Long non-coding RNAs (lncRNAs), which are important functional regulators in cancer, have received increased attention in recent years. In this study, next-generation sequencing technology was used ...to identify aberrantly expressed lncRNAs in follicular thyroid carcinoma (FTC). The long non-coding RNA-HLA complex P5 (HCP5) was found to be overexpressed in FTC. The results of the qPCR analysis were consistent with the sequencing results. In addition, functional experiments showed that overexpression of HCP5 can promote the proliferation, migration, invasiveness and angiogenic ability of FTC cells. Furthermore, according to the sequencing results, HCP5 and alpha-2, 6-sialyltransferase 2 (ST6GAL2) were co-expressed in FTC. We hypothesised that ST6GAL2 may be regulated by HCP5, which would in turn mediate the activity of FTC cells. Through qPCR, immunostaining analyses and functional experiments, we determined that the expression of HCP5 was elevated and was correlated with the levels of ST6GAL2 in FTC tissues and cells. Mechanistic experiments showed that HCP5 functions as a competing endogenous RNA (ceRNA) and acts as a sponge for miR-22-3p, miR-186-5p and miR-216a-5p, which activates ST6GAL2. In summary, our study revealed that HCP5 is a tumour regulator in the development of FTC and that it may contribute to improvement of FTC diagnosis and therapy.
Hepatocellular carcinoma (HCC) samples were clustered into three energy metabolism-related molecular subtypes (C1, C2, and C3) with different prognosis using the gene expression data from The Cancer ...Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). HCC energy metabolism-related molecular subtype analysis was conducted based on the 594 energy metabolism genes. Differential expression analysis yielded 576 differentially expressed genes (DEGs) among the three subtypes, which were closely related to HCC progression. Six genes were finally selected from the 576 DEGs through LASSO-Cox regression and used in constructing a six-gene signature-associated prognostic risk model, which was validated using the TCGA internal and three GEO external validation cohorts. The risk model showed that high
ANLN, ENTPD2, TRIP13, PLAC8
, and
G6PD
expression levels were associated with bad prognosis, and high expression of
ADH1C
was associated with a good prognosis. The validation results showed that our risk model had a high distinguishing ability of prognosis in HCC patients. The four enriched pathways of the risk model were obtained by gene set enrichment analysis (GSEA) and found to be associated with the tumorigenesis and development of HCC, including the cell cycle, Wnt signaling pathway, drug metabolism cytochrome P450, and primary bile acid biosynthesis. The risk score calculated from the established risk model in 204 samples and other clinical characteristics were used in building a nomogram with a good prognostic prediction ability (C-index = 0.746, 95% CI = 0.714–0.777). The area under the curves (AUCs) of the nomogram model in 1-, 2-, and 3-years were 0.82, 0.77, and 0.79, respectively. Then, qRT-PCR and immunohistochemistry were used to validate the mRNA expression levels of the six genes, and significant differences in mRNA and gene expression were observed among the tumor and adjacent tissues. Overall, our study divided HCC patients into three energy metabolism-related molecular subtypes with different prognosis. Then, a risk model with a good performance in prognostic prediction was built using the TCGA dataset. This model can be used as an independent prognostic evaluation index for HCC patients.
Background Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Single-cell transcriptome sequencing (scRNA-seq) can provide accurate gene expression data for ...individual cells. In this study, a new prognostic model was constructed by scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data of CRC samples to develop a new understanding of CRC. Methods CRC scRNA-seq data were downloaded from the GSE161277 database, and CRC bulk RNA-seq data were downloaded from the TCGA and GSE17537 databases. The cells were clustered by the FindNeighbors and FindClusters functions in scRNA-seq data. CIBERSORTx was applied to detect the abundance of cell clusters in the bulk RNA-seq expression matrix. WGCNA was performed with the expression profiles to construct the gene coexpression networks of TCGA-CRC. Next, we used a tenfold cross test to construct the model and a nomogram to assess the independence of the model for clinical application. Finally, we examined the expression of the unreported model genes by qPCR and immunohistochemistry. A clone formation assay and orthotopic colorectal tumour model were applied to detect the regulatory roles of unreported model genes. Results A total of 43,851 cells were included after quality control, and 20 cell clusters were classified by the FindCluster () function. We found that the abundances of C1, C2, C4, C5, C15, C16 and C19 were high and the abundances of C7, C10, C11, C13, C14 and C17 were low in CRC tumour tissues. Meanwhile, the results of survival analysis showed that high abundances of C4, C11 and C13 and low abundances of C5 and C14 were associated with better survival. The WGCNA results showed that the red module was most related to the tumour and the C14 cluster, which contains 615 genes. Lasso Cox regression analysis revealed 8 genes (PBXIP1, MPMZ, SCARA3, INA, ILK, MPP2, L1CAM and FLNA), which were chosen to construct a risk model. In the model, the risk score features had the greatest impact on survival prediction, indicating that the 8-gene risk model can better predict prognosis. qPCR and immunohistochemistry analysis showed that the expression levels of MPZ, SCARA3, MPP2 and PBXIP1 were high in CRC tissues. The functional experiment results indicated that MPZ, SCARA3, MPP2 and PBXIP1 could promote the colony formation ability of CRC cells in vitro and tumorigenicity in vivo. Conclusions We constructed a risk model to predict the prognosis of CRC patients based on scRNA-seq and bulk RNA-seq data, which could be used for clinical application. We also identified 4 previously unreported model genes (MPZ, SCARA3, MPP2 and PBXIP1) as novel oncogenes in CRC. These results suggest that this model could potentially be used to evaluate the prognostic risk and provide potential therapeutic targets for CRC patients. Keywords: Colorectal cancer, Single cell, WGCNA, Prognostic model
Numerous studies have implicated autophagy in the pathogenesis of thyroid carcinoma. This investigation aimed to establish an autophagy-related gene model and nomogram that can help predict the ...overall survival (OS) of patients with differentiated thyroid carcinoma (DTHCA).
Clinical characteristics and RNA-seq expression data from TCGA (The Cancer Genome Atlas) were used in the study. We also downloaded autophagy-related genes (ARGs) from the Gene Set Enrichment Analysis website and the Human Autophagy Database. First, we assigned patients into training and testing groups. R software was applied to identify differentially expressed ARGs for further construction of a protein-protein interaction (PPI) network for gene functional analyses. A risk score-based prognostic risk model was subsequently developed using univariate Cox regression and LASSO-penalized Cox regression analyses. The model's performance was verified using Kaplan-Meier (KM) survival analysis and ROC curve. Finally, a nomogram was constructed for clinical application in evaluating the patients with DTHCA. Finally, a 7-gene prognostic risk model was developed based on gene set enrichment analysis.
Overall, we identified 54 differentially expressed ARGs in patients with DTHCA. A new gene risk model based on 7-ARGs (CDKN2A, FGF7, CTSB, HAP1, DAPK2, DNAJB1, and ITPR1) was developed in the training group and validated in the testing group. The predictive accuracy of the model was reflected by the area under the ROC curve (AUC) values. Univariate and multivariate Cox regression analysis indicated that the model could independently predict the prognosis of patients with THCA. The constrained nomogram derived from the risk score and age also showed high prediction accuracy.
Here, we developed a 7-ARG prognostic risk model and nomogram for differentiated thyroid carcinoma patients that can guide clinical decisions and individualized therapy.
Breast cancer (BRCA) has become the most diagnosed cancer worldwide for female and seriously endanger female health. The epithelial-mesenchymal transition (EMT) process is associated with metastasis ...and drug resistance in BRCA patients. However, the prognostic value of EMT-related lncRNA in BRCA still needs to be revealed. The aim of this study is to construct an EMT-related lncRNA (ERL) signature with accuracy predictive ability for the prognosis of BRCA patients.
RNA-seq expression data and Clinical characteristics obtained from the TCGA (The Cancer Genome Atlas) were used in the study. First, we identified the EMT-related lncRNA by the Pearson correlation analysis. An EMT-related lncRNAs prognostic risk signature was constructed using univariate Cox regression and Lasso-penalized Cox regression analyses. The model's performance was validated using Kaplan-Meier (KM) survival analysis, ROC curve and C-index. Finally, a nomogram was constructed for clinical practice in evaluating the patients with BRCA and validated by calibration curve and decision curve analysis (DCA). We also evaluated the drug sensitivity of signature lncRNA and the tumor immune cell infiltration in breast cancer.
We constructed a 10-lncRNA risk score signature based on the lncRNAs associated with the EMT process. We could assign BRCA patients to the high- and low-risk group according to the median risk score. The prognostic risk signature showed excellent accuracy and demonstrated sufficient independence from other clinical characteristics. The immune cell infiltration analysis showed that the prognostic risk signature was related to the infiltration of the immune cell subtype. Drug sensitivity analysis proved ERLs signature could effectively predict the sensitivity of patients to common chemotherapy drugs in BRCA and provide guidance for chemotherapy drugs for high-risk and low-risk patients.
Our ERL signature and nomogram have excellent prognostic value and could become reliable tools for clinical guidance.
ABSTRACTSpatial interaction research is particularly important for geographical analyses, as it plays a crucial role in extracting travel patterns. However, previous studies on spatial interactions ...have not adequately considered regional population variations over time, resulting in insufficiently precise travel predictions. Moreover, the threshold of spatial correlations is difficult to determine. Existing studies have assumed fully connected spatial correlation matrices, which is not realistic. To address these limitations, we proposed the Self-paced Gaussian-Based Graph Convolutional Network (SG-GCN) to automatically estimate the threshold of spatial correlations for travel flow predictions. It incorporates a temporal dimension into spatial relationship matrices to enhance the accuracy of vehicle flow predictions. In particular, Gaussian-based GCN identifies patterns in a time series of regional flows, enabling more precise capturing of spatial relationships while fusing node and edge features. Building on this model, self-paced contrastive learning automatically sets thresholds to determine the presence or absence of spatial relationships. The model's performance was verified through two empirical case studies conducted in New York City, USA, and Ningbo, China, using 2.8 million bicycle-sharing records and 1.25 million taxi trip records, respectively. The proposed model helps delineate mobility patterns in cities of varying scales and with different modes of transportation.
For unilateral papillary thyroid carcinoma (PTC) patients with contralateral benign nodules, optimal treatment decisions are made according to patient preference and the disease's pathological ...features. This study was performed to evaluate the efficacy and complications of hemithyroidectomy with intraoperative radiofrequency ablation (RFA) compared with total thyroidectomy.
Patients with unilateral PTC and cytologically benign contralateral nodules were enrolled from 2014 to 2018. Total thyroidectomy or hemithyroidectomy with intraoperative RFA of the contralateral nodule was offered to patients who had anxiety regarding their disease. The operation-related parameters, transient or permanent nerve injury, hypocalcemia and disease recurrence, were recorded and compared between the two groups.
After propensity score matching, 191 patients who underwent total thyroidectomy and 224 contralateral nodules in 191 patients underwent hemithyroidectomy with intraoperative RFA (HTRFA) were included. The volume reduction ratios of the contralateral nodules were 67.7% at 12 months and 95.8% at 24 months. The total thyroidectomy group reported significantly higher hypocalcemia than HTRFA within one year (7.8% vs. 2.6%, p = 0.022). Supplemental levothyroxine was not required in 28.3% (54/191) of the patients one year after HTRFA. With a median follow-up of 4.1 years, three recurrences (1.6%) were observed in the HTRFA, and no recurrence occurred in the total thyroidectomy group (p = 0.246).
Hemithyroidectomy for unilateral PTC and intraoperative RFA for contralateral nodules were acceptable and effective treatment approaches and did not increase the risk of complications.
Abstract
Background
The prognosis of tumor patients can be assessed by measuring the levels of lncRNAs (long non-coding RNAs), which play a role in controlling the methylation of the RNA. Prognosis ...in individuals with colorectal adenocarcinoma (CRC) is strongly linked to lncRNA expression, making it imperative to find lncRNAs that are associated with RNA methylation with strong prognostic value.
Methods
In this study, by analyzing TCGA dataset, we were able to develop a risk model for lncRNAs that are associated with m5C with prognostic significance by employing LASSO regression and univariate Cox proportional analysis. There were a number of methods employed to ensure the model was accurate, including multivariate and univariate Cox regression analysis, Kaplan analysis, and receiver operating characteristic curve analysis. The principal component analysis, GSEA and GSVA analysis were used for risk model analysis. The CIBERSORT instrument and the TIMER database were used to evaluate the link between the immune cells that infiltrate tumors and the risk model. In vitro experiments were also performed to validate the predicted m5C-related significant lncRNAs.
Results
The m5c regulators were differentially expressed in colorectal cancer and normal tissue. Based on the screening criteria and LASSO regression, 11 m5c-related lncRNAs were identified for developing the prognostic risk model. Multivariate and univariate Cox regression analysis showed the risk score is a crucial prognostic factor in CRC patients. The 1-year, 3-year, and 5-year AUC curves showed the risk score was higher than those identified for other clinicopathological characteristics. A nomogram using the risk score as a quantitative tool was developed for predicting patients' outcomes in clinical settings. In addition, the risk profile of m5C-associated lncRNAs can discriminate between tumor immune cells’ characteristics in CRC. Mutation patterns and chemotherapy were analyzed between high- and low- risk groups of CRC patients. Moreover, TNFRSF10A-AS1 was chosen for the in vitro verification of the m5C-connected lncRNA to demonstrate impressive effects on the proliferation, migration and invasion of CRC cells.
Conclusion
A risk model including the prognostic value of 11 m5C-associated lncRNAs proves to be a useful prognostic tool for CRC and improves the care of patients suffering from CRC based on these findings.
Anaplastic thyroid carcinoma (ATC) is a rare but extremely malignant tumor, with a rapid growth rate and early metastasis thus leading to poor survival of patients. The molecular mechanisms ...underlying these aggressive traits of ATC remain unknown, which impedes the substantial progress in treatment to prolong ATC patient survival.
We applied weighted gene co-expression network analysis (WGCNA) to identify ATC-specific modules. The Metascape web and R package clusterProfiler were employed to perform enrichment analysis. Combined with differentially expressed gene analysis, we screened out the most potential driver genes and validated them using receiver operator characteristic (ROC) analysis, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blotting, immunohistochemistry (IHC), and triple immunofluorescence staining.
A gene expression matrix covering 75 normal samples, 83 papillary thyroid carcinoma (PTC), 26 follicular thyroid carcinoma (FTC), 19 poor-differentiated thyroid carcinoma (PDTC), and 41 ATC tissue samples were integrated, based on which we detected three most potential ATC-specific modules and found that hub genes of these modules were enriched in distinct biological signals. Hub genes in the turquoise module were mainly enriched in mitotic cell cycle, tube morphogenesis, and cell differentiation, hub genes in the magenta module were mainly clustered in the extracellular matrix organization, positive regulation of cell motility, and regulation of Wnt signaling pathway, while hub genes in the blue module primarily participated in the inflammatory response, innate immune response, and adaptive immune response. We showed that 9 top genes, 8 transcription factors (TFs), and 4 immune checkpoint genes (ICGs) were differentially expressed in ATC compared to other thyroid samples and had high diagnostic values for ATC, among which, 9 novel ATC-specific genes (
, and
) were validated with our clinical samples. Furthermore, we illustrated that ADAM12, RNASE2, and HAVCR2 were predominantly present in the cytoplasm.
Our study identified a set of novel ATC-specific genes that were mainly related to cell proliferation, invasion, metastasis, and immunosuppression, which might throw light on molecular mechanisms underlying aggressive phenotypes of ATC and provide promisingly diagnostic biomarkers and therapeutic targets.
Background
5‐Methylcytosine (m5C) methylation is a major epigenetic RNA modification and is closely related to tumorigenesis in various cancers. This study aimed to explore the prognostic value of ...m5C‐related lncRNAs in breast cancer.
Methods
Clinical characteristics and RNA‐seq expression data from TCGA (The Cancer Genome Atlas) were used in the study. First, we performed differentially expressed gene (DEG) analysis and constructed a PPI network for the 12 m5C regulators. Then, we identified the m5C‐related LncRNAs by the “cor. test.” An m5C‐related lncRNA prognostic risk signature was developed using univariate Cox regression and Lasso‐penalized Cox regression analyses. The model's performance was determined using Kaplan–Meier (KM) survival analysis and ROC curves. Finally, a nomogram was constructed for clinical application in evaluating patients with BRCA. We also researched the drug sensitivity of signature lncRNAs and immune cell infiltration. Finally, we validated the expression of the signature lncRNAs through qRT–PCR in a breast cancer cell line and a breast epithelial cell line.
Results
Overall, we constructed an 11‐lncRNA risk score signature based on the lncRNAs associated with m5C regulators. According to the median risk score, we divided BRCA patients into high‐ and low‐risk groups. The prognostic risk signature displayed excellent accuracy and demonstrated sufficient independence from other clinical characteristics. The immune cell infiltration analysis showed that the prognostic risk signature was related to the infiltration of immune cell subtypes. Drug sensitivity proved that our prognostic risk signature potentially has therapeutic value.
Conclusions
The m5C‐related lncRNA signature reliably predicted the prognosis of breast cancer patients and may provide new insight into the breast cancer tumor immune microenvironment.
By RNA‐seq and TCGA, an 11‐lncRNA risk score signature was constructed based on lncRNAs associated with m5C regulators, which could reliably predict the prognosis of breast cancer patients. Immune cell infiltration assays and drug susceptibility assays demonstrate their value for studying the tumor immune microenvironment in breast cancer.
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