Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway

Exosome-Mediated Communication in Thyroid Cancer: Implications for Prognosis and Therapeutic Targets

Thyroid cancer (THCA) is one of the most common malignancies of the endocrine system. Exosomes have significant value in performing molecular treatments, evaluating the diagnosis and determining tumor prognosis. Thus, the identification of exosome-related genes could be valuable for the diagnosis and potential treatment of THCA. In this study, we examined a set of exosome-related differentially expressed genes (DEGs) (BIRC5, POSTN, TGFBR1, DUSP1, BID, and FGFR2) by taking the intersection between the DEGs of the TCGA-THCA and GeneCards datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the exosome-related DEGs indicated that these genes were involved in certain biological functions and pathways. Protein‒protein interaction (PPI), mRNA‒miRNA, and mRNA-TF interaction networks were constructed using the 6 exosome-related DEGs as hub genes. Furthermore, we analyzed the correlation between the 6 exosome-related DEGs and immune infiltration. The Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Cell Line Encyclopedia (CCLE), and the CellMiner database were used to elucidate the relationship between the exosome-related DEGs and drug sensitivity. In addition, we verified that both POSTN and BID were upregulated in papillary thyroid cancer (PTC) patients and that their expression was correlated with cancer progression. The POSTN and BID protein expression levels were further examined in THCA cell lines. These findings provide insights into exosome-related clinical trials and drug development.

[Overexpression of LncRNA MEG3 promotes ferroptosis and enhances chemotherapy sensitivity of hepatocellular carcinoma cells to cisplatin]

OBJECTIVE

To investigate the effect of overexpression of LncRNA MEG3 on proliferation, migration and cisplatin sensitivity of hepatoma cells HepG2 and LM3 and explore the underlying and mechanism.

METHODS

The expression of MEG3 in healthy individuals and patients with hepatocellular carcinoma (HCC) was analyzed by online bioinformatics analysis, and Real-time fluorescence quantitative PCR (qRT-PCR) was used to detect MEG3 expression in different HCC cell lines. A MEG3-overexpresing plasmid was transfected in HepG2 and LM3 cells, and the changes in cell proliferation and migration were examined using CCK8 assay and scratch assay. CCK8 assay was used to determine the inhibitory rate of cisplatin on the transfected cells. A reactive oxygen species (ROS) fluorescence probe (DCFH-DA) and malondialdehyde (MDA) kit were used to assess the changes in ROS production and MDA level in the cells. Western blotting was performed to detect the expression levels of ferroptosis-related proteins glutathione peroxidase 4 (GPX4) and ferritin heavy chain 1 (FTH1).

RESULTS

The expression of MEG3 was significantly lower in HCC cells than in LO2 cells, which was consistent with the results of bioinformatic analysis (P < 0.05). Overexpression of MEG3 in the HCC cell lines significantly suppressed cell proliferation and migration (P < 0.05), increased the cell inhibition rate of cisplatin (P < 0.05), enhanced cellular ROS production and increased MDA levels in the cells (P < 0.05). MEG3 overexpression significantly decreased the expressions of GPX4 and FTH1 in the HCC cell lines.

CONCLUSION

The expression of MEG3 is decreased in HCC cells, and its overexpression inhibits proliferation and migration and enhances cisplatin sensitivity of HCC cells by promoting ferroptosis of the cells.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Clinical significance and diagnostic value of QPCT, SCEL and TNFRSF12A in papillary thyroid cancer

PURPOSE

To identify specific novel genes that could be used as diagnostic and prognostic factors in papillary thyroid carcinoma (PTC).

METHODS

Screening of differential genes by RNA sequencing (RNA-Seq) in normal thyroid, Hashimoto's thyroiditis, PTC combined with Hashimoto's thyroiditis and PTC tissues. The genes QPCT, SCEL and TNFRSF12A were selected by qRT-PCR and immunohistochemical pre-experiments. The GEPIA2 database, qRT-PCR, and immunohistochemical studies were used to confirm the target genes QPCT, SCEL, and TNFRSF12A. ROC curves were used to assess the diagnostic usefulness of these 3 genes for PTC in more detail.

RESULTS

Functional enrichment analysis showed that QPCT, SCEL and TNFRSF12A were enriched in the pathways for peptidyl-pyroglutamic acid biosynthesis, keratinocyte differentiation, WNT signaling, apoptosis. GEPIA2 database analysis revealed that QPCT, SCEL and TNFRSF12A were high in thyroid cancer, and TC patients with lower TNFRSF12A levels had short survival. QPCT, SCEL and TNFRSF12A were elevated in PTC and thyroid adenoma. The mRNA diagnostic values were as follows: for QPCT, AUROC = 0.891, 95% CI, 0.835-0.947; for SCEL, AUROC = 0.921, 95% CI, 0.869-0.974; for TNFRSF12A, AUROC = 0.884, 95% CI, 0.809-0.958. Immunohistochemical results showed that QPCT, SCEL, and TNFRSF12A differed to varying degrees between subgroups of thyroid tissue. SCEL was associated with BRAF V600E mutation status and stratification of recurrence risk, while TNFRSF12A was associated with Cyclin D1. The protein diagnostic values were as follows: for QPCT, AUROC = 0.752, 95% CI, 0.685-0.819; for SCEL, AUROC = 0.715, 95% CI, 0.645-0.784; for TNFRSF12A, AUROC = 0.660, 95% CI, 0.587-0.734.

CONCLUSION

QPCT, SCEL and TNFRSF12A are expected to be diagnostic markers for PTC.

VHL tumor suppressor as a novel potential candidate biomarker in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of endocrine cancer, with an increasing incidence worldwide. The treatment of PTC is currently the subject of clinical controversy, making it critically important to identify molecular markers that would help improve the risk stratification of PTC patients and optimize the therapeutic approach. The VHL tumor suppressor gene has been implicated in tumorigenesis of various types of carcinoma and linked with their aggressive biological behavior. The role of VHL in the origin and development of PTC has only recently begun to be revealed. In this narrative review we attempt to summarize the existing knowledge that implicates VHL in PTC pathogenesis and to outline its potential significance as a candidate molecular biomarker for the grouping of PTC patients into high and low risk groups.

MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma

Purpose

BRAF V600E mutation can compensate for the low detection rate by fine-needle aspiration (FNA) and is related to aggressiveness and lymph node metastasis. This study aimed to investigate the relationship between texture analysis features based on magnetic resonance imaging (MRI) and mutations.

Methods

Retrospective analysis was performed on patients with postoperative pathology confirmed papillary thyroid carcinoma (PTC) from 2017 to 2021. One thousand one hundred and thirty-two texture features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) separately by outlining the tumor volume of interest (VOI). Univariate, minimum redundancy maximum relevance (mRMR), and multivariate analyses were used for feature selection to construct 3 models (T2WI, CE-T1WI, and combined model) to predict mutation. The reproducibility between observers was evaluated by intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) analysis was used to assess the performance of models. The diagnostic performance of the optimal cut-off value of models were calculated and validated by 10-fold cross-validation.

Results

A total of 80 PTCs (22 BRAF V600E wild-type and 58 BRAF V600E mutant) were included in our study. Good interobserver agreement was found on texture features we selected (all ICCs >0.75). The area under the ROC curves (AUCs) for the T2WI model, CE-T1WI model, and combined model were 0.83 (95% CI: 0.75-0.91), 0.83 (95% CI: 0.73-0.90), and 0.88 (95% CI: 0.81-0.94), respectively. The accuracy, sensitivity, specificity, PPV, and NPV were 0.776, 0.679, 0.905, 0.905, and 0.679 for the T2WI model at a cut-off value of 0.674; 0.755, 0.750, 0.762, 0.808, and 0.696 for the CE-T1WI model at a cut-off value of 0.573; 0.816, 0.893, 0.714, 0.806, and 0.833 for the combined model at a cut-off value of 0.420.

Conclusion

MRI-based texture analysis could be a potential method for predicting BRAF V600E mutation in PTC preoperatively.

Prediction of EVT6-NTRK3-Dependent Papillary Thyroid Cancer Using Minor Expression Profile

Solid tumors resulting from oncogenic stimulation of neurotrophin receptors (TRK) by chimeric proteins are a group of rare tumors of various localization that respond to therapy with targeted drugs entrectinib and larotrectinib. The standard method for detecting chimeric TRK genes in tumor samples today is considered to be next generation sequencing with the determination of the prime structure of the chimeric transcripts. We hypothesized that expression of the chimeric tyrosine kinase proteins in tumors can determine the specific transcriptomic profile of tumor cells. We detected differentially expressed genes allowing distinguishing between TRK-dependent tumors papillary thyroid cancer (TC) from other molecular variants of tumors of this type. Using PCR with reverse transcription (RT-PCR), we identified 7 samples of papillary TC carrying a EVT6-NTRK3 rearrangement (7/215, 3.26%). Using machine learning and the data extracted from TCGA, we developed of a recognition function for predicting the presence of rearrangement in NTRK genes based on the expression of 10 key genes: AUTS2, DTNA, ERBB4, HDAC1, IGF1, KDR, NTRK1, PASK, PPP2R5B, and PRSS1. The recognition function was used to analyze the expression data of the above genes in 7 TRK-dependent and 10 TRK-independent thyroid tumors obtained by RT-PCR. On the test samples from TCGA, the sensitivity was 72.7%, the specificity - 99.6%. On our independent validation samples tested by RT-PCR, sensitivity was 100%, specificity - 70%. We proposed an mRNA profile of ten genes that can classify TC in relation to the presence of driver NTRK-chimeric TRK genes with acceptable sensitivity and specificity.

Identification and Validation of a Prognostic Signature for Thyroid Cancer Based on Ferroptosis-Related Genes

Background: Thyroid cancer is the most common endocrine malignancy. Most PTC patients have a good prognosis; however, there are 5–20% of PTC patients with extra-thyroidal invasion, vascular invasion, or distant metastasis who have relatively poor prognoses. The aim of this study is to find new and feasible molecular pathological markers and therapeutic targets for early identification and appropriate management. Methods: The GEO and TCGA databases were used to gather gene expression data and clinical outcomes. Based on gene expression and clinical parameters, we developed a ferroptosis-related gene-based prognostic model and a nomogram. CCK-8, wound-healing, and transwell assays were conducted to explore the proliferation, migration, and invasion abilities of thyroid cancer cells. Results: We found 75 genes associated with ferroptosis that were differentially expressed between normal thyroid tissue and thyroid cancer tissues. The prognostic values of the 75 ferroptosis-related gene expressions were evaluated using the TCGA-THCA dataset, and five (AKR1C3, BID, FBXW7, GPX4, and MAP3K5) of them were of significance. Following that, we chose AKR1C3 as the subject for further investigation. By combining gene expression and clinical parameters, we developed a ferroptosis-related gene-based prognostic model with an area under the curve (AUC) of 0.816, and the nomogram also achieved good predictive efficacy for the three-year survival rate of thyroid cancer patients. Knocking down AKR1C3 enhances thyroid cancer cell proliferation, invasion, and migration abilities. Conclusions: A ferroptosis-related gene-based prognostic model was constructed that provided unique insights into THCA prognosis prediction. In addition, AKR1C3 was found to be a progression promoter in thyroid cancer cell lines.