A Five-Gene Prognostic Nomogram Predicting Disease-Free Survival of Differentiated Thyroid Cancer

Analysis of metastasis‑related risk factors and clinical relevance in adult soft‑tissue sarcoma

Metastasis occurs in nearly 50% of cases of adult soft-tissue sarcoma (ASTS), leading to a dismal prognosis, with a 2-year survival rate of ~30%. Consequently, a prognostic model that incorporates metastatic characteristics may be instrumental in predicting survival time and in crafting optimal personalized therapeutic strategies for patients with ASTS. In the present study, a prognostic prediction model for ASTS was developed by examining genes that are differentially expressed between non-metastatic and metastatic patients in the Gene Expression Omnibus dataset. The prognostic model, which includes five featured genes [actin γ2 (ACTG2), apolipoprotein D, coatomer protein complex subunit γ2 imprinted transcript 1, collagen type VI α6 chain and osteomodulin], was further validated in patients with ASTS from the Cancer Genome Atlas dataset. Based on these five-gene signatures, patients were categorized into high- and low-risk groups. Functional and pathway analyses revealed disparities in stemness, extracellular matrix and cell adhesion-related pathways between the two risk groups, particularly noting the activation of the PI3K-Akt pathway in high-risk cases. Analysis of immune infiltration also revealed variations in immune microenvironment changes between the two risk groups. Immunohistochemical staining substantiated the prognostic significance of these gene signatures in a specific sarcoma subtype. Additionally, wound-healing and Transwell assays demonstrated that inhibition of ACTG2 by shRNAs curbed cell migration and invasion in a sarcoma HOS cell line, underscoring its role in sarcoma metastasis. In conclusion, the present study successfully developed and validated a metastasis-based prognosis prediction model. This model not only reliably forecasts the survival of patients with ASTS, but also may pave the way for further investigation into the processes underlying sarcoma metastasis, ultimately aiding in the design of tailored therapeutic regimens.

PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery

BACKGROUND

In the field of biology and medicine, the interpretability and accuracy are both important when designing predictive models. The interpretability of many machine learning models such as neural networks is still a challenge. Recently, many researchers utilized prior information such as biological pathways to develop neural networks-based methods, so as to provide some insights and interpretability for the models. However, the prior biological knowledge may be incomplete and there still exists some unknown information to be explored.

RESULTS

We proposed a novel method, named PathExpSurv, to gain an insight into the black-box model of neural network for cancer survival analysis. We demonstrated that PathExpSurv could not only incorporate the known prior information into the model, but also explore the unknown possible expansion to the existing pathways. We performed downstream analyses based on the expanded pathways and successfully identified some key genes associated with the diseases and original pathways.

CONCLUSIONS

Our proposed PathExpSurv is a novel, effective and interpretable method for survival analysis. It has great utility and value in medical diagnosis and offers a promising framework for biological research.

A risk prediction model mediated by genes of APOD/APOC1/SQLE associates with prognosis in cervical cancer

Cervical cancer is one of the most common gynecological malignancies. Due to the high heterogeneity of cervical cancer accelerating cancer progression, it is necessary to identify new prognostic markers and treatment regimens for cervical cancer to improve patients' survival rates. We purpose to construct and verify a risk prediction model for cervical cancer patients. Based on the analysis of data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), differences of genes in normal and cancer samples were analyzed and then used analysis of WGCNA along with consistent clustering to construct single-factor + multi-factor risk models. After regression analysis, the target genes were obtained as prognostic genes and prognostic risk models were constructed, and the validity of the risk model was confirmed using the receiver operating characteristic curve (ROC) and Kaplan-Meier curve. Subsequently, the above model was verified on the GSE44001 data validation followed by independent prognostic analysis. Enrichment analysis was conducted by grouping the high and low risks of the model. In addition, differences in immune analysis (immune infiltration, immunotherapy), drug sensitivity, and other levels were counted by the high and low risks groups. In our study, three prognostic genes including APOD, APOC1, and SQLE were obtained, and a risk model was constructed along with validation based on the above-mentioned analysis. According to the model, immune correlation and immunotherapy analyses were carried out, which will provide a theoretical basis and reference value for the exploration and treatment of cervical cancer.

Expression of transcription, growth factors, steroid hormone receptors, LC3B in papillary thyroid cancer tissue, association with prognosis and risk of recurrence

Introduction. Biological characteristics of the tumor play a major role in it’s development and progression. Currently, using the molecular markers aimed at resolving the problems in clinical oncology is becoming more important, including thyroid carcinomas. Heterogeneous contradictory data had been accumulated to date showing the ability of tumors genetic and biological parameters to predict the diseases outcome.Aim. To investigate prognostic value of transcription, growth factors, components of AKT / mTOR signaling pathway and autophagy protein LC3B in patients with papillary thyroid cancer in relation to recurrences and overall survival.Materials and methods. The study included 65 patients with T1–4N0–1M0 papillary thyroid cancer. According to the criteria of the American Thyroid Association (ATA) (2015), patients were divided into groups of patients with high, low and intermediate risk. 30 patients were classified as low risk, 23 as intermediate risk, and 12 as high risk. The BRAFV600 mutation was identified in 18 samples. The expression of transcription factors (p65 and p50 subunits of nuclear factor kappa B (NF-κB p65, NF-κB p50), hypoxia-inducible factor 1 (HIF-1), hypoxia-inducible factor 2 (HIF-2), growth factors (vascular endothelial growth factor (VEGF), receptor VEGF (VEGF-2), carbonic anhydrases of type 9 (CAIX)), AKT, c-RAF, GSK- 3β, p70S6, mammalian target of rapamycin (m-TOR), PDK, PTEN, 4E-BP1 in the tumor was assessed by real-time polymerase chain reaction (PCR). The BRAFV600 mutation was investigated using real-time allele-specific PCR. The content of the LC3B protein was examined using the Western Blot method.Results. As a result of the study, there is an increase in c-RAF expression with an increase in risk from low to high, which was accompanied by a decrease in 4E-BP1 expression. c-RAF mRNA levels were increased 3.0- and 2.8‑fold in the intermediate and high-risk groups, respectively, compared to low risk patients. There is a change in the expression of Brn-3α depending on the relapse risk. The maximum mRNA levels were found in patients with intermediate risk, where the figure was 4.3 and 6.2 times higher than in patients with low and high risk, respectively. An increase in LC3B expression by 56.0 and 28.0 times was shown in the tumor tissue of patients with intermediate risk compared with patients with low and high risk. This fact corresponds with an increasing content of the protein itself, which was higher in patients with intermediate risk. Patients with a negative BRAF gene status had an intermediate and high risk of tumor recurrence. The prognostic significance of the estrogen receptor β (ER-β) and NF-κB p50 expression level had been revealed in relation with relapse-free and overall survival of patients with papillary thyroid cancer.Conclusion. As a result of the study, additional molecular markers were found in order to for predict the tumors recurrence risk. The study showed the significance of ERβ and NF-κB p50 expression levels for predicting disease outcomes.

Signaling Pathways Regulating the Expression of the Glioblastoma Invasion Factor TENM1

Glioblastoma (GBM) is one of the most aggressive cancers, with dismal prognosis despite continuous efforts to improve treatment. Poor prognosis is mostly due to the invasive nature of GBM. Thus, most research has focused on studying the molecular players involved in GBM cell migration and invasion of the surrounding parenchyma, trying to identify effective therapeutic targets against this lethal cancer. Our laboratory discovered the implication of TENM1, also known as ODZ1, in GBM cell migration in vitro and in tumor invasion using different in vivo models. Moreover, we investigated the microenvironmental stimuli that promote the expression of TENM1 in GBM cells and found that macrophage-secreted IL-6 and the extracellular matrix component fibronectin upregulated TENM1 through activation of Stat3. We also described that hypoxia, a common feature of GBM tumors, was able to induce TENM1 by both an epigenetic mechanism and a HIF2α-mediated transcriptional pathway. The fact that TENM1 is a convergence point for various cancer-related signaling pathways might give us a new therapeutic opportunity for GBM treatment. Here, we briefly review the findings described so far about the mechanisms that control the expression of the GBM invasion factor TENM1.

A seven-autophagy-related gene signature for predicting the prognosis of differentiated thyroid carcinoma

Background

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).

Methods

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.

Results

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.

Conclusions

Here, we developed a 7-ARG prognostic risk model and nomogram for differentiated thyroid carcinoma patients that can guide clinical decisions and individualized therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02590-6.