Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Dedifferentiation of Papillary Thyroid Cancer

LPCAT2 inhibits colorectal cancer progression via the PRMT1/SLC7A11 axis

Colorectal cancer (CRC) has a high degree of heterogeneity and identifying the genetic information of individual tumor cells could help enhance our understanding of tumor biology and uncover potential therapeutic targets for CRC. In this study, we identified LPCAT2+ tumor cell populations with less malignancy than LPCAT2- tumor cells in human and mouse CRC tissues using scRNA-seq. Combining in vitro and in vivo experiments, we found that LPCAT2 could inhibit the proliferation of CRC cells by inducing ferroptosis. Mechanistically, LPCAT2 arrested PRMT1 in cytoplasm of CRC cells via regulating acetylation of PRMT1 at the K145 site. In turn, PRMT1 enhanced SLC7A11 promoter activity. Thus, LPCAT2 attenuated the positive regulatory effect of PRMT1 on SLC7A11 promoter. Notably, SLC7A11 acts as a ferroptosis regulator. Furthermore, in LPCAT2 knockout mice (LPCAT2-/-) colon cancer model, we found that LPCAT2-/- mice exhibited more severe lesions, while PRMT1 or SLC7A11 inhibitors delayed the progression. Altogether, we elucidated that LPCAT2 suppresses SLC7A11 expression by inhibiting PRMT1 nuclear translocation, thereby inducing ferroptosis in CRC cells. Moreover, inhibitors of the PRMT1/SLC7A11 axis could delay tumor progression in CRC with low LPCAT2 expression, making it a potentially effective treatment for CRC.

Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks

Although papillary thyroid cancer (PTC) has a good prognosis, its recurrence rate is high and remains a core concern in the clinic. Molecular factors contributing to different recurrence risks (RRs) remain poorly defined. Here, we perform an integrative proteogenomic and metabolomic characterization of 102 Chinese PTC patients with different RRs. Genomic profiling reveals that mutations in MUC16 and TERT promoter as well as multiple gene fusions like NCOA4-RET are enriched by the high RR. Integrative multi-omics analyses further describe the multi-dimensional characteristics of PTC, especially in metabolism pathways, and delineate dominated molecular patterns of different RRs. Moreover, the PTC patients are clustered into four subtypes (CS1: low RR and BRAF-like; CS2: high RR and metabolism type, worst prognosis; CS3: high RR and immune type, better prognosis; CS4: high RR and BRAF-like) based on the omics data. Notably, the subtypes display significant differences considering BRAF and TERT promoter mutations, metabolism and immune pathway profiles, epithelial cell compositions, and various clinical factors (especially RRs and prognosis) as well as druggable targets. This study can provide insights into the complex molecular characteristics of PTC recurrences and help promote early diagnosis and precision treatment of recurrent PTC.

tRF-1:30-Gly-CCC-3 inhibits thyroid cancer via binding to PC and modulating metabolic reprogramming

tRFs and tiRNAs (tRNA-derived fragments) are an emerging class of small noncoding RNAs produced by the precise shearing of tRNAs in response to specific stimuli. They have been reported to regulate the pathological processes of numerous human cancers. However, the biofunction of tRFs and tiRNAs in the development and progression of papillary thyroid cancer (PTC) has not been reported yet. In this study, we aimed to explore the biological roles of tRFs and tiRNAs in PTC and discovered that a novel 5'tRNA-derived fragment called tRF-1:30-Gly-CCC-3 (tRF-30) was markedly down-regulated in PTC tissues and cell lines. Functionally, tRF-30 inhibited the proliferation and invasion of PTC cells. Mechanistically, tRF-30 directly bound to the biotin-dependent enzyme pyruvate carboxylase (PC), downregulated its protein level, interfered with the TCA cycle intermediate anaplerosis, and thus affected metabolic reprogramming and PTC progression. These findings revealed a novel regulatory mechanism for tRFs and a potential therapeutic target for PTC.

Unraveling the role of the mitochondrial one-carbon pathway in undifferentiated thyroid cancer by multi-omics analyses

The role of the serine/glycine metabolic pathway (SGP) has recently been demonstrated in tumors; however, the pathological relevance of the SGP in thyroid cancer remains unexplored. Here, we perform metabolomic profiling of 17 tumor-normal pairs; bulk transcriptomics of 263 normal thyroid, 348 papillary, and 21 undifferentiated thyroid cancer samples; and single-cell transcriptomes from 15 cases, showing the impact of mitochondrial one-carbon metabolism in thyroid tumors. High expression of serine hydroxymethyltransferase-2 (SHMT2) and methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is associated with low thyroid differentiation scores and poor clinical features. A subpopulation of tumor cells with high mitochondrial one-carbon pathway activity is observed in the single-cell dataset. SHMT2 inhibition significantly compromises mitochondrial respiration and decreases cell proliferation and tumor size in vitro and in vivo. Collectively, our results highlight the importance of the mitochondrial one-carbon pathway in undifferentiated thyroid cancer and suggest that SHMT2 is a potent therapeutic target.

Development of a Novel Lipid Metabolism-related Gene Prognostic Signature for Patients with Colorectal Cancer

BACKGROUND

The purpose of this study was to explore the expression profiles of lipid metabolism-related genes in patients with Colorectal Cancer (CRC).

METHODS

The lipid metabolism statuses of CRC patients from The Cancer Genome Atlas (TCGA) were analyzed. Risk characteristics were constructed by univariate Cox regression and minimum Absolute contraction and Selection Operator (LASSO) Cox regression. A histogram was constructed based on factors such as age, sex, TNM stage, T stage, N stage, and risk score to provide a visual tool for clinicians to predict the probability of 1-year, 3-year, and 5-year OS for CRC patients. By determining Area Under Curve (AUC) values, the time-dependent Receiver Operating characteristic Curve (ROC) was used to evaluate the efficiency of our model in predicting prognosis.

RESULTS

A novel risk signal based on lipid metabolism-related genes was constructed to predict the survival of CRC patients. Risk characteristics were shown to be an independent prognostic factor in CRC patients (p <0.001). There were significant differences in the abundance and immune characteristics of tumor-filtering immune cells between high-risk and low-risk groups. The nomogram had a high potential for clinical application and the ROC AUC value was 0.827. Moreover, ROC analysis demonstrated that the nomogram model was more accurate to predict the survival of CRC patients than age, gender, stage and risk score.

CONCLUSION

In this study, we demonstrated a lipid metabolism-related genes prognosis biomarker associated with the tumor immune micro-environment in patients with CRC.

Advances in transcriptomics and proteomics in differentiated thyroid cancer: An updated perspective (Review)

Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.

Targeting metabolism by B-raf inhibitors and diclofenac restrains the viability of BRAF-mutated thyroid carcinomas with Hif-1α-mediated glycolytic phenotype

BACKGROUND

B-raf inhibitors (BRAFi) are effective for BRAF-mutated papillary (PTC) and anaplastic (ATC) thyroid carcinomas, although acquired resistance impairs tumour cells' sensitivity and/or limits drug efficacy. Targeting metabolic vulnerabilities is emerging as powerful approach in cancer.

METHODS

In silico analyses identified metabolic gene signatures and Hif-1α as glycolysis regulator in PTC. BRAF-mutated PTC, ATC and control thyroid cell lines were exposed to HIF1A siRNAs or chemical/drug treatments (CoCl2, EGF, HGF, BRAFi, MEKi and diclofenac). Genes/proteins expression, glucose uptake, lactate quantification and viability assays were used to investigate the metabolic vulnerability of BRAF-mutated cells.

RESULTS

A specific metabolic gene signature was identified as a hallmark of BRAF-mutated tumours, which display a glycolytic phenotype, characterised by enhanced glucose uptake, lactate efflux and increased expression of Hif-1α-modulated glycolytic genes. Indeed, Hif-1α stabilisation counteracts the inhibitory effects of BRAFi on these genes and on cell viability. Interestingly, targeting metabolic routes with BRAFi and diclofenac combination we could restrain the glycolytic phenotype and synergistically reduce tumour cells' viability.

CONCLUSION

The identification of a metabolic vulnerability of BRAF-mutated carcinomas and the capacity BRAFi and diclofenac combination to target metabolism open new therapeutic perspectives in maximising drug efficacy and reducing the onset of secondary resistance and drug-related toxicity.

Circadian Genes MBOAT2/CDA/LPCAT2/B4GALT5 in the Metabolic Pathway Serve as New Biomarkers of PACA Prognosis and Immune Infiltration

Pancreatic cancer (PACA) is a highly malignant tumor with a poor prognosis. Recent studies have discovered substantial differences in the expression levels of several circadian genes in PACA samples compared to normal samples. The goal of this research was to find differentially expressed rhythm genes (DERGs) in PACA samples and determine their role in the development of PACA. A total of 299 DERGs were identified in PACA, including 134 downregulated genes and 165 upregulated genes. DERGs were significantly abundant in the metabolic pathway and immune response pathways, according to GO and KEGG analyses. Survival analyses showed that PACA patients who had higher expression levels of MBOAT2/CDA/LPCAT2/B4GALT5 had shorter overall survival times. Using cell assay verification, the mRNA levels of MBOAT2/CDA/LPCAT2/B4GALT5 in Patu-8988 and PNAC-1 cells were found to be significantly higher than those in HPDE6-C7 cells, which was in line with previous studies on PACA patient data. Through conducting univariate Cox analysis, it was determined that MBOAT2/CDA/LPCAT2/B4GALT5 expression, age and grade were all high-risk factors. The MBOAT2/CDA/LPCAT2/B4GALT5 genes were independently correlated with overall survival, according to the multivariate Cox analysis. The proportion of immune cells in PACA and normal samples significantly changed, according to the immune infiltration analysis. Furthermore, MBOAT2/CDA/LPCAT2/B4GALT5 expression levels were significantly related to the level of immune cell infiltration. The protein-protein interaction network of the MBOAT2/CDA/LPCAT2/B4GALT5 genes included 54 biological nodes and 368 interacting genes. In conclusion, the finding of these DERGs adds to the investigation of the molecular processes underlying the onset and progression of PACA. In the future, DERGs may serve as prognostic and diagnostic biomarkers as well as drug targets for chronotherapy in PACA patients.

LINC00886 Negatively Regulates Malignancy in Anaplastic Thyroid Cancer

Anaplastic thyroid cancer (ATC) is the most aggressive type of thyroid cancer. This study aimed to identify specific long noncoding RNAs (lncRNAs) associated with ATC, and further investigated their biological functions and molecular mechanism underlying regulation of malignancy in ATC. We searched for lncRNAs associated with dedifferentiation and screened out specific lncRNAs significantly deregulated in ATC by using transcriptome data of dedifferentiation cancers from Fudan University Shanghai Cancer Center (FUSCC) and the Gene Expression Omnibus (GEO) database. The above lncRNAs were analyzed to identify a potential biomarker in thyroid cancer patients from the FUSCC, GEO, and The Cancer Genome Atlas, which was then investigated for its functional roles and molecular mechanism in ATC in vitro. The clinicopathological association analyses revealed that LINC00886 expression was significantly correlated with dedifferentiation and suppressed in ATC. In vitro, LINC00886 was confirmed to negatively regulate cell proliferation, and cell migration and invasion of ATC. LINC00886 physically interacted with protein kinase R (PKR) and affected its stability through the ubiquitin/proteasome-dependent degradation pathway in the ATC cell. Decreased PKR caused by downregulation of LINC00886 enhanced the activity of eukaryotic initiation factor 2α (eIF2α) via reducing phosphorylation of eIF2α and thus promoted protein synthesis to maintain ATC malignancy. Our findings identify LINC00886 as a novel biomarker of thyroid cancer and suggest that LINC00886/PKR/eIF2α signaling is a potential therapeutic target in ATC.

Targeting Tumor Hypoxia Inhibits Aggressive Phenotype of Dedifferentiated Thyroid Cancer

CONTEXT

Hypoxia is commonly observed in multiple aggressive cancers. Its role remains unclear in the biology and therapy of dedifferentiated thyroid cancer (DDTC).

OBJECTIVE

We aimed to elucidate hypoxia's roles in DDTC tumor biology.

METHODS

We discovered and confirmed hypoxia's correlation with dedifferentiation status, poor prognoses, and immune checkpoints in thyroid cancer using transcriptome data from our center and Gene Expression Omnibus (GEO) database. Then, the effect of targeting hypoxia was investigated via treating anaplastic thyroid cancer (ATC) cells with acriflavine (ACF) in vitro and in vivo, and hypoxia was analyzed for its association with response to immunotherapy in patients.

RESULTS

Hypoxia score was positively associated with dedifferentiation status, and high hypoxia score significantly correlated with reduced overall survival, TP53 mutation, and elevated expression of immunosuppression-related markers in DDTC. ACF and siRNA targeting HIF-1α significantly suppressed growth and proliferation of thyroid cancer cells in vitro and in vivo, and reduced c-MYC and PDL1 expression in ATC. HIF-1α showed a positive correlation with PDL1 expression in DDTC. Integrated analyses of phosphoproteome and RNA sequencing data revealed that ACF's target was connected with differentiation genes and immune checkpoints via tumor-related kinases in ATC. Furthermore, hypoxia score was associated with immunotherapeutic response in some cancer types.

CONCLUSION

Hypoxia score serves as a significant indicator for dedifferentiation status, prognoses, and immunotherapeutic response predicted by Tumor Immune Dysfunction and Exclusion in DDTC patients. Targeting hypoxia by ACF is useful to alleviate aggressive phenotype of ATC in a preclinical model of DDTC.

Comprehensive Analysis of the Prognosis and Drug Sensitivity of Differentiation-Related lncRNAs in Papillary Thyroid Cancer

Simple Summary

Merging evidence has indicated that dedifferentiation is the main concern associated with radioactive iodine (RAI) refractoriness in patients with papillary thyroid cancer (PTC), and the underlying mechanisms of PTC dedifferentiation remain unclear. This study systematically delineated the expression pattern, tumor immune microenvironment, drug sensitivity, and prognostic value of differentiation-related lncRNAs. It also demonstrated that DPH6-DT could be considered as a novel signature to indicate differentiation and promote TC progression via activating the PI3K-AKT signaling pathway.

Abstract

Dedifferentiation is the main concern associated with radioactive iodine (RAI) refractoriness in patients with papillary thyroid cancer (PTC), and the underlying mechanisms of PTC dedifferentiation remain unclear. The present work aimed to identify a useful signature to indicate dedifferentiation and further explore its role in prognosis and susceptibility to chemotherapy drugs. A total of five prognostic-related DR-lncRNAs were selected to establish a prognostic-predicting model, and corresponding risk scores were closely associated with the infiltration of immune cells and immune checkpoint blockade. Moreover, we built an integrated nomogram based on DR-lncRNAs and age that showed a strong ability to predict the 3- and 5-year overall survival. Interestingly, drug sensitivity analysis revealed that the low-risk group was more sensitive to Bendamustine and TAS-6417 than the high-risk group. In addition, knockdown of DR-lncRNAs (DPH6-DT) strongly promoted cell proliferation, invasion, and migration via PI3K-AKT signal pathway in vitro. Furthermore, DPH6-DT downregulation also increased the expression of vimentin and N-cadherin during epithelial-mesenchymal transition. This study firstly confirms that DR-lncRNAs play a vital role in the prognosis and immune cells infiltration in patients with PTC, as well as a predictor of the drugs’ chemosensitivity. Based on our results, DR-lncRNAs can serve as a promising prognostic biomarkers and treatment targets.

Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

BACKGROUND

Biochemical recurrence (BCR) after initial treatment, such as radical prostatectomy, is the most frequently adopted prognostic factor for patients who suffer from prostate cancer (PCa). In this study, we aimed to construct a prognostic model consisting of gene expression profiles to predict BCR-free survival.

METHODS

We analyzed 70 metabolic pathways in 152 normal prostate samples and 494 PCa samples from the UCSC Xena dataset (training set) via gene set enrichment analysis (GSEA) to select BCR-related genes and constructed a BCR-related gene risk score (RS) model. We tested the power of our model using Kaplan-Meier (K-M) plots and receiver operator characteristic (ROC) curves. We performed univariate and multivariate analyses of RS using other clinicopathological features and established a nomogram model, which has stronger prediction ability. We used GSE70770 and DFKZ 2018 datasets to validate the results. Finally, we performed differential expression and quantitative real-time polymerase chain reaction analyses of the UCSC data for further verification of the findings.

RESULTS

A total of 194 core enriched genes were obtained through GSEA, among which 16 BCR-related genes were selected and a three-gene RS model based on the expression levels of CA14, LRAT, and MGAT5B was constructed. The outcomes of the K-M plots and ROC curves verified the accuracy of the RS model. We identified the Gleason score, pathologic T stage, and RS model as independent predictors through univariate and multivariate Cox analyses and constructed a nomogram model that presented better predictability than the RS model. The outcomes of the validation set were consistent with those of the training set. Finally, the results of differential expression analyses support the effectiveness of our model.

CONCLUSION

We constructed an RS model based on metabolic genes that could predict the prognosis of PCa patients. The model can be easily used in clinical applications and provide important insights into future research on the underlying mechanism of PCa.

Metabolic syndrome related gene signature predicts the prognosis of patients with pancreatic ductal carcinoma. A novel link between metabolic dysregulation and pancreatic ductal carcinoma

Background

Pancreatic cancer is one of the most common malignancies worldwide. In recent years, specific metabolic activities, which involves the development of tumor, caused wide public concern. In this study, we wish to explore the correlation between metabolism and progression of tumor.

Methods

A retrospective analysis including 95 patients with pancreatic ductal adenocarcinoma (PDAC) and PDAC patients from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and The Gene Expression Omnibus (GEO) database were involved in our study. Multivariate Cox regression analysis was used to construct the prognosis model. The potential connection between metabolism and immunity of PDAC was investigated through a weighted gene co-expression network analysis (WGCNA). 22 types of Tumor-infiltrating immune cells (TIICs) between high-risk and low-risk groups were estimated through CIBERSORT. Moreover, the potential immune-related signaling pathways between high-risk and low-risk groups were explored through the gene set enrichment analysis (GSEA). The role of key gene GMPS in developing pancreatic tumor was further investigated through CCK-8, colony-information, and Transwell.

Results

The prognostic value of the MetS factors was analyzed using the Cox regression model, and a clinical MetS-based nomogram was established. Then, we established a metabolism-related signature to predict the prognosis of PDAC patients based on the TCGA databases and was validated in the ICGC database and the GEO database to find the distinct molecular mechanism of MetS genes in PDAC. The result of WGCNA showed that the blue module was associated with risk score, and genes in the blue module were found to be enriched in the immune-related signaling pathway. Furthermore, the result of CIBERSORT demonstrated that proportions of T cells CD8, T cells Regulatory, Tregs NK cells Activated, Dendritic cells Activated, and Mast cells Resting were different between high-risk and low-risk groups. These differences are potential causes of different prognoses of PDAC patients. GSEA and the protein–protein interaction network (PPI) further revealed that our metabolism-related signature was significantly enriched in immune‐related biological processes. Moreover, knockdown of GMPS in PDAC cells suppressed proliferation, migration, and invasion of tumor cells, whereas overexpression of GMPS performed oppositely.

Conclusion

The results shine light on fundamental mechanisms of metabolic genes on PDAC and establish a reliable and referable signature to evaluate the prognosis of PDAC. GMPS was identified as a potential candidate oncogene with in PDAC, which can be a novel biomarker and therapeutic target for PDAC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02378-w.

Identification of prognostic metabolic genes in adrenocortical carcinoma and establishment of a prognostic nomogram: A bioinformatic study

ABSTRACT

Adrenocortical carcinoma is an invasive malignancy with poor prognosis, high recurrence rate and limited therapeutic options. Therefore, it is necessary to establish an effective method to diagnose and evaluate the prognosis of patients, so as to realize individualized treatment and improve their survival rate.This study investigated metabolic genes that may be potential therapeutic targets for Adrenocortical carcinoma (ACC). Level 3 gene expression data from the ACC cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. To verify, other ACC datasets (GSE76021, GSE19750) were downloaded from the Gene Expression Omnibus (GEO) database. The ACC datasets from TCGA and GEO were used to screen metabolic genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping metabolic genes of the 2 datasets were identified.A signature of five metabolic genes (CYP11B1, GSTM2, IRF9, RPL31, and UBE2C) was identified in patients with ACC. The signature could be used to divide the patients with ACC into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of ACC. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model.The results indicated that the signature of 5 metabolic genes had excellent predictive value for ACC. These findings might help improve personalized treatment and medical decisions.

Tumor-Infiltrating Immune-Related Long Non-Coding RNAs Indicate Prognoses and Response to PD-1 Blockade in Head and Neck Squamous Cell Carcinoma

Long non-coding RNAs (lncRNAs) in immune cells play critical roles in tumor cell–immune cell interactions. This study aimed to characterize the landscape of tumor-infiltrating immune-related lncRNAs (Ti-lncRNAs) and reveal their correlations with prognoses and immunotherapy response in head and neck squamous cell carcinoma (HNSCC). We developed a computational model to identify Ti-lncRNAs in HNSCC and analyzed their associations with clinicopathological features, molecular alterations, and immunotherapy response. A signature of nine Ti-lncRNAs demonstrated an independent prognostic factor for both overall survival and disease-free survival among the cohorts from Fudan University Shanghai Cancer Center, The Cancer Genome Atlas, GSE41613, and GSE42743. The Ti-lncRNA signature scores in immune cells showed significant associations with TP53 mutation, CDKN2A mutation, and hypoxia. Inferior signature scores were enriched in patients with high levels of PDCD1 and CTLA4 and high expanded immune gene signature (IGS) scores, who displayed good response to PD-1 blockade in HNSCC. Consistently, superior clinical response emerged in melanoma patients with low signature scores undergoing anti-PD-1 therapy. Moreover, the Ti-lncRNA signature was a prognostic factor independent of PDCD1, CTLA4, and the expanded IGS score. In conclusion, tumor-infiltrating immune profiling identified a prognostic Ti-lncRNA signature indicative of clinical response to PD-1 blockade in HNSCC.

A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

Background and aim

Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC).

Methods

The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression.

Results

A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone.

Conclusions

This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08902-5.

A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.

Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Antitumor Immunosuppression of EGFR Wild Type Lung Cancers With Low PD-L1 Expression

Purpose

With the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), non-small cell lung cancer (NSCLC) patients have achieved remarkable survival benefits in recent years. However, epidermal growth factor receptor (EGFR) wild-type and low expression of programmed death-ligand 1 (PD-L1) NSCLCs remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a metabolic gene signature that could successfully identify high-risk patients and reveal its underlying molecular immunology characteristics.

Methods

By identifying the bottom 50% PD-L1 expression level as PD-L1 low expression and removing EGFR mutant samples, a total of 640 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) tumor samples and 93 adjacent non-tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). We identified differentially expressed metabolic genes (DEMGs) by R package limma and the prognostic genes by Univariate Cox proportional hazards regression analyses. The intersect genes between DEMGs and prognostic genes were put into the least absolute shrinkage and selection operator (LASSO) penalty Cox regression analysis. The metabolic gene signature contained 18 metabolic genes generated and successfully stratified LUAD and LUSC patients into the high-risk and low-risk groups, which was also validated by the Gene Expression Omnibus (GEO) database. Its accuracy was proved by the time-dependent Receiver Operating Characteristic (ROC) curve, Principal Components Analysis (PCA), and nomogram. Furthermore, the Single-sample Gene Set Enrichment Analysis (ssGSEA) and diverse acknowledged methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS, and CIBERSORT revealed its underlying antitumor immunosuppressive status. Besides, its relationship with somatic copy number alterations (SCNAs) and tumor mutational burden (TMB) was also discussed.

Results

It is noteworthy that metabolism reprogramming is associated with the survival of the double-negative LUAD and LUSC patients. The SCNAs and TMB of critical metabolic genes can inhibit the antitumor immune process, which might be a promising therapeutic target.

Enhancement of nanoparticle-mediated double suicide gene expression driven by 'E9-hTERT promoter' switch in dedifferentiated thyroid cancer cells

Differentiated thyroid cancer (DTC), such as papillary thyroid cancer, has a good prognosis after routine treatment. However, in the course of treatment, 5% to 20% of cases may dedifferentiate and can be transformed into dedifferentiated DTC (deDTC) or anaplastic thyroid cancer, leading to treatment failure. To date, several drugs have been used effectively for dedifferentiated thyroid cancer, whereas gene therapy may be a potential method. Literature reported that double suicide genes driven by human telomerase reverse transcriptase promoter (hTERTp) can specifically express in cancer cells and kill them. However, the weak activity of hTERTp limits its further research. To overcome this weakness, we constructed a novel chitosan nanocarrier containing double suicide genes driven by a ‘gene switch’ (a cascade of radiation enhancer E9 and a hTERTp). The vector was labeled with iodine-131 (¹³¹I). On one hand, E9 can significantly enhance the activity of hTERTp under the weak radiation of ¹³¹I, thereby increasing the expression of double suicide genes in deDTC cells. On the other hand, ¹³¹I also plays a certain killing role when it enters host cells. The proposed nanocarrier has good specificity for deDTC cells and thus deserves further study.

Favorable function of Ectonucleoside triphosphate diphosphohydrolase 1 high expression in thyroid carcinoma

BACKGROUND

Ectonucleoside triphosphate diphosphohydrolase 1 (ENTPD1) has been proved to play a vital role in human cancers. Nevertheless, the exact role of ENTPD1 in thyroid carcinoma (THCA) remained unclear. This study aimed to evaluate its prognostic value and reveal the potential regulatory mechanism in THCA.

RESULTS

(1) Higher expression of ENTPD1 was found in THCA tissue compared with normal tissue (all P < 0.05). ENTPD1 expression was associated with age, sub-type and clinical stage of THCA patients (all P < 0.05). Immunohistochemistry showed its higher expression in patients with early stage. (2) ENTPD1 high expression was associated with favorable overall survival of THCA patients (all P < 0.05), especially for male patients and those with advanced stage, B-cells and Natural killer T-cells decreased (all P < 0.05). (3) Pathway analysis indicated that ENTPD1 mainly participated in metabolic process and negatively regulated metabolism-related pathway such as butanoate metabolism, pyruvate metabolism and fatty acid metabolism ((all P < 0.05). (4) ENTPD1 appeared genetic alteration in THCA, and the main mutation type of ENTPD1 was missense substitution (15.89%). (5) A weak correlation between ENTPD1 expression and methylation was found (P < 0.001). Methylation of ENTPD1 in THCA was lower than in normal group (P < 0.001), but it did not correlate with any clinical phenotypes of THCA patients.

CONCLUSIONS

ENTPD1 was highly expressed in THCA, and ENTPD1 high expression contributed to the prognosis of THCA patients. The present study inferred that ENTPD1 might serve as a metabolism-related gene and play a critical role in THCA through regulating metabolic pathways.

A 4 Gene-based Immune Signature Predicts Dedifferentiation and Immune Exhaustion in Thyroid Cancer

CONTEXT

The role of immune-related genes (IRGs) in thyroid cancer dedifferentiation and accompanying immune exhaustion remains largely unexplored.

OBJECTIVE

To construct a significant IRG-based signature indicative of dedifferentiation and immune exhaustion in thyroid cancer.

DESIGN AND SETTINGS

One exploratory cohort and 2 validation cohorts were used to identify stably dysregulated IRGs in dedifferentiated thyroid cancer (DDTC) and to obtain independent risk factors for dedifferentiation. The IRGs formed a gene signature, whose predictive value was tested by the receiver operating characteristic curve. Correlations between the signature and differentiation-related genes, immune checkpoints, and prognosis were analyzed. Gene set enrichment analyses were performed to identify related signaling pathways.

RESULTS

Four IRGs (PRKCQ, PLAUR, PSMD2, and BMP7) were found to be repeatedly dysregulated in DDTC, and they formed an IRG-based signature with a satisfactory predictive value for thyroid cancer dedifferentiation. Correlation analyses revealed that immune checkpoints were closely related to the 4 IRGs and the IRG-based signature, which was significantly associated with the histological subtype (P = 0.026), lymph node metastasis (P = 0.001), and BRAFV600E mutation (P < 0.001). The downregulated expression of PRKCQ shortened the disease-free survival for patients with thyroid cancer. Furthermore, we identified several signaling pathways inherently associated with the IRG-based signature.

CONCLUSIONS

This study suggests that IRGs participate in the dedifferentiation and immune exhaustion process of thyroid cancer and are potential biomarkers for DDTC.

Identification of Key Functional Gene Signatures Indicative of Dedifferentiation in Papillary Thyroid Cancer

Background: Differentiated thyroid cancer (DTC) is the most common type of thyroid cancer. Many of them can relapse to dedifferentiated thyroid cancer (DDTC) and exhibit different gene expression profiles. The underlying mechanism of dedifferentiation and the involved genes or pathways remained to be investigated.

Methods: A discovery cohort obtained from patients who received surgical resection in the Fudan University Shanghai Cancer Center (FUSCC) and two validation cohorts derived from Gene Expression Omnibus (GEO) database were used to screen out differentially expressed genes in the dedifferentiation process. Weighted gene co-expression network analysis (WGCNA) was constructed to identify modules highly related to differentiation. Gene Set Enrichment Analysis (GSEA) was used to identify pathways related to differentiation, and all differentially expressed genes were grouped by function based on the GSEA and literature reviewing data. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to control the number of variables in each group. Next, we used logistic regression to build a gene signature in each group to indicate differentiation status, and we computed receiver operating characteristic (ROC) curve to evaluate the indicative performance of each signature.

Results: A total of 307 upregulated and 313 downregulated genes in poorly differentiated thyroid cancer (PDTC) compared with papillary thyroid cancer (PTC) and normal thyroid (NT) were screened out in FUSCC cohort and validated in two GEO cohorts. WGCNA of 620 differential genes yielded the seven core genes with the highest correlation with thyroid differentiation score (TDS). Furthermore, 395 genes significantly correlated with TDS in univariate logistic regression analysis were divided into 11 groups. The areas under the ROC curve (AUCs) of the gene signature of group transcription and epigenetic modification, signal and substance transport, extracellular matrix (ECM), and metabolism in the training set [The Cancer Genome Atlas (TCGA) cohort] and validation set (combined GEO cohort) were both >0.75. The gene signature based on group transcription and epigenetic modification, cilia formation and movement, and proliferation can reflect the patient's disease recurrence state.

Conclusion: The dedifferentiation of DTC is affected by a variety of mechanisms including many genes. The gene signature of group transcription and epigenetic modification, signal and substance transport, ECM, and metabolism can be used as biomarkers for DDTC.

Identification of an immune-related signature indicating the dedifferentiation of thyroid cells

Background

Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells.

Methods

We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan–Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy.

Results

Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores.

Conclusion

Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01939-3.

Cancer-Associated Fibroblasts Positively Correlate with Dedifferentiation and Aggressiveness of Thyroid Cancer

Background and Objectives

Targeting cancer-associated fibroblast (CAF) is being explored as an approach to improve cancer therapies. The roles of CAF remain unclarified in malignant transformation of papillary thyroid cancer (PTC) into dedifferentiated thyroid cancer (DDTC). This study aimed to investigate correlations of CAF with dedifferentiation and clinicopathological characteristics of thyroid cancer.

Materials and Methods

We applied three different mRNA-based CAF gene signatures to quantify CAF in our cohort, the Gene Expression Omnibus (GEO) cohort and The Cancer Genome Atlas (TCGA) cohort, and analyzed expression of α-SMA by immunohistochemistry in thyroid cancer. The CAF score was analyzed for its associations with clinicopathological characteristics, genetic mutations, tumor-associated signaling pathways and immune landscape.

Results

The CAF score increased significantly in DDTCs compared with normal thyroid tissues and PTCs, and the α-SMA-positive CAFs were found enriched in DDTCs. The high CAF score showed a significant correlation with the anaplastic phenotype in DDTC and low thyroid differentiation score in PTC. Patients with a high CAF score remarkably increased the risk of aggressive outcomes in both DDTC and PTC. Furthermore, the CAF score was positively correlated with genetic mutations, oncogenic signaling pathways, the immune score and increased expression of tumor microenvironment (TME) target markers.

Conclusion

Our findings suggest CAFs positively correlate with dedifferentiation and aggressive outcomes of thyroid cancer, and targeting CAFs as a therapeutic approach may benefit DDTC patients.

A Novel Immune-Related Prognostic Signature for Thyroid Carcinoma

Background:

Recent studies have confirmed that immune-associated genes perform a crucial function in recurrence and metastasis of thyroid carcinoma. A reliable immune-related prognostic signature for patients with thyroid cancer is needed. This study constructed a novel immune-related prognostic signature for thyroid cancer and evaluated its prognostic value by bioinformatics analysis.

Methods:

In this study, we anatomized differentially expressed immune-associated genes from The Cancer Genome Atlas database. The samples from The Cancer Genome Atlas database were randomly divided into training set and test set. A novel immune-related prognostic signature for thyroid cancer was developed by least absolute shrinkage and selection operator and Cox regression analysis: Risk score = (0.6846 × expression value of C-X-C motif chemokine ligand 5 [CXCL5]) + (1.1556 × expression value of Azurocidin 1 [AZU1]) + (−0.3156 × expression value of nucleotide binding oligomerization domain containing 1 [NOD1] + (0.0542 × expression value of TNF Receptor Superfamily Member 11b [TNFRSF11B]) + (0.0952 × expression value of VGF nerve growth factor inducible [VGF]). The established prognostic signature was evaluated based on training set and test set by survival curves, receiver–operator characteristic curves, risk score, survival status, heatmap, and independent prognostic analysis. Meanwhile, we appraised the correlation between target immune-associated genes and clinical stage, tumor-infiltrating immune cells respectively.

Results:

Five immune-associated genes were used for constructing an immune-related prognostic signature by least absolute shrinkage and selection operator, univariate, and multivariate analysis. Survival curves, receiver–operator characteristic curves, and independent prognostic analysis showed the signature had significant prediction value. Clinical and immune cell correlation analyses indicated that target immune-associated genes may participate in tumor immune infiltration and tumor progression.

Conclusions:

We constructed a novel 5 immune-associated genes signature for predicting the prognosis of patients with thyroid cancer, which may help clinical workers evaluate individualized therapy and prognosis.

Development and validation of a novel metabolic signature for predicting prognosis in patients with laryngeal cancer

PURPOSE

Despite advances in the development of treatments for laryngeal cancer (LC), including surgical treatments and radio-chemotherapy, the survival rate of LC remains low. Therefore, novel metabolic signatures are urgently needed to evaluate the prognosis of LC patients.

METHODS

Differentially expressed metabolic genes were extracted via bioinformatics analysis from the raw data of The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and LASSO analyses were performed to identify metabolic genes that were significantly correlated with overall survival (OS). Using the Kaplan-Meier analysis and receiver operating characteristics, the prognostic power of candidate signatures was evaluated in the two databases. Gene Set Enrichment Analysis (GSEA) was performed to explore significant signaling pathways and underlying mechanisms in the high- and low-risk groups.

RESULTS

Thirteen metabolism genes showed superior ability to predict OS for LC when compared to clinical variables, and patients in the high-risk group showed significantly poorer OS than those in the low-risk group. The area under the curve of receiver operating curves for 5- and 3-year OS was 0.929 and 0.899, respectively, which were better than the OS obtained with clinicopathological variables. Similar results obtained in the GEO cohort indicated that this gene signature could differentiate between LC patients with and without recurrence.

CONCLUSION

To our knowledge, this study is the first to report that the 13 metabolic genes could serve as an independent biomarker for LC, which could provide vital prognostic information and prediction for personalized treatment of LC.

A new metabolic signature contributes to disease progression and predicts worse survival in melanoma

Metabolic reprogramming is a common hallmark of tumor cells and is a crucial mediator of resistance toward anticancer therapies. The pattern of a metabolism-related signature in melanoma remains unknown. Here, we explored the role of a multi-metabolism-related gene signature in melanoma.We used the training and validation sets to develop a multi-metabolism-related gene signature. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for constructing a model. The predictive role of the metabolic signature with clinicopathological features of melanoma was also analyzed. Functional analysis of this metabolic signature was also investigated.A ten metabolism-related gene signature was identified and can stratify melanoma into high- and low- risk groups. The signature was correlated with progressive T stage, Breslow thickness, Clark level, and worse survival (all Ps< 0.01). This metabolic signature was shown as an independent prognostic factor and was also a predictive indicator for worse survival in various clinical and molecular features of melanoma. Furthermore, the metabolic signature was implicated in immune responses such as the regulation of T cell activation and cytokine activity. The metabolic signaturewas associated with the progression and worse survival of melanoma. Our study offered a valuable metabolism-targeted therapy approach for melanoma.

Mitotically associated long non-coding RNA is a tumor promoter in anaplastic thyroid cancer

Background

Patients with anaplastic thyroid cancer (ATC), which is among the deadliest of all cancers, often have a poor response to traditional therapies. Currently, the role of long non-coding RNAs (lncRNAs) in ATC carcinogenesis is unclear. In this study, we analyzed the lncRNA expression profile of ATC with the aim of identifying potential molecular targets for treatment of the disease.

Methods

Whole transcriptome sequencing of three ATC and two normal thyroid (NT) samples was performed, and the lncRNA expression profile of ATC was analyzed. Original data as well as datasets deposited in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were used for clinical validation. Cell proliferation, Transwell, and apoptosis assays were performed using ATC cell lines. Gene Ontology (GO) and gene set enrichment analyses (GSEA) were performed to determine the dysregulated pathways.

Results

Whole transcriptome sequencing revealed 182 lncRNAs to be differentially expressed in ATC. One of the lncRNAs, mitotically associated long non-coding RNA (MANCR; LINC00704), was significantly overexpressed in ATC cell lines and patient samples compared with NT and papillary thyroid cancer (PTC). MANCR depletion in ATC cells significantly inhibited cancer cell proliferation and invasion, and induced apoptosis. By further analyzing the transcriptome data, we identified 451 genes co-expressed with MANCR. GO and GSEA showed that the top dysregulated pathways were related to mitosis and cell cycle.

Conclusions

MANCR is a tumor promoter in ATC, and its role in carcinogenesis is possibly associated with cell cycle regulation. Because MANCR expression is minimal in most normal tissues, it may serve as a potential target in the future treatment of ATC.

Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients

BACKGROUND

Metabolic abnormalities have recently been widely studied in various cancer types. This study aims to explore the expression profiles of metabolism-related genes (MRGs) in endometrial cancer (EC).

METHODS

We analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated with EC patient prognosis. Functional pathway enrichment analysis of the DE-MRGs was performed. LASSO and Cox regression analyses were performed to select MRGs closely related to EC patient outcomes. A prognostic signature was developed, and the efficacy was validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability.

RESULTS

Forty-seven DE-MRGs were significantly correlated with EC patient prognosis. Functional enrichment analysis showed that these MRGs were highly enriched in amino acid, glycolysis, and glycerophospholipid metabolism. Nine MRGs were found to be closely related to EC patient outcomes: CYP4F3, CEL, GPAT3, LYPLA2, HNMT, PHGDH, CKM, UCK2 and ACACB. Based on these nine DE-MRGs, we developed a prognostic signature, and its efficacy in part of and the entire TCGA EC cohort was validated. The nine-MRG signature was independent of other clinical features, and could effectively distinguish high- and low-risk EC patients and predict patient OS. The nomogram showed excellent consistency between the predictions and actual survival observations.

CONCLUSIONS

The MRG prognostic model and the comprehensive nomogram could guide precise outcome prediction and rational therapy selection in clinical practice.

Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma

Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified.

Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC.

Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue.

Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.

Development and validation of a metabolic gene signature for predicting overall survival in patients with colon cancer

The reprogramming of cellular metabolism is a hallmark of tumorigenesis. However, the prognostic value of metabolism-related genes in colon cancer remains unclear. This study aimed to identify a metabolic gene signature to categorize colon cancer patients into high- and low-risk groups and predict prognosis. Samples from the Gene Expression Omnibus database were used as the training cohort, while samples from The Cancer Genome Atlas database were used as the validation cohort. A metabolic gene signature was established to investigate a robust risk stratification for colon cancer. Subsequently, a prognostic nomogram was established combining the metabolism-related risk score and clinicopathological characteristics of patients. A total of 351 differentially expressed metabolism-related genes were identified in colon cancer. After univariate analysis and least absolute shrinkage and selection operator-penalized regression analysis, an eight-gene metabolic signature (MTR, NANS, HADH, IMPA2, AGPAT1, GGT5, CYP2J2, and ASL) was identified to classify patients into high- and low-risk groups. High-risk patients had significantly shorter overall survival than low-risk patients in both the training and validation cohorts. A high-risk score was positively correlated with proximal colon cancer (P = 0.012), BRAF mutation (P = 0.049), and advanced stage (P = 0.027). We established a prognostic nomogram based on metabolism-related gene risk score and clinicopathologic factors. The areas under the curve and calibration curves indicated that the established nomogram showed a good accuracy of prediction. We have established a novel metabolic gene signature that could predict overall survival in colon cancer patients and serve as a biomarker for colon cancer.

Thyroid cancer: incidence and mortality trends in China, 2005-2015

PURPOSE

Understanding secular trends of thyroid cancer is critical to plan strategies for cancer prevention and control. Our aim was to estimate the incidence and mortality trends of thyroid cancer in China during 2005-2015.

METHODS

A retrospective cohort evaluation of thyroid cancer cases and deaths during 2005-2015 was performed using population-based data from the Chinese Cancer Registry Annual Report. The incidence and mortality rates of thyroid cancer were stratified by gender, age group (0, 1-4, 5-9, 10-14…80-84, 85-), and area (urban or rural). A Joinpoint regression model was used to examine secular trends.

RESULTS

In China, the age-standardized incidence was 3.21/10⁵ in 2005, and increased to 9.61/10⁵ in 2015. Besides, a significant increase incidence rate was observed with the average annual percent change (AAPC) of 12.4% (95% CI: 10.5%-14.4%) in the period 2005-2015. The age-standardized mortality was 0.30/10⁵ in 2005 and 0.35/10⁵ in 2015, and the AAPC was 2.9% (95% CI: 1.3%-4.5%). For both incidence and mortality, the rates of thyroid cancer were much higher in females than in males, and in urban areas rather than rural areas; however, the rates of increasing trends showed no significant differences. With respect to the highest age-specific rates, it appeared in the age group of 50-54 years old for incidence and in the age group of 80-84 years old for mortality. Notably, the rate of increasing incidence trend was lower in older age groups, especially for people aged 70-79 years old.

CONCLUSION

A rapid increase in incidence and a moderate increase in mortality of thyroid cancer were observed from 2005 to 2015 in our study. Effective measures and tailored programs should be taken to curb the growth trend and reduce the disease burden.

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Changes in energy metabolism may be potential biomarkers and therapeutic targets for cancer as they frequently occur within cancer cells. However, basic cancer research has failed to reach a consistent conclusion on the function(s) of mitochondria in energy metabolism. The significance of energy metabolism in the prognosis of ovarian cancer remains unclear; thus, there remains an urgent need to systematically analyze the characteristics and clinical value of energy metabolism in ovarian cancer. Based on gene expression patterns, the present study aimed to analyze energy metabolism-associated characteristics to evaluate the prognosis of patients with ovarian cancer. A total of 39 energy metabolism-related genes significantly associated with prognosis were obtained, and three molecular subtypes were identified by nonnegative matrix factorization clustering, among which the C1 subtype was associated with poor clinical outcomes of ovarian cancer. The immune response was enhanced in the tumor microenvironment. A total of 888 differentially expressed genes were identified in C1 compared with the other subtypes, and the results of the pathway enrichment analysis demonstrated that they were enriched in the ‘PI3K-Akt signaling pathway’, ‘cAMP signaling pathway’, ‘ECM-receptor interaction’ and other pathways associated with the development and progression of tumors. Finally, eight characteristic genes (tolloid-like 1 gene, type XVI collagen, prostaglandin F2α, cartilage intermediate layer protein 2, kinesin family member 26b, interferon inducible protein 27, growth arrest-specific gene 1 and chemokine receptor 7) were obtained through LASSO feature selection; and a number of them have been demonstrated to be associated with ovarian cancer progression. In addition, Cox regression analysis was performed to establish an 8-gene signature, which was determined to be an independent prognostic factor for patients with ovarian cancer and could stratify sample risk in the training, test and external validation datasets (P<0.01; AUC >0.8). Gene Set Enrichment Analysis results revealed that the 8-gene signature was involved in important biological processes and pathways of ovarian cancer. In conclusion, the present study established an 8-gene signature associated with metabolic genes, which may provide new insights into the effects of energy metabolism on ovarian cancer. The 8-gene signature may serve as an independent prognostic factor for ovarian cancer patients.

Gene Expression Patterns Unveil New Insights in Papillary Thyroid Cancer

Background and objectives: Papillary thyroid carcinoma is the most frequent variety of all malignant endocrine tumors. It represents a heterogeneous malignancy with various clinical outcomes, emphasizing the need to identify powerful biomarkers with clinical relevance. Materials and Methods: Available gene expression data (level 3) for thyroid cancers were downloaded from the Cancer Genome Atlas (TCGA), followed by bioinformatic analyses performed on the data set. Results: Based on gene expression analysis, we were able to identify common and specific gene signatures for the three main types of papillary thyroid carcinoma (classical, follicular variant, and tall-cell). The survival rate was not significantly different among the main subtypes, but we were able to identify a biological adhesion signature with impact in patient prognostic. Conclusions: Taken together, the gene expression signature and particular adhesion signature, along with ITGA10 and MSLN in particular, could be used as a prognostic tool with important clinical relevance.