Development and validation of an individualized diagnostic signature in thyroid cancer

Transcriptomic era of cancers in females: new epigenetic perspectives and therapeutic prospects

In the era of transcriptomics, the role of epigenetics in the study of cancers in females has gained increasing recognition. This article explores the impact of epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNA, on cancers in females, including breast, cervical, and ovarian cancers (1). Our findings suggest that these epigenetic markers not only influence tumor onset, progression, and metastasis but also present novel targets for therapeutic intervention. Detailed analyses of DNA methylation patterns have revealed aberrant events in cancer cells, particularly promoter region hypermethylation, which may lead to silencing of tumor suppressor genes. Furthermore, we examined the complex roles of histone modifications and long non-coding RNAs in regulating the expression of cancer-related genes, thereby providing a scientific basis for developing targeted epigenetic therapies. Our research emphasizes the importance of understanding the functions and mechanisms of epigenetics in cancers in females to develop effective treatment strategies. Future therapeutic approaches may include drugs targeting specific epigenetic markers, which could not only improve therapeutic outcomes but also enhance patient survival and quality of life. Through these efforts, we aim to offer new perspectives and hope for the prevention, diagnosis, and treatment of cancers in females.

TIPARP as a prognostic biomarker and potential immunotherapeutic target in male papillary thyroid carcinoma

BACKGROUND

Male patients with papillary thyroid carcinoma (PTC) tend to have poorer prognosis compared to females, partially attributable to a higher rate of lymph node metastasis (LNM). Developing a precise predictive model for LNM occurrence in male PTC patients is imperative. While preliminary predictive models exist, there is room to improve accuracy. Further research is needed to create optimized prognostic models specific to LNM prediction in male PTC cases.

METHODS

We conducted a comprehensive search of publicly available microarray datasets to identify candidate genes continuously upregulated or downregulated during PTC progression in male patients only. Univariate Cox analysis and lasso regression were utilized to construct an 11-gene signature predictive of LNM. TIPARP emerged as a key candidate gene, which we validated at the protein level using immunohistochemical staining. A prognostic nomogram incorporating the signature and clinical factors was developed based on the TCGA cohort.

RESULTS

The 11-gene signature demonstrated good discriminative performance for LNM prediction in training and validation datasets. High TIPARP expression associated with advanced stage, high T stage, and presence of LNM. A prognostic nomogram integrating the signature and clinical variables reliably stratified male PTC patients into high and low recurrence risk groups.

CONCLUSIONS

We identified a robust 11-gene signature and prognostic nomogram for predicting LNM occurrence in male PTC patients. We propose TIPARP as a potential contributor to inferior outcomes in males, warranting further exploration as a prognostic biomarker and immunotherapeutic target. Our study provides insights into the molecular basis for gender disparities in PTC.

Application of single-cell sequencing to the research of tumor microenvironment

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

Identification of prognostic signature with seven LncRNAs for papillary thyroid carcinoma

PURPOSE

With the increasing incidence of thyroid cancer (TC), the prognostic risk assessment of thyroid cancer has been becoming more and more important. The aim of this study was to screen TC-related biomarkers and identify key multi-long non coding RNA (lncRNA) signature for prognostic risk assessment of papillary TC.

MATERIAL AND METHODS

The lncRNAs differentially expressed between TC tissue and adjacent normal tissue was identified by R language. Bioinformatics analysis was applied to screen the lncRNAs significantly associated with prognosis in TC patients and build the multi-lncRNA signature. The lncRNAs were annotated by co-expression and enrichment analysis to demonstrate the underlying mechanism of their effect on prognosis.

RESULTS

285 up-regulated and 174 down-regulated differently expressed lncRNAs were identified. Based on seven signature lncRNAs (AL591846.2, AC253536.3, AC004112.1, LINC00900, AC008555.1, TNRC6C-AS1, LINC01736) a prognostic risk assessment model was built. The model can segregate the patients into the high-risk and low-risk groups (P value <0.0001, CI: 0.02∼0.14). ROC analysis revealed that the area under the curve reached 0.86, indicating that this model had an excellent sensitivity and specificity. Also, the model could act as an independent prognostic indication (HR ​= ​2.90, P value ​= ​0.0094 with multivariate analysis). Annotation results further supported and enriched our understanding of the seven signature lncRNAs. Importantly, expression levels of three of the seven lncRNAs were confirmed in Gene Expression Omnibus (GEO) data.

CONCLUSIONS

This study has provided a promising method for the prognostic risk assessment in patients with TC.

Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5–15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly. However, gene signatures of thyroid nodules identified in a plenty of transcriptomic studies are highly inconsistent and extremely difficult to be applied in clinical application. Therefore, it is highly necessary to identify consistent signatures to discriminate benign or malignant thyroid nodules. In this study, five independent transcriptomic studies were combined to discover the gene signature between benign and malignant thyroid nodules. This combined dataset comprises 150 malignant and 93 benign thyroid samples. Then, there were 279 differentially expressed genes (DEGs) discovered by the feature selection method (Student’s t test and fold change). And the weighted gene co-expression network analysis (WGCNA) was performed to identify the modules of highly co-expressed genes, and 454 genes in the gray module were discovered as the hub genes. The intersection between DEGs by the feature selection method and hub genes in the WGCNA model was identified as the key genes for thyroid nodules. Finally, four key genes (ST3GAL5, NRCAM, MT1F, and PROS1) participated in the pathogenesis of malignant thyroid nodules were validated using an independent dataset. Moreover, a high-performance classification model for discriminating thyroid nodules was constructed using these key genes. All in all, this study might provide a new insight into the key differentiation of benign and malignant thyroid nodules.

From bulk, single-cell to spatial RNA sequencing

RNA sequencing (RNAseq) can reveal gene fusions, splicing variants, mutations/indels in addition to differential gene expression, thus providing a more complete genetic picture than DNA sequencing. This most widely used technology in genomics tool box has evolved from classic bulk RNA sequencing (RNAseq), popular single cell RNA sequencing (scRNAseq) to newly emerged spatial RNA sequencing (spRNAseq). Bulk RNAseq studies average global gene expression, scRNAseq investigates single cell RNA biology up to 20,000 individual cells simultaneously, while spRNAseq has ability to dissect RNA activities spatially, representing next generation of RNA sequencing. This article highlights these technologies, characteristic features and suitable applications in precision oncology.

Effect of a WeChat-based perioperative nursing intervention on risk events and self-management efficacy in patients with thyroid cancer

OBJECTIVE

This study aimed to explore the effect of perioperative nursing intervention based on WeChat app on the incidence of risk events and self-management efficacy after thyroid cancer surgery.

METHODS

A total of 96 patients with thyroid cancer were enrolled and divided into a conventional group (n=49) receiving conventional perioperative nursing intervention and the WeChat app group (n=47) receiving WeChat-based perioperative nursing intervention plus conventional nursing. The Chinese version of Strategies Used by People to Promote Health (C-SUPPH) was used to assess self-management efficacy, and the Self-Assessment Scale for Anxiety (SAS) and the Self-Assessment Scale for Depression (SDS) were used to assess psychological status. The occurrence of risk events, changes in self-management efficacy and psychological status, and the occurrence of adverse events were compared between the two groups.

RESULTS

The risk event rate in the WeChat app group (2.13%) was significantly lower than that of the conventional group (14.29%) (P<0.05). Stress reduction, positive attitudes, and decision-making scores were elevated in both groups after intervention (P<0.05), and were significantly higher in the WeChat app group than in the conventional group (P<0.05). SAS and SDS scores were lower in both groups after intervention (P<0.05), and were significantly lower in the WeChat app group than in the conventional group (P<0.05). The rate of postoperative adverse reactions in the WeChat app group (31.91%) was significantly lower than that in the conventional group (73.47%) (P<0.05).

CONCLUSIONS

WeChat app-based perioperative nursing interventions can effectively reduce risk events, improve self-management efficacy, and alleviate adverse emotions of thyroid cancer patients, leading to fewer adverse reactions.

Advances in understanding the molecular pathology of gynecological malignancies: the role and potential of RNA sequencing

For many years technological limitations restricted the progress of identifying the underlying genetic causes of gynecologicalcancers. However, during the past decade, high-throughput next-generation sequencing technologies have revolutionized cancer research. RNA sequencing has arisen as a very useful technique in expanding our understanding of genome changes in cancer. Cancer is characterized by the accumulation of genetic alterations affecting genes, including substitutions, insertions, deletions, translocations, gene fusions, and alternative splicing. If these aberrant genes become transcribed, aberrations can be detected by RNA sequencing, which will also provide information on the transcript abundance revealing the expression levels of the aberrant genes. RNA sequencing is considered the technique of choice when studying gene expression and identifying new RNA species. This is due to the quantitative and qualitative improvement that it has brought to transcriptome analysis, offering a resolution that allows research into different layers of transcriptome complexity. It has also been successful in identifying biomarkers, fusion genes, tumor suppressors, and uncovering new targets responsible for drug resistance in gynecological cancers. To illustrate that we here review the role of RNA sequencing in studies that enhanced our understanding of the molecular pathology of gynecological cancers.

Artificial intelligence in oncology: From bench to clinic

In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproducibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.

Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology

RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.

High incidence of FLT3 mutations in follicular thyroid cancer: potential therapeutic target in patients with advanced disease stage

Background:

Conventional treatments for follicular thyroid cancer (FTC) can be ineffective, leading to poor prognosis. The aim of this study was to identify mutations associated with FTC that would serve as novel molecular markers of the disease and its outcome and could potentially identify new therapeutic targets.

Methods:

FLT3 mutations were first detected in a 29-year-old White female diagnosed with metastasized, treatment-refractory FTC. Analyses of FLT3 mutational status through next-generation sequencing of formalin-fixed, paraffin-embedded FTC specimens were subsequently performed in 35 randomly selected patients diagnosed with FTC.

Results:

FLT3 mutations were found in 69% of patients. FLT3 mutation-positive patients were significantly older than those that were FLT3 mutation-negative [median age at diagnosis 54 (36–82) versus 45 (27–58) (p = 0.023)]. Patients over 60 years were 23 times more likely to be FLT3 mutation-positive (p = 0.006). However, the number of FLT3 mutations did not correlate with age (r-Pearson: –0.244, p-value: 0.25). A total of 26 mutations were identified in the FLT3 gene with 2–16 FLT3 mutations in each FLT3 mutation-positive patient (mean: 5.6 mutations/patient). Tyrosine kinase domain (TKD) mutations in the FLT3 gene were detected in 58% of FLT3 mutation-positive patients. All FLT3 mutation-positive patients with a disease stage of pT2N1 or worse harbored at least one mutation in the TKD of FLT3.

Conclusions:

There is a wide spectrum and high frequency of FLT3 mutations in FTC. The precise role of FLT3 mutations in the genesis of FTC, as well as its potential role as a therapeutic target, requires further investigation.

Identification of a robust non-coding RNA signature in diagnosing autism spectrum disorder by cross-validation of microarray data from peripheral blood samples

Novel molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood non-coding RNA (ncRNA) signature in diagnosing ASD. One hundred eighty six blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profile was re-annotated into the expression profile of 4143 ncRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 20-ncRNA signature, and a diagnostic score was calculated for each sample according to the ncRNA expression levels and the model coefficients. The score demonstrated an excellent diagnostic ability for ASD in the training set (area under receiver operating characteristic curve [AUC] = 0.96), validation set (AUC = 0.97) and the overall (AUC = 0.96). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.96). In subgroup analysis, the signature was also robust when considering the potential confounders of sex, age, and disease subtypes. In comparison with a 55-gene signature deriving from the same dataset, the ncRNA signature showed an obviously better diagnostic ability (AUC: 0.96 vs 0.68, P < .001). In conclusion, this study identified a robust blood ncRNA signature in diagnosing ASD, which might help improve the diagnostic accuracy for ASD in clinical practice.

Integrative Analysis of Methylation and Transcriptome Identified Epigenetically Regulated lncRNAs With Prognostic Relevance for Thyroid Cancer

Emerging evidence has shown that epigenetic changes in DNA methylation, an important regulator of long non-coding RNA (lncRNA) expression, can disturb the expression patterns of lncRNAs and contribute to carcinogenesis. However, knowledge about crosstalk effects between DNA methylation and lncRNA regulation in thyroid cancer (THCA) remain largely unknown. In this study, we performed an integrated analysis of methylation and the transcriptome and identified 483 epigenetically regulated lncRNAs (EpilncRNAs) associated with the development and progression of THCA. These EpilncRNAs can be divided into two categories based on their methylation and expression patterns: 228 HyperLncRNAs and 255 HypoLncRNAs. Then, we identified a methylation-driven 5-lncRNA-based signature (EpiLncPM) to improve prognosis prediction using the random survival forest and multivariate Cox analysis, which were then validated using the training dataset [Hazard ratio (HR) = 50.097, 95% confidence interval (CI): 10.231-245.312, p < 0.001] and testing dataset (HR = 4.395, 95% CI: 0.981-19.686, p = 0.053). Multivariate analysis suggested that the EpiLncPM is an independent prognostic factor. By performing a functional enrichment analysis of GO and KEGG for mRNAs co-expressed with the EpiLncPM, we found that the EpiLncPM was involved in immune and inflammatory-related biological processes. Finally, in situ hybridization analysis in 119 papillary thyroid carcinoma (PTC) tissues and paired adjacent normal tissues revealed that selected candidate lncRNA AC110011 has significantly higher expression of PTC compared to adjacent non-neoplastic tissues, and was closely related to the tumor size, lymph node metastasis, and extrathyroidal extension. In summary, our study characterized the crosstalk between DNA methylation and lncRNA, and provided novel biomarkers for the prognosis of THCA.

Development and validation of a novel and robust blood small nuclear RNA signature in diagnosing autism spectrum disorder

Reliable molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood small nuclear RNA (snRNA) signature in diagnosing ASD. 186 blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profiles were re-annotated into the expression profiles of 1253 snRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 9-snRNA signature (RNU1-16P, RNU6-1031P, RNU6-258P, RNU6-335P, RNU6-485P, RNU6-549P, RNU6-98P, RNU6ATAC26P, and RNVU1-15), and a diagnostic score was calculated for each sample according to the snRNA expression levels and the model coefficients. The score demonstrated a good diagnostic ability for ASD in the training set (area under receiver operating characteristic curve (AUC) = 0.90), validation set (AUC = 0.87), and the overall (AUC = 0.88). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.88). In subgroup analysis, the signature was robust when considering the confounders of gender, age, and disease subtypes, and displayed a significantly better performance among the female and younger cases (P = .039; P = .002). In comparison with a 55-gene signature deriving from the same dataset, the snRNA signature showed a better diagnostic ability (AUC: 0.88 vs 0.80, P = .049). In conclusion, this study identified a novel and robust blood snRNA signature in diagnosing ASD, which might help improve the diagnostic accuracy for ASD in clinical practice. Nevertheless, a large-scale prospective study was needed to validate our results.

Evaluation of 167 Gene Expression Classifier (GEC) and ThyroSeq v2 Diagnostic Accuracy in the Preoperative Assessment of Indeterminate Thyroid Nodules: Bivariate/HROC Meta-analysis

The objective of this meta-analysis was to evaluate the performance of the Gene Expression Classifier (GEC) and ThyroSeq v2 (ThyroSeq) in the preoperative diagnosis of thyroid nodules with indeterminate fine-needle aspiration biopsy results. We searched literature databases from January 2001 to April 2018. The bivariate model analysis was performed to estimate pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), and negative predictive value (NPV). Pooled data from 1086 nodules with histopathologic confirmation from 16 GEC studies enabled calculation of diagnostic parameters (95% confidence interval): sensitivity 98% (96-99%), specificity 12% (8-20%), PPV 45% (37-53%), and NPV 91% (85-96%). Pooled data from five ThyroSeq studies assessing 459 nodules showed sensitivity of 84% (74-91%), specificity 78% (50-92%), PPV 58% (31-81%), and NPV 93% (89-97%). When both tools were compared, GEC had a significantly higher sensitivity (p = 0.003), while ThyroSeq had a significantly higher specificity (p < 0.001) and accuracy (p = 0.015). Pooled LR+ was higher for ThyroSeq: 3.79 (1.40-10.27) vs. 1.12 (1.05-1.20). Pooled LR- was higher for GEC, 0.20 (0.10-0.39) vs. 0.13 (0.05-0.31). The bivariate summary estimates of sensitivity and specificity for GEC and ThyroSeq and their pooled accuracy showed a superiority of the ThyroSeq test. The GEC with a high sensitivity and NPV may be helpful in ruling out malignancy in cases of indeterminate thyroid nodule cytology. ThyroSeq has a significantly higher specificity and accuracy with an acceptable sensitivity so that it has the potential for use as an all-round test of malignancy of thyroid nodules.

Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature

Purpose

Papillary thyroid carcinoma (PTC), the most frequent type of malignant thyroid tumor, lacks novel and reliable biomarkers of patients’ prognosis. In the current study, we mined The Cancer Genome Atlas (TCGA) to develop lncRNA signature of PTC.

Patients and methods

The intersection of PTC lncRNAs was obtained from the TCGA database using integrative computational method. By the univariate and multivariate Cox analysis, key lncRNAs were identified to construct the prognostic model. Then, all patients were divided into the high-risk group and low-risk group to perform the Kaplan–Meier (K–M) survival curves and time-dependent receiver operating characteristic (ROC) curve, estimating the prognostic power of the prognostic model. Functional enrichment analysis was also performed. Finally, we verified the results of the TCGA analysis by the Gene Expression Omnibus (GEO) databases and quantitative real-time PCR (qRT-PCR).

Results

After the comprehensive analysis, a three-lncRNA signature (PRSS3P2, KRTAP5-AS1 and PWAR5) was obtained. Interestingly, patients with low-risk scores tended to gain obviously longer survival time, and the area under the time-dependent ROC curve was 0.739. Furthermore, gene ontology (GO) and pathway analysis revealed the tumorigenic and prognostic function of the three lncRNAs. We also found three potential transcription factors to help understand the mechanisms of the PTC-specific lncRNAs. Finally, the GEO databases and qRT-PCR validation were consistent with our TCGA bioinformatics results.

Conclusion

We built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.

A novel gene-pair signature for relapse-free survival prediction in colon cancer

Background

Colon cancer (CC) patients with early relapse usually have a poor prognosis. In this study, we aimed to identify a novel signature to improve the prediction of relapse-free survival (RFS) in CC.

Methods

Four microarray datasets were merged into a training set (n=1,045), and one RNA-sequencing dataset was used as a validation set (n=384). In the training set, microarray meta-analysis screened out 596 common RFS-related genes across datasets, which were used to construct 177,310 gene pairs. Then, the LASSO penalized generalized linear model identified 16 RFS-related gene pairs, and a risk score was calculated for each sample according to the model coefficients.

Results

The risk score demonstrated a good ability in predicting RFS (area under the curve [AUC] at 5 years: 0.724; concordance index [C-index]: 0.642, 95% CI: 0.615–0.669). High-risk patients showed a poorer prognosis than low-risk patients (HR: 3.519, 95% CI: 2.870–4.314). Subgroup analysis reached consistent results when considering multiple confounders. In the validation set, the risk score had a similar performance (AUC at 5 years: 0.697; C-index: 0.696, 95% CI: 0.627–0.766; HR: 2.926, 95% CI: 1.892–4.527). When compared with a 13-gene signature, a 15-gene signature, and TNM stage, the score showed a better performance (P<0.0001; P=0.0004; P=0.0125), especially for the patients with a longer follow-up (R2=0.988, P<0.0001). When the follow-up was >5 years (n=314), the score demonstrated an excellent performance (C-index: 0.869, 95% CI: 0.816–0.922; HR: 13.55, 95% CI: 7.409–24.78).

Conclusion

Our study identified a novel gene-pair signature for prediction of RFS in CC.