OSbrca: A Web Server for Breast Cancer Prognostic Biomarker Investigation With Massive Data From Tens of Cohorts

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Methylation-Mediated Silencing of RBP7 Promotes Breast Cancer Progression through PPAR and PI3K/AKT Pathway

Retinoid-binding protein7 (RBP7) is a member of the cellular retinol-binding protein (CRBP) family, which is involved in the pathogenesis of breast cancer. The study aims to illustrate the prognostic value and the potential regulatory mechanisms of RBP7 expression in breast cancer. Bioinformatics analysis with the TCGA and CPTAC databases revealed that the mRNA and protein expression levels of RBP7 in normal were higher compared to breast cancer tissues. Survival analysis displayed that the lower expression of RBP7, the worse the prognosis in ER-positive (ER+) breast cancer patients. Genomic analysis showed that low expression of RBP7 correlates with its promoter hypermethylation in breast cancer. Functional enrichment analysis demonstrated that downregulation of RBP7 expression may exert its biological influence on breast cancer through the PPAR pathway and the PI3K/AKT pathway. In summary, we identified RBP7 as a novel biomarker that is helpful for the prognosis of ER+ breast cancer patients. Promoter methylation of RBP7 is involved in its gene silencing in breast cancer, thus regulating the occurrence and development of ER+ breast cancer through the PPAR and PI3K/AKT pathways.

High APLN Expression Predicts Poor Prognosis for Glioma Patients

Apelin (APLN) is an endogenous ligand of the G protein-coupled receptor APJ (APLNR). APLN/APLNR system was involved in a variety of pathological and physiological functions, such as tumorigenesis and development. However, its prognostic roles in patients with central nervous system (CNS) cancers remain unknown. The present study was designed to explore the expression profile, prognostic significance, and interaction network of APLN/APLNR by integrating data from Oncomine, GEPIA, LOGpc, STRING, GeneMANIA, and immunohistochemical staining. The results demonstrated that APLN and APLNR mRNA expression were significantly increased in CNS cancers, including both low-grade glioma (LGG) and glioblastoma (GBM), when compared with normal CNS tissues. The high APLN, but not APLNR, expression was significantly correlated with overall survival (OS), recurrence free survival (RFS), and progression free survival (PFS) of LGG patients. However, neither APLN nor APLNR expression was significantly related to prognostic value in terms of OS, disease free interval (DFI), disease specific survival (DSS), or progression free interval (PFI) for GBM patients. Additionally, immunohistochemistry staining confirmed the increased APLN expression in tissues of LGG patients with grade II than grade I. These results showed that an elevated APLN level could predict poor OS, RFS, and PFS for LGG patients, and it could be a promising prognostic biomarker for LGG.

A complex epigenome-splicing crosstalk governs epithelial-to-mesenchymal transition in metastasis and brain development

Epithelial-to-mesenchymal transition (EMT) renders epithelial cells migratory properties. While epigenetic and splicing changes have been implicated in EMT, the mechanisms governing their crosstalk remain poorly understood. Here we discovered that a C2H2 zinc finger protein, ZNF827, is strongly induced during various contexts of EMT, including in brain development and breast cancer metastasis, and is required for the molecular and phenotypic changes underlying EMT in these processes. Mechanistically, ZNF827 mediated these responses by orchestrating a large-scale remodelling of the splicing landscape by recruiting HDAC1 for epigenetic modulation of distinct genomic loci, thereby slowing RNA polymerase II progression and altering the splicing of genes encoding key EMT regulators in cis. Our findings reveal an unprecedented complexity of crosstalk between epigenetic landscape and splicing programme in governing EMT and identify ZNF827 as a master regulator coupling these processes during EMT in brain development and breast cancer metastasis.

Analysis of the Risk Factors for Elevated D-Dimer Level After Breast Cancer Surgery: A Multicenter Study Based on Nursing Follow-Up Data

D-dimer level is often used to assess the severity of trauma as well as the risk of thrombosis. This study investigated the risk factors for high postoperative D-dimer level. This study included a total of 2706 patients undergoing breast cancer surgery to examine the associations between various clinicopathological factors and variation in D-dimer levels. After adjusting for other factors, T stage, neoadjuvant chemotherapy, blood loss, surgery type, diabetes, and elevated leukocyte and neutrophil counts were found to be significant risk factors for D-dimer variation. This study identified several factors associated with elevated D-dimer levels and consequent thrombosis after breast cancer surgery, which may aid in the development of more precise preventive measures and interventions as well as serve as a reference for future research.

Cancer-Testis Antigen LDH-C4 in Tissue, Serum, and Serum-Derived Exosomes Serves as a Promising Biomarker in Lung Adenocarcinoma

Objective

As a cancer-testis antigen (CTA), human lactate dehydrogenase C4 (LDH-C4) enzyme protein encoded by the LDHC gene has been reported to be involved in the occurrence and development of various malignancies, while its expression and clinical significance in lung adenocarcinoma (LUAD) remain unclear. This study aims to investigate the expression of LDH-C4 in LUAD and its diagnostic and prognostic value.

Methods

The mRNA and protein levels of LDH-C4 in LUAD and adjacent normal tissues were analyzed based on the UALCAN database, and the prognostic significance was assessed using the LOGpc database. The LDHC mRNA level in serum and serum secretion of LUAD patients was determined by quantitative real-time PCR (qRT-PCR). Based on the high-throughput LUAD tissue chip combined with immunohistochemistry (IHC), the protein level of LDH-C4 in LUAD tissues was measured, and its correlation with clinicopathological features and prognosis was analyzed.

Results

LDHC expression was upregulated in LUAD, which was related to the clinical stage and poor prognosis of patients. The positive rates of LDHC mRNA expression in serum and exosome of LUAD patients were 78.3% and 66.7%, respectively. The area under the curve (AUC) of serum and exosomal LDHC in the diagnosis of LUAD was 0.8121 and 0.8925, respectively. The expression of LDHC in serum and serum-derived exosomes from LUAD patients was negatively correlated with medical treatment and positively correlated with the recurrence of LUAD. The positive expression rate of LDH-C4 in LUAD tissues was 96.7% (89/92), which was significantly higher than that in adjacent normal tissues 22.6% (19/84) (p < 0.001). The median overall survival (OS) time of patients with a high expression of LDH-C4 was significantly shorter than that of patients with low expression (34 months versus 62 months) (p = 0.016). Further relative risk analysis exhibited that the expression of LDH-C4 was an independent prognostic factor of OS in patients with LUAD.

Conclusions

LDHC/LDH-C4 expression was upregulated in LUAD, and LDH-C4 could be used as a molecular indicator of the prognosis of LUAD. Serum and serum-derived exosomes of LDHC can be used as an important biomarker for the diagnosis, efficacy evaluation, and recurrence monitoring of LUAD.

High Expression of TIMELESS Predicts Poor Prognosis: A Potential Therapeutic Target for Skin Cutaneous Melanoma

Background

Skin cutaneous melanoma (SKCM) is the most lethal skin cancer with an increasing incidence worldwide. The poor prognosis of SKCM urgently requires us to discover prognostic biomarkers for accurate therapy. As a regulator of DNA replication, TIMELESS (TIM) has been found to be highly expressed in various malignancies but rarely reported in SKCM. The objective of this study was to evaluate the relationship between TIM and SKCM tumorigenesis and prognosis.

Methods

We obtained RNA sequencing data from TCGA and GTEx to analyze TIM expression and differentially expressed genes (DEGs). Subsequently, GO/KEGG, GSEA, immune cell infiltration analysis, and protein-protein interaction (PPI) network were used to perform the functional enrichment analysis of TIM-related DEGs. Moreover, the receiver operating characteristic (ROC) curves, Cox regression analysis, Kaplan–Meier (K-M) analysis, and nomograms were applied to figure out the clinical significance of TIM in SKCM. In addition, we investigated the relationship between TIM promoter methylation and SKCM prognosis through the UALCAN database. Finally, the immunohistochemical (IHC) results of normal skin and SKCM were analyzed to determine expression differences.

Results

TIM was significantly elevated in various malignancies, including SKCM, and high expression of TIM was associated with poor prognosis. Moreover, a total of 402 DEGs were identified between the two distinct TIM expression groups, and functional annotation showed enrichment with positive regulation of cell cycle and classic oncogenic pathways in the high TIM expression phenotype, while keratinization pathways were negatively regulated and enriched. Further analysis showed that TIM was correlated with infiltration of multiple immune cells. Finally, IHC validated the differential expression of TIM in SKCM.

Conclusion

TIM might play a pivotal role in tumorigenesis of SKCM and is closely related to its prognosis.

A tumor-specific pro-IL-12 activates preexisting cytotoxic T cells to control established tumors

It is a challenge to effectively reactivate preexisting tumor-infiltrating lymphocytes (TILs) without causing severe toxicity. Interleukin-12 (IL-12) can potently activate lymphocytes, but its clinical use is limited by its short half-life and dose-related toxicity. In this study, we developed a tumor-conditional IL-12 (pro-IL-12), which masked IL-12 with selective extracellular receptor–binding domains of the IL-12 receptor while preferentially and persistently activating TILs after being unmasked by matrix metalloproteinases expressed by tumors. Systemic delivery of pro-IL-12 demonstrated reduced toxicity but better control of established tumors compared with IL-12-Fc. Mechanistically, antitumor responses induced by pro-IL-12 were dependent on TILs and IFNγ. Furthermore, direct binding of IL-12 to IL-12R on CD8+, not CD4+, T cells was essential for maximal effectiveness. Pro-IL-12 improved the efficacy of both immune checkpoint blockade and targeted therapy when used in combination. Therefore, our study demonstrated that pro-IL-12 could rejuvenate TILs, which then combined with current treatment modalities while limiting adverse effects for treating established tumors.

OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer

Simple Summary

The OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction.

Abstract

Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.

Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models

BACKGROUND

The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.

METHODS

Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.

RESULTS

The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations.

CONCLUSIONS

The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.

GeneCoNet: A web application server for constructing cancer patient-specific gene correlation networks with prognostic gene pairs

BACKGROUND AND OBJECTIVE

Most prognostic gene signatures that have been known for cancer are either individual genes or combination of genes. Both individual genes and combination of genes do not provide information on gene-gene relations, and often have less prognostic significance than random genes associated with cell proliferation. Several methods for generating sample-specific gene networks have been proposed, but programs implementing the methods are not publicly available.

METHODS

We have developed a method that builds gene correlation networks specific to individual cancer patients and derives prognostic gene correlations from the networks. A gene correlation network specific to a patient is constructed by identifying gene-gene relations that are significantly different from normal samples. Prognostic gene pairs are obtained by carrying out the Cox proportional hazards regression and the log-rank test for every gene pair.

RESULTS

We built a web application server called GeneCoNet with thousands of tumor samples in TCGA. Given a tumor sample ID of TCGA, GeneCoNet dynamically constructs a gene correlation network specific to the sample as output. As an additional output, it provides information on prognostic gene correlations in the network. GeneCoNet found several prognostic gene correlations for six types of cancer, but there were no prognostic gene pairs common to multiple cancer types.

CONCLUSION

Extensive analysis of patient-specific gene correlation networks suggests that patients with a larger subnetwork of prognostic gene pairs have shorter survival time than the others and that patients with a subnetwork that contains more genes participating in prognostic gene pairs have shorter survival time than the others. GeneCoNet can be used as a valuable resource for generating gene correlation networks specific to individual patients and for identifying prognostic gene correlations. It is freely accessible at http://geneconet.inha.ac.kr.

Expression and clinical implications of basic leucine zipper ATF-like transcription factor 2 in breast cancer

Background

Basic leucine zipper ATF-like transcription factor 2 (BATF2) has been reported to participate in the occurrence and development of some malignancies. Herein, we aimed to explore the expression pattern and clinical implications of BATF2 in breast cancer (BC).

Methods

We assessed the differences in BATF2 mRNA expression between cancerous and noncancerous tissues in BC using GEPIA and UALCAN data and in BATF2 protein expression pattern using Human Protein Atlas (HPA) data. BATF2 co-expression networks were analyzed in Coexpedia. The association between the differentially expressed BATF2 mRNA and BC prognosis was assessed using UALCAN, OSbrca, and GEPIA databases. In external validations, BATF2 protein expression in BC tissues was quantitated using a tissue microarray and immunohistochemistry (IHC) analysis, and BATF2 mRNA expression in serum and serum-derived exosomes of BC patients using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results

No difference in the BATF2 mRNA expression level was found between cancerous and noncancerous tissues in BC based on databases. There were low-to-moderate levels of increases in BATF2 protein expressions in BC cases from the HPA cohort. BATF2 mRNA expression was negatively correlated with androgen receptor (AR) and positively correlated with BRCA2 DNA repair associated (BRCA2), marker of proliferation Ki-67 (Mki67), and tumor protein p53 (TP53) expressions. Generally, BATF2 mRNA exhibited a non-significant association with BC prognosis; yet the subgroup analyses showed that triple-negative breast cancer (TNBC) patients with high BATF2 mRNA expressions had a longer overall survival (OS). Our IHC analysis revealed a positive rate of BATF2 protein expression of 46.90%, mainly located in the nucleus of cancer cells in BC, and the OS of BC patients with high BATF2 protein expressions was prolonged. The positive rates of BATF2 mRNA expressions in the serum and exosomes were 45.00 and 41.67%, respectively. Besides, the AUCs of serum and exosomal BATF2 mRNA for BC diagnosis were 0.8929 and 0.8869, respectively.

Conclusions

BC patients exhibit low-to-moderate expressions in BATF2 mRNA expression levels in cancerous tissues. The high BATF2 protein expression can be a potential indicator of a better BC prognosis. Serum and exosomal BATF2 mRNA levels also serve as promising noninvasive biomarkers for BC diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08785-6.

Computational resources for identification of cancer biomarkers from omics data

Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

NR3C2-Related Transcriptome Profile and Clinical Outcome in Invasive Breast Carcinoma

Background

Increasing evidence has indicated that the nuclear receptor subfamily 3 group C member 2 (NR3C2) may be associated with tumorigenesis and patient prognosis for certain types of tumors. However, the clinical significance of NR3C2 is unclear in invasive breast carcinoma (BRCA).

Methods

We used bioinformatics to broadly investigate and obtain a deeper understanding of the prognostic significance between NR3C2 and BRCA. RNA-sequencing data and clinical information of patients with BRCA from the Cancer Genome Atlas database were collected for subsequent analysis. The diagnostic efficacy of NR3C2 was evaluated by calculating the receiver operating characteristic curve. The prognostic value of NR3C2 was evaluated by Kaplan-Meier analysis and Cox regression analysis for patients with BRCA. Moreover, the OSbrca database was used to validate NR3C2 as a prognostic biomarker for BRCA. Gene set enrichment analysis (GSEA) and tumor immune infiltration analysis were conducted to explore the molecular mechanism of NR3C2 in BRCA.

Results

The expression level of NR3C2 in BRCA tissues decreased compared to that in normal breast tissues (P < 0.001). NR3C2 presented good diagnostic efficacy (AUC = 0.908). Moreover, the expression of NR3C2 was verified using the Oncomine database. High expression of NR3C2 was statistically associated with prolonged overall survival (HR = 0.65, 95% CI: 0.47-0.91, and P = 0.012), progression-free interval (HR = 0.68, 95% CI: 0.49-0.95, and P = 0.024), and disease-specific survival (HR = 0.57, 95% CI: 0.36-0.89, and P = 0.015) for patients with BRCA. Besides, the prognostic value of NR3C2 was verified by the OSbrca database. GSEA results suggested that enriched pathways included neuroactive ligand-receptor interaction, focal adhesion, and ECM-receptor interaction. NR3C2 expression was moderately correlated with mast cells and some T cell subsets in BRCA.

Conclusion

NR3C2 is a potential prognostic biomarker that could help clinicians develop more appropriate treatment plans for individual patients with BRCA.

Increased Expression of TICRR Predicts Poor Clinical Outcomes: A Potential Therapeutic Target for Papillary Renal Cell Carcinoma

Background: Papillary renal cell carcinoma (PRCC), although the second-most common type of renal cell carcinoma, still lacks specific biomarkers for diagnosis, treatment, and prognosis. TopBP1-interacting checkpoint and replication regulator (TICRR) is a DNA replication initiation regulator upregulated in various cancers. We aimed to evaluate the role of TICRR in PRCC tumorigenesis and prognosis.

Methods: Based on the Kidney Renal Papillary cell carcinoma Project (KIRP) on The Cancer Genome Atlas (TCGA) database, we determined the expression of TICRR using the Wilcoxon rank sum test. The biological functions of TICRR were evaluated using the Metascape database and Gene Set Enrichment Analysis (GSEA). The association between TICRR and immune cell infiltration was investigated by single sample GSEA. Logistic analysis was applied to study the correlation between TICRR expression and clinicopathological characteristics. Finally, Cox regression analysis, Kaplan–Meier analysis, and nomograms were used to determine the predictive value of TICRR on clinical outcomes in PRCC patients.

Results:TICRR expression was significantly elevated in PRCC tumors (P < 0.001). Functional annotation indicated enrichment with negative regulation of cell division, cell cycle, and corresponding pathways in the high TICRR expression phenotype. High TICRR expression in PRCC was associated with female sex, younger age, and worse clinical stages. Cox regression analysis revealed that TICRR was a risk factor for overall survival [hazard ratio (HR): 2.80, P = 0.002], progression-free interval (HR: 2.86, P < 0.001), and disease-specific survival (HR: 7.03, P < 0.001), especially in patients with male sex, age below 60 years, clinical stages II–IV and clinical T stage T1–T2.

Conclusion: Increased TICRR expression in PRCC might play a role in tumorigenesis by regulating the cell cycle and has prognostic value for clinical outcomes.

Expression Profile and Prognostic Values of Mini-Chromosome Maintenance Families (MCMs) in Breast Cancer

Background

Mini-chromosome maintenance families (MCMs) were considered the key factors for DNA replication initiation. Emerging evidences indicate that MCM2-7 (MCMs) are highly expressed in tissues from various malignant tumors. However, little is known about the clinical values of MCMs in breast cancer.

Material/Methods

In our study, a comprehensive bioinformatics analysis was performed to investigate expression patterns, potential functions, and prognostic values of MCMs in breast cancer, through ONCOMINE, bc-GenExMiner v4.1, Kaplan-Meier Plotter, cBioPortal and GeneMANIA databases.

Results

We found that mRNA levels of MCMs were significantly elevated in breast cancer, especially in fast-growing and spreading tumor subtypes. These over-expressed MCMs predicted worse prognosis for breast cancer patients with shorter relapse-free survival (RFS) and overall survival. Among these six factors, high expression of MCM2/4/5/7 significantly reduced the RFS for patients with Luminal-A or B breast cancer and elevated MCM6/7 indicated shorter RFS for patients with basal-like or HER2-positive breast cancer. We also found that genomic alteration of MCMs was frequently found in breast cancer and the most common alteration was mRNA upregulation and amplification. Furthermore, MCMs were highly correlated with CDC45, CDC7, TIMELESS, ORC6, MCM10, ORC5, ORC4 and ORC3, mainly functioning to control the DNA replication initiation and genome stability.

Conclusions

These results suggest that MCMs are attractive prognostic biomarkers for breast cancer. Our study also provides useful clinical information about the potential of MCMs as therapeutic targets.

Expression of tripartite motif-containing 44 and its prognostic and clinicopathological value in human malignancies:a meta-analysis

BACKGROUND

Previous researches have reported that tripartite motif-containing 44 (TRIM44) is related to the prognosis of multiple human tumors. This study was designed to systematically assess the prognostic value of TRIM44 in human malignancies and summarize its possible tumor-related mechanisms.

METHODS

The available databases were searched for eligible studies that evaluated the clinicopathological and prognostic roles of TRIM44 in patients with malignancies. The hazard ratios (HR) and odds ratios (OR) were combined to assess the predictive role of TRIM44 using Stata/SE 14.1 software.

RESULTS

A total of 1740 patients from thirteen original studies were finally included in this study. The results of the combined analysis showed that over-expression of TRIM44 protein was significantly correlated with shorter overall survival (OS) (HR = 1.94, 95% CI: 1.60-2.35) and worse disease-free survival (DFS) (HR = 2.13, 95% CI: 1.24-3.65) in cancer patients. Additionally, the combined ORs indicated that elevated expression level of TRIM44 protein was significantly associated with lymph node metastasis (OR = 2.69, 95% CI: 1.71-4.24), distant metastasis (OR = 10.35, 95% CI: 1.01-106.24), poor tumor differentiation (OR = 1.78, 95% CI: 1.03-3.09), increased depth of tumor invasion (OR = 2.72, 95% CI: 1.73-4.30), advanced clinical stage (OR = 2.75, 95% CI: 2.04-3.71), and recurrence (OR = 2.30, 95% CI: 1.34-3.95). Furthermore, analysis results using Gene Expression Profiling Interactive Analysis (GEPIA) showed that the expression level of TRIM44 mRNA was higher in most tumor tissues than in the corresponding normal tissues, and the relationship between TRIM44 mRNA level and prognosis in various malignant tumors also explored in GEPIA and OS analysis webservers.

CONCLUSIONS

TRIM44 may serve as a valuable prognostic biomarker and a potential therapeutic target for patients with malignancies.

OSluca: An Interactive Web Server to Evaluate Prognostic Biomarkers for Lung Cancer

Lung cancer is the principal cause of leading cancer-related incidence and mortality in the world. Various studies have excavated the potential prognostic biomarkers for cancer patients based on gene expression profiles. However, most of these reported biomarkers lack independent validation in multiple cohorts. Herein, we collected 35 datasets with long-term follow-up clinical information from TCGA (2 cohorts), GEO (32 cohorts), and Roepman study (1 cohort), and developed a web server named OSluca (Online consensus Survival for Lung Cancer) to assess the prognostic value of genes in lung cancer. The input of OSluca is an official gene symbol, and the output web page of OSluca displays the survival analysis summary with a forest plot and a survival table from Cox proportional regression in each cohort and combined cohorts. To test the performance of OSluca, 104 previously reported prognostic biomarkers in lung carcinoma were evaluated in OSluca. In conclusion, OSluca is a highly valuable and interactive prognostic web server for lung cancer. It can be accessed at http:// bioinfo.henu.edu.cn/LUCA/LUCAList.jsp.

The Application of Deep Learning in Cancer Prognosis Prediction

Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

OBJECTIVE

Invasive breast cancer (BRCA) is one of the prevalent types of invasive tumors with high mortality worldwide. Due to the lack of effective treatment to control the recurrence of distant metastases, the prognosis of BRCA is still very unsatisfactory. We aimed to find some biomarkers by bioinformatics analysis for survival prediction.

METHODS

Differentially expressed genes (DEGs) were screened out based on tumor group and normal group. Then, the weighted gene correlation network analysis (WGCNA) was employed to identify the clinically associated gene sets. Meanwhile, the enrichment analyses were performed for the functional annotation of the critical genes. The Kaplan Meier analysis calculated the essential genes' prognostic value.

RESULTS

After threshold screening, 1655 DEGs were obtained for subsequent analysis. 51 out of 1655 DEGs were significantly associated with BRCA patients' estrogen receptor status via WGCNA. Three genes (FABP7, CXCL3, and LOC284578) out of the 51 genes were associated with overall survival, and 3 genes were relapse-free survival associated. Finally, we obtained 5 essential prognostic associated genes (FABP7, CXCL3, LOC284578, CAPN6, and NRG2), which could be used as prognostic factors for BRCA.

CONCLUSION

Our findings obtained a gene module associated with BRCA clinical trait and several key genes that acted as essential components in the prognostic of cancer, which may improve its treatment.