only two modalities, lncRNA and clinical, at concordance indices (C-indices) of 0.69 ± 0.03 for LUAD and 0.62 ± 0.03 for LUSC. Models utilizing all five modalities achieved mean C-indices of 0.67 ± 0.04 and 0.63 ± 0.02 for LUAD and LUSC, respectively, while the best individual modality performance reached C-indices of 0.64 ± 0.03 for LUAD and 0.59 ± 0.03 for LUSC. Analysis of biological differences revealed two distinct survival subtypes with over 900 differentially expressed transcripts

Molecular Portraits of Cancer Evolution and Ecology

Research on the molecular lesions that drive cancers holds the translational promise of unmasking distinct disease subtypes in otherwise pathologically identical patients. Yet clinical adoption is hindered by the reproducibility crisis for cancer biomarkers. In this thesis, a novel metric uncovered transcriptional diversity within individual non-small cell lung cancers, driven by chromosomal instability. Existing prognostic biomarkers were confounded by tumour sampling bias, arising from this diversity, in ~50% of patients assessed. An atlas of consistently expressed genes was derived to address this diagnostic challenge, yielding a clonal biomarker robust to sampling bias. This diagnostic based on cancer evolutionary principles maintained prognostic value in a metaanalysis of >900 patients, and over known risk factors in stage I disease, motivating further development as a clinical assay. Next, in situ RNA profiles of immune, fibroblast and endothelial cell subsets were generated from cancerous and adjacent non-malignant lung tissue. The phenotypic adaptation of stromal cells in the tumour microenvironment undermined the performance of existing molecular signatures for cell-type enumeration. Transcriptome-wide analysis delineated ~10% of genes displaying cell-type-specific expression, paving the way for high-fidelity signatures for the accurate digital dissection of tumour ecology. Lastly, the impact of branching, Darwinian evolution on the detection of epistatic interactions was evaluated in a pan-cancer analysis. The clonal status of driver genes was associated with the proportion of significant epistatic findings in 44-78% of the cancer-types assessed. Integrating the clonal architecture of tumours in future analyses could help decipher evolutionary dependencies. This work provides pragmatic solutions for refining molecular portraits of cancer in the light of their evolutionary and ecological features, moving the needle for precision cancer diagnostics

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic