Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation

Dang, Huy Hoang; Ta, Hoang Dang Khoa; Nguyen, Truc T. T.; Anuraga, Gangga; Wang, Chih‐Yang; Le, Nguyen Quoc Khanh

doi:10.1007/s10142-022-00883-3

Cited by 47 publications

(34 citation statements)

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“…However, genetic features such as epidermal growth factor receptor mutation status, anaplastic lymphoma kinase mutation status, programmed death ligand 1 expression level, and tumor mutation burden were not recorded in the database. Further efforts on broader clinicopathological features such as radiomics features (33) and tumor molecular profiles (34) recruitment are also warranted. Third, external validation was lacking in this study.…”

Section: Discussionmentioning

confidence: 99%

Stage IV non-small cell lung cancer among young individuals: Incidence, presentations, and survival outcomes of conventional therapies

Cai

Qiu²,

Yang³

et al. 2022

Front. Oncol.

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BackgroundThere is a paucity of data published on the clinicopathological features and prognosis of stage IV non-small cell lung cancer (NSCLC) patients aged ≤45 years. Herein, we evaluated a large clinical series in an effort to provide a clearer picture of this population.MethodsThe least absolute shrinkage and selection operator (LASSO)-penalized Cox regression model was performed to identify prognostic factors for NSCLC among individuals aged ≤45 years. The Kaplan–Meier method with log-rank test was used to compare overall survival (OS) differences between groups. Competing risk analysis with the Fine–Gray test was used to analyze cancer-specific survival (CSS) differences. Propensity score matching (PSM) was used to minimize selection bias.ResultsIncidence-rate analyses, including 588,680 NSCLC cases (stage IV, 233,881; age ≤ 45 years stage IV, 5,483; and age > 45 years stage IV, 228,398) from 2004 to 2015, showed that the incidence of stage IV NSCLC among young individuals decreased over the years. In comparative analyses of clinical features and survival outcomes, a total of 48,607 eligible stage IV cases (age ≤ 45 years stage IV, 1,390; age > 45 years stage IV, 47,217) were included. The results showed that although patients in the young cohort were more likely to be diagnosed at advanced stages, they were also more likely to receive aggressive treatments. In addition, the survival rates of the young patients were superior to those of the older patients both before and after PSM.ConclusionsStage IV NSCLC patients aged ≤45 years comprise a relatively small but special NSCLC subgroup. Although this population had better survival outcomes than older patients, these patients deserve more attention due to their young age and the significant socioeconomic implications.

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Section: Discussionmentioning

confidence: 99%

Stage IV non-small cell lung cancer among young individuals: Incidence, presentations, and survival outcomes of conventional therapies

Cai

Qiu²,

Yang³

et al. 2022

Front. Oncol.

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“…UALCAN (http://ualcan.path.uab.edu/analysis.html) is an online professional database for analyzing tumor gene expression and methylation levels [11][12][13]. We used UAL-CAN to analyze the expression and methylation levels of PYCR1, PYCR2, and PYCRL in healthy subjects and patients with KIRP.…”

Section: Ualcanmentioning

confidence: 99%

“…cBioPortal (http://cbioportal.org) is an online professional database used to analyze genetic alterations in tumors [11][12][13]. We used the cBioPortal database to analyze genetic alterations in PYCR1, PYCR2, PYCRL, and their neighboring genes.…”

Section: Cbioportalmentioning

confidence: 99%

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PYCR in Kidney Renal Papillary Cell Carcinoma: Expression, Prognosis, Gene Regulation Network, and Regulation Targets

Zheng¹,

Lu²,

Cui³

et al. 2022

Front. Biosci. (Landmark Ed)

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Background: Pyrroline-5-carboxylate reductase (PYCR) includes three human genes encoding three isozymes, PYCR1, PYCR2, and PYCR3 (or PYCRL), which facilitate the final step in the conversion of glutamine to proline. These genes play important roles in regulating the cell cycle and redox homeostasis as well as promoting growth signaling pathways. Proline is abnormally upregulated in a variety of cancers, and as the last key enzyme in proline production, PYCR plays an integral role in promoting tumorigenesis and cancer progression. However, its role in patients with kidney renal papillary cell carcinoma (KIRP) has not been fully elucidated. In this study, we aimed to systematically analyze the expression, gene regulatory network, prognostic value, and target prediction of PYCR in patients with KIRP, elucidate the association between PYCR expression and KIRP, and identify potential new targets for the clinical treatment of KIRP. Methods: We systematically analyzed the expression, prognosis, gene regulatory network, and regulatory targets of PYCR1, PYCR2, and PYCRL in KIRP using multiple online databases including cBioPortal, STRING, MethSurv, GeneMANIA, Gene Expression Profiling Interactive Analysis (GEPIA), Metascape, UALCAN, LinkedOmics, and TIMER. Results: The expression levels of PYCR1, PYCR2, and PYCRL were considerably upregulated in patients with KIRP based on sample type, sex, age, and individual cancer stage. PYCR1 and PYCR2 transcript levels were markedly upregulated in females than in males, and patients aged 21-40 years had higher PYCR1 and PYCR2 transcript levels than those in other age groups. Interestingly, PYCR2 transcript levels gradually decreased with age. In addition, the expressions of PYCR1 and PYCR2 were notably correlated with the pathological stage of KIRP. Patients with KIRP with low PYCR1 and PYCR2 expression had longer survival than those with high PYCR1 and PYCR2 expression. PYCR1, PYCR2, and PYCRL were altered by 4%, 7%, and 6%, respectively, in 280 patients with KIRP. The methylation levels of cytosine-phosphate-guanine (CpG) sites in PYCR were markedly correlated with the prognosis of patients with KIRP. PYCR1, PYCR2, PYCRL, and their neighboring genes form a complex network of interactions. The molecular functions of the genes, as demonstrated by their corresponding Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, included calcium channel activity, phospholipid binding, RNA polymerase II-specificity, and kinase and GTPase-regulatory activities. PYCR1, PYCR2, and PYCRL targeted miR-21, miR-221, and miR-222, resulting in a better prognosis of KIRP. We analyzed mRNA sequencing data from 290 patients with KIRP and found that ADA, NPM3, and TKT were positively associated with PYCR1 expression; PFDN2, JTB, and HAX1 were positively correlated with PYCR2 expression; SHARPIN, YDJC, and NUBP2 were positively correlated with PYCRL expression; PYCR1 was positively correlated with B cell and CD8+ T-cell infiltration levels; macrophage infiltration was negatively correlated with PY...

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“…The accurate prediction of disease prognosis and the outcome of drug treatment, which may serve as a reference for treatment decision-making and drug selection, has become an essential topic in the clinical medicine [ 9 , 10 ]. Developing disease-risk and prognosis-prediction models using machine-learning or deep-learning algorithms with big data is a major area of AI-based academic research in the medical field [ 10 , 11 ]. Studies have used machine-learning and/or deep-learning algorithms to develop lung cancer risk and prognosis-prediction models [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival

Hsu

Nguyen²,

Phuc³

et al. 2022

Cancers

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A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018. All patients were monitored from the index date of cancer diagnosis until the event of death. Variables, including demographics, comorbidities, medications, laboratories, and patient gene tests, were used. Nine machine-learning algorithms with various modes were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 3714 patients were included. The best performance of the artificial neural network (ANN) model was achieved when integrating all variables with the AUC, accuracy, precision, recall, and F1-score of 0.89, 0.82, 0.91, 0.75, and 0.65, respectively. The most important features were cancer stage, cancer size, age of diagnosis, smoking, drinking status, EGFR gene, and body mass index. Overall, the ANN model improved predictive performance when integrating different data types.

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Prospective role and immunotherapeutic targets of sideroflexin protein family in lung adenocarcinoma: evidence from bioinformatics validation

Cited by 47 publications

References 50 publications

Stage IV non-small cell lung cancer among young individuals: Incidence, presentations, and survival outcomes of conventional therapies

Stage IV non-small cell lung cancer among young individuals: Incidence, presentations, and survival outcomes of conventional therapies

PYCR in Kidney Renal Papillary Cell Carcinoma: Expression, Prognosis, Gene Regulation Network, and Regulation Targets

Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival

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