Nomograms incorporating genetic variants in BMP/Smad4/Hamp pathway to predict disease outcomes after definitive radiotherapy for non-small cell lung cancer

High Hepcidin expression predicts poor prognosis in patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a growing public health challenge worldwide. Hepcidin antimicrobial peptide (HAMP) is differentially expressed in various tumors. However, the roles and functions of HAMP in ccRCC remain unclear. In the present study, we integrated systematic bioinformatics approaches to investigate the roles and functions of HAMP and its association with immune cell infiltration in ccRCC. Compared with paracancerous tissue, HAMP expression was significantly upregulated in ccRCC patients. Meanwhile, we found good diagnostic performance of HAMP for ccRCC patients and its close associations with the clinicopathological features of ccRCC patients. In addition, we found that HAMP is closely related to multiple immune pathways and positively correlated with various immune cells. HAMP was a significant independent predictor for ccRCC. High expression of HAMP was associated with worse clinical prognosis and more immune cell infiltration in ccRCC patients. HAMP may offer potential as a biomarker to predict prognosis and the clinical treatment outcome of ccRCC patients.

A nomogram based on A-to-I RNA editing predicting overall survival of patients with lung squamous carcinoma

Background

Adenosine-to-inosine RNA editing (ATIRE) is characterized as non-mutational epigenetic reprogramming hallmark of cancer, while little is known about its predictive role in cancer survival.

Methods

To explore survival-related ATIRE events in lung squamous cell carcinoma (LUSC), ATIRE profile, gene expression data, and corresponding clinical information of LUSC patients were downloaded from the TCGA database. Patients were randomly divided into a training (n = 134) and validation cohort (n = 94). Cox proportional hazards regression followed by least absolute shrinkage and selection operator algorithm were performed to identify survival-related ATIRE sites and to generate ATIRE risk score. Then a nomogram was constructed to predict overall survival (OS) of LUSC patients. The correlation of ATIRE level and host gene expression and ATIREs’ effect on transcriptome expression were analyzed.

Results

Seven ATIRE sites that were TMEM120B chr12:122215052A > I, HMOX2 chr16:4533713A > I, CALCOCO2 chr17:46941503A > I, LONP2 chr16:48388244A > I, ZNF440 chr19:11945758A > I, CLCC1 chr1:109474650A > I, and CHMP3 chr2:86754288A > I were identified to generate the risk score, of which high levers were significantly associated with worse OS and progression-free survival in both the training and validation sets. High risk-score was also associated with advanced T stages and worse clinical stages. The nomogram performed well in predicting OS probability of LUSC. Moreover, the editing of ATIRE sites exerted a significant association with expression of host genes and affected several cancer-related pathways.

Conclusions

This is the first comprehensive study to analyze the role of ATIRE events in predicting LUSC survival. The AITRE-based model might serve as a novel tool for LUSC survival prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09773-0.

Genetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Cancer

Purpose: Transforming growth factor-β1 (TGF-β1), a known immune suppressor, plays an important role in tumor progression and overall survival (OS) in many types of cancers. We hypothesized that genetic variations of single nucleotide polymorphisms (SNPs) in the TGF-β1 pathway can predict survival in patients with non-small cell lung cancer (NSCLC) after radiation therapy. Materials and Methods: Fourteen functional SNPs in the TGF-β1 pathway were measured in 166 patients with NSCLC enrolled in a multi-center clinical trial. Clinical factors, including age, gender, ethnicity, smoking status, stage group, histology, Karnofsky Performance Status, equivalent dose at 2 Gy fractions (EQD2), and the use of chemotherapy, were first tested under the univariate Cox's proportional hazards model. All significant clinical predictors were combined as a group of predictors named "Clinical." The significant SNPs under the Cox proportional hazards model were combined as a group of predictors named "SNP." The predictive powers of models using Clinical and Clinical + SNP were compared with the cross-validation concordance index (C-index) of random forest models. Results: Age, gender, stage group, smoking, histology, and EQD2 were identified as significant clinical predictors: Clinical. Among 14 SNPs, BMP2:rs235756 (HR = 0.63; 95% CI:0.42-0.93; p = 0.022), SMAD9:rs7333607 (HR = 2.79; 95% CI 1.22-6.41; p = 0.015), SMAD3:rs12102171 (HR = 0.68; 95% CI: 0.46-1.00; p = 0.050), and SMAD4: rs12456284 (HR = 0.63; 95% CI: 0.43-0.92; p = 0.016) were identified as powerful predictors of SNP. After adding SNP, the C-index of the model increased from 84.1 to 87.6% at 24 months and from 79.4 to 84.4% at 36 months. Conclusion: Genetic variations in the TGF-β1 pathway have the potential to improve the prediction accuracy for OS in patients with NSCLC.

Iron Metabolism in Cancer

Demanded as an essential trace element that supports cell growth and basic functions, iron can be harmful and cancerogenic though. By exchanging between its different oxidized forms, iron overload induces free radical formation, lipid peroxidation, DNA, and protein damages, leading to carcinogenesis or ferroptosis. Iron also plays profound roles in modulating tumor microenvironment and metastasis, maintaining genomic stability and controlling epigenetics. in order to meet the high requirement of iron, neoplastic cells have remodeled iron metabolism pathways, including acquisition, storage, and efflux, which makes manipulating iron homeostasis a considerable approach for cancer therapy. Several iron chelators and iron oxide nanoparticles (IONPs) has recently been developed for cancer intervention and presented considerable effects. This review summarizes some latest findings about iron metabolism function and regulation mechanism in cancer and the application of iron chelators and IONPs in cancer diagnosis and therapy.