Prognostic factor analysis for breast cancer using gene expression profiles

The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data

BACKGROUND

Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer.

METHODS

The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method.

RESULTS

The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients.

CONCLUSIONS

Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.

XGBLC: an improved survival prediction model based on XGBoost

MOTIVATION

Survival analysis using gene expression profiles plays a crucial role in the interpretation of clinical research and assessment of disease therapy programs. Several prediction models have been developed to explore the relationship between patients' covariates and survival. However, the high-dimensional genomic features limit the prediction performance of the survival model. Thus, an accurate and reliable prediction model is necessary for survival analysis using high-dimensional genomic data.

RESULTS

In this study, we proposed an improved survival prediction model based on XGBoost framework called XGBLC, which used Lasso-Cox to enhance the ability to analyze high-dimensional genomic data. The novel first- and second-order gradient statistics of Lasso-Cox were defined to construct the loss function of XGBLC. We extensively tested our XGBLC algorithm on both simulated and real-world datasets, and estimated the performance of models with 5-fold cross-validation. Based on 20 cancer datasets from The Cancer Genome Atlas (TCGA), XGBLC outperforms five state-of-the-art survival methods in terms of C-index, Brier score and AUC. The results show that XGBLC still keeps good accuracy and robustness by comparing the performance on the simulated datasets with different scales. The developed prediction model would be beneficial for physicians to understand the effects of patient's genomic characteristics on survival and make personalized treatment decisions.

AVAILABILITY AND IMPLEMENTATION

The implementation of XGBLC algorithm based on R language is available at: https://github.com/lab319/XGBLC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Controversial roles of cold‑inducible RNA‑binding protein in human cancer (Review)

Cold‑inducible RNA‑binding protein (CIRBP) is a cold‑shock protein comprised of an RNA‑binding motif that is induced by several stressors, such as cold shock, UV radiation, nutrient deprivation, reactive oxygen species and hypoxia. CIRBP can modulate post‑transcriptional regulation of target mRNA, which is required to control DNA repair, circadian rhythms, cell growth, telomere integrity and cardiac physiology. In addition, the crucial function of CIRBP in various human diseases, including cancers and inflammatory disease, has been reported. Although CIRBP is primarily considered to be an oncogene, it may also serve a role in tumor suppression. In the present study, the controversial roles of CIRBP in various human cancers is summarized, with a focus on the interconnectivity between CIRBP and its target mRNAs involved in tumorigenesis. CIRBP may represent an important prognostic marker and therapeutic target for cancer therapy.

Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.

Diagnostic accuracy of MRI textural analysis in the classification of breast tumors

OBJECTIVE

To investigate whether textural analysis (TA) of MRI heterogeneity may play a role in the clinical assessment and classification of breast tumors.

MATERIALS AND METHODS

For this retrospective study, patients with breast masses ≥1 cm on contrast-enhanced MRI were obtained in 69 women (mean age: 51 years; range 21-78 years) with 77 masses (38 benign, 39 malignant) from 2006 to 2018. The selected single slice sagittal peak post-contrast T1-weighted image was analyzed with commercially available TA software [TexRAD Ltd., UK]. Eight histogram TA parameters were evaluated at various spatial scaling factors (SSF) including mean pixel intensity, standard deviation of the pixel histogram (SD), entropy, mean of the positive pixels (MPP), skewness, kurtosis, sigma, and Tx_sigma. Additional statistical tests were used to determine their predictiveness.

RESULTS

Entropy showed a significant difference between benign and malignant tumors at all textural scales (p < 0.0001) and kurtosis was significant at SSF = 0-5 (p = 0.0026-0.0241). The single best predictor was entropy at SSF = 4 with AUC = 0.80, giving a sensitivity of 95% and specificity of 53%. An AUC of 0.91 was found using a model combining entropy with sigma, which yielded better performance with a sensitivity of 92% and specificity of 79%.

CONCLUSION

TA of breast masses has the potential to assist radiologists in categorizing tumors as benign or malignant on MRI. Measurements of entropy, kurtosis, and entropy combined with sigma may provide the best predictability.

The Construction of Bone Metastasis-Specific Prognostic Model and Co-expressed Network of Alternative Splicing in Breast Cancer

Background

Breast cancer (BRCA) ranks among the top most common female malignancies and was regarded as incurable when combined with bone and distant metastasis. Alternative splicing events (ASEs) together with splicing factors (SFs) were considered responsible for the development and progression of tumors.

Methods

Datasets including RNA sequencing and ASEs of BRCA samples were achieved from TCGA and TCGASpliceSeq databases. Then, a survival model was built including 15 overall-survival-associated splicing events (OS-SEs) by Cox regression and Lasso regression. The co-expressed SFs of each bone-and-distant-metastasis-related OS-SE were discovered by Pearson correlation analysis. Additionally, Gene Set Variation Analysis (GSVA) was performed to identify the downstream mechanisms of the key OS-SEs. Finally, the results were validated in different online platforms.

Results

A reliable survival model was established (the area under ROC = 0.856), and CIRBP was found co-expressed with FAM110B (R = 0.320, P < 0.001) associated with the fatty acid metabolism pathway.

Conclusion

Aberrant SF, CIRBP, regulated a specific ASE, exon skip (ES) of FAM110B, during which the fatty acid metabolism pathway played an essential part in tumorigenesis and prognosis of BRCA.

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data

BACKGROUND

Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data.

METHODS

In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell's concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno's concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates.

RESULTS

Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell's concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno's concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively.

CONCLUSION

In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

The impact of the effectiveness of GATA3 as a prognostic factor in breast cancer

The transcription factor GATA3 plays a significant role in mammary gland development and differentiation. We analyzed expression of GATA3 in breast cancer (BC) cell lines and clinical specimens from BC patients in Taiwan. Semiquantitative reverse-transcription polymerase chain reaction (RT-PCR), quantitative real-time PCR were carried out to determine the mRNA level of GATA3 from 241 pairs of matched tumor and adjacent normal tissues from anonymous female donors. GATA3 immunohistochemistry (IHC) staining and H-score were performed (n = 25). Inducing and silencing of GATA3 were done by exposure MCF-7 cell line to nicotine or curcumin, respectively. GATA3 expression was detected in most of the estrogen receptor-positive (ER+) tumor specimens (176/241, 73%) compared with paired normal tissues (65/241, 27%) (P < .001). The GATA3 level was highest in Luminal A, and independent t-tests revealed higher GATA3 was associated with ER+ (P = .018) and BC stages (stage II, and stage IV). Nuclear protein expression of GATA3 was detected in tumor tissues (P < .001) with higher H-score in Luminal A patients (P = .012). Kaplan-Meier survival analyses showed that ER+/progesterone receptor (PgR)+ and lower grade BC patients with relatively high GATA3 had better clinical overall survival (OS). GATA3 regulates ERα and BCL-2 as BC luminal subtype markers. Cox univariate and multivariate analyses demonstrated that the expression of GATA3 was an effective predictor of the risk of death. We demonstrated a correlation between GATA3 expression and only ER+ and suggest that a higher GATA3 expression is a good prognostic factor for OS for ER+ BC patients.