Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What’s more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.</jats:p

Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma

Abstract Background Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5‐year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA‐seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice. Methods We studied single‐cell RNA‐seq dataset and The Cancer Genome Atlas‐glioblastoma multiforme (TCGA‐GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model. Results We identified 2128 DEGs from the single‐cell RNA‐seq dataset and 6461 DEGs from the bulk RNA‐seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low‐risk group showed better OS than those in the high‐risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients. Conclusion We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA‐seq dataset and a bulk RNA‐seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients

Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What's more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs

A Five-LLPS Gene Risk Score Prognostic Signature Predicts Survival in Hepatocellular Carcinoma

Background. Primary liver cancer, dominated by hepatocellular carcinoma (HCC), is one of the most common cancer types and the third leading cause of cancer death in 2020. Previous studies have shown that liquid–liquid phase separation (LLPS) plays an important role in the occurrence and development of cancer including HCC, but its influence on the patient prognosis is still unknown. It is necessary to explore the effect of LLPS genes on prognosis to accurately forecast the prognosis of HCC patients and identify relevant targeted therapeutic sites. Methods. Using The Cancer Genome Atlas dataset and PhaSepDB dataset, we identified LLPS genes linked to the overall survival (OS) of HCC patients. We applied Least Absolute Shrinkage and Selection Operator (LASSO) Cox penalized regression analysis to choose the best genes for the risk score prognostic signature. We then analysed the validation dataset and evaluated the effectiveness of the risk score prognostic signature. Finally, we performed quantitative real-time PCR experiments to validate the genes in the prognostic signature. Results. We identified 43 differentially expressed LLPS genes that were associated with the OS of HCC patients. Five of these genes (BMX, FYN, KPNA2, PFKFB4, and SPP1) were selected to generate a prognostic risk score signature. Patients in the low-risk group were associated with better OS than those in the high-risk group in both the training dataset and the validation dataset. We found that BMX and FYN had lower expression levels in HCC tumour tissues, whereas KPNA2, PFKFB4, and SPP1 had higher expression levels in HCC tumour tissues. The validation demonstrated that the five-LLPS gene risk score signature has the capability of predicting the OS of HCC patients. Conclusion. Our study constructed a five-LLPS gene risk score signature that can be applied as an effective and convenient prognostic tool. These five genes might serve as potential targets for therapy and the treatment of HCC

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

Abstract Background Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.Methods Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. Results We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. Conclusion Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.</jats:p