High WDR34 mRNA expression as a potential prognostic biomarker in patients with breast cancer as determined by integrated bioinformatics analysis

Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer

Background

Human epidermal growth receptor 2-positive (HER2+) breast cancer (BC) is a heterogeneous subgroup. Estrogen receptor (ER) status is emerging as a predictive marker within HER2+ BCs, with the HER2+/ER+ cases usually having better survival in the first 5 years after diagnosis but have higher recurrence risk after 5 years compared to HER2+/ER-. This is possibly because sustained ER signaling in HER2+ BCs helps escape the HER2 blockade. Currently HER2+/ER+ BC is understudied and lacks biomarkers. Thus, a better understanding of the underlying molecular diversity is important to find new therapy targets for HER2+/ER+ BCs.

Methods

In this study, we performed unsupervised consensus clustering together with genome-wide Cox regression analyses on the gene expression data of 123 HER2+/ER+ BC from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort to identify distinct HER2+/ER+ subgroups. A supervised eXtreme Gradient Boosting (XGBoost) classifier was then built in TCGA using the identified subgroups and validated in another two independent datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) (accession number GSE149283)). Computational characterization analyses were also performed on the predicted subgroups in different HER2+/ER+ BC cohorts.

Results

We identified two distinct HER2+/ER+ subgroups with different survival outcomes using the expression profiles of 549 survival-associated genes from the Cox regression analyses. Genome-wide gene expression differential analyses found 197 differentially expressed genes between the two identified subgroups, with 15 genes overlapping the 549 survival-associated genes.XGBoost classifier, using the expression values of the 15 genes, achieved a strong cross-validated performance (Area under the curve (AUC) = 0.85, Sensitivity = 0.76, specificity = 0.77) in predicting the subgroup labels. Further investigation partially confirmed the differences in survival, drug response, tumor-infiltrating lymphocytes, published gene signatures, and CRISPR-Cas9 knockout screened gene dependency scores between the two identified subgroups.

Conclusion

This is the first study to stratify HER2+/ER+ tumors. Overall, the initial results from different cohorts showed there exist two distinct subgroups in HER2+/ER+ tumors, which can be distinguished by a 15-gene signature. Our findings could potentially guide the development of future precision therapies targeted on HER2+/ER+ BC.

Epidemiological, Clinical, and Genomic Profile in Head and Neck Cancer Patients and Their Families

Inherited cancer predisposition genes are described as risk factors in head and neck cancer (HNC) families. To explore the clinical and epidemiological data and their association with a family history of cancer, we recruited 74 patients and 164 relatives affected by cancer. The germline copy number alterations were evaluated in 18 patients using array comparative genomic hybridization. Two or more first-degree relatives with HNC, tobacco-associated tumor sites (lung, esophagus, and pancreas), or other related tumors (breast, colon, kidney, bladder, cervix, stomach carcinomas, and melanoma) were reported in 74 families. Ten index patients had no exposure to any known risk factors. Family members presented tumors of 19 topographies (30 head and neck, 26 breast, 21 colon). In first-degree relatives, siblings were frequently affected by cancer (n = 58, 13 had HNC). Breast cancer (n = 21), HNC (n = 19), and uterine carcinoma (n = 15) were commonly found in first-degree relatives and HNC in second-degree relatives (n = 11). Nineteen germline genomic imbalances were detected in 13 patients; three presented gains of WRD genes. The number of HNC patients, the degree of kinship, and the tumor types detected in each relative support the role of heredity in these families. Germline alterations may potentially contribute to cancer development.

Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis

OBJECTIVES

To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS).

MATERIALS AND METHODS

This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model.

RESULTS

Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively).

CONCLUSION

The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.

COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation

Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831–3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis.

Prognostic Assessment of Oxidative Stress-Related Genes in Colorectal Cancer and New Insights into Tumor Immunity

Oxidative stress is crucial to the biology of tumors. Oxidative stress' potential predictive significance in colorectal cancer (CRC) has not been studied; nevertheless here, we developed a forecasting model based on oxidative stress to forecast the result of CRC survival and enhance clinical judgment. The training set was chosen from the transcriptomes of 177 CRC patients in GSE17536. For validation, 65 samples of colon cancer from GSE29621 were utilized. For the purpose of choosing prognostic genes, the expression of oxidative stress-related genes (OXEGs) was found. Prognostic risk models were built using multivariate Cox regression analysis, univariate Cox regression analysis, and LASSO regression analysis. The outcomes of the western blot and transcriptome sequencing tests were finally confirmed. ATF4, CARS2, CRP, GPX1, IL1B, MAPK8, MRPL44, MTFMT, NOS1, OSGIN2, SOD2, AARS2, and FOXO3 were among the 14 OXEGs used to build prognostic characteristics. Patients with CRC were categorized into low-risk and high-risk groups according on their median risk scores. Cox regression analysis using single and multiple variables revealed that OXEG-related signals were independent risk factors for CRC. Additionally, the validation outcomes from western blotting and transcriptome sequencing demonstrated that OXEGs were differently expressed. Using 14 OXEGs, our work creates a predictive signature that may be applied to the creation of new prognostic models and the identification of possible medication candidates for the treatment of CRC.

Pan-Cancer Analysis Reveals the Relation between TRMT112 and Tumor Microenvironment

Dysregulated epigenetic modifications play a critical role in cancer development where TRMT112 is a member of the transfer RNA (tRNA) methyltransferase family. Till now, no studies have revealed the linkage between TRMT112 expression and diverse types of tumors. Based on TCGA data, we first probed into the relation between TRMT112 and prognosis and the potential role of TRMT112 in tumor microenvironment across 33 types of tumor. TRMT112 presented with increased expression in most cancers, which was significantly prognostic. Furthermore, TRMT112 was associated with tumor-associated fibroblasts in a variety of cancers. Additionally, a positive relationship was identified between TRMT112 expression and multiple tumor-related immune infiltrations, such as dendritic cells, CD8+ T cells, macrophages, CD4+ T cells, neutrophils, and B cells in lung adenocarcinoma and breast invasive carcinoma. In summary, our results suggest that TRMT112 might be a potential prognostic predictor of cancers and involved in regulating multiple cancer-related immune responses to some extent.

Developing a 5-Gene Signature Related to Pyroptosis for Osteosarcoma Patients

Although the incidence of osteosarcoma (OS) is relatively low compared with other cancer types, the overall survival of metastatic OS was less than 30%. This study aimed to reveal the role of pyroptosis in osteosarcoma and develop a prognostic model related to pyroptosis. Weighted correlation network analysis (WGCNA) was applied to identify key gene modules related to pyroptosis. Univariate Cox regression analysis was used to screen prognostic genes related to pyroptosis. The least absolute shrinkage and selection operator (LASSO) and stepwise Akaike information criterion (stepAIC) were employed to optimize and construct a prognostic model. Five prognostic genes (COL13A1, TNFRSF1A, LILRA6, CTNNBIP1, and CD180) related to pyroptosis were identified. According to the 5-gene signature, OS samples were divided into high- and low-PPRS groups with differential prognosis. Immune-related pathways were more activated in the low-PPRS group. The 5-gene signature was effective and robust to predict OS prognosis. These five prognostic genes were involved in OS development and may serve as new targets for developing therapeutic drugs.

Comprehensive Analysis of Histone Modifications in Hepatocellular Carcinoma Reveals Different Subtypes and Key Prognostic Models

Histone modification, an important epigenetic mechanism, is related to the carcinogenesis of hepatocellular carcinoma (HCC). In three datasets, we screened 88 epigenetic-dysregulated PCGs (epi-PCGs) , which were significantly associated with HCC survival and could cluster HCC into three molecular subtypes. These subtypes were associated with prognosis, immunomodulatory alterations, and response to different treatment strategies. Based on 88 epi-PCGs in the TCGA training set, a risk prediction model composed of 4 epi-PCGs was established. The model was closely related to the clinicopathological features and showed a strong predictive ability in different clinical subgroups. In addition, the risk prediction model was an independent prognostic factor for patients with HCC. The significance of epi-PCGs in HCC is revealed by our data analysis.

Identification of Six Genes as Diagnostic Markers for Colorectal Cancer Detection by Integrating Multiple Expression Profiles

Background

Many studies have demonstrated the promising utility of DNA methylation and miRNA as biomarkers for colorectal cancer (CRC) early detection. However, mRNA is rarely reported. This study aimed to identify novel fecal-based mRNA signatures.

Methods

The differentially expressed genes (DEGs) were first determined between CRCs and matched normal samples by integrating multiple datasets. Then, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidates of aberrantly expressed genes. Next, the potential functions were investigated for the candidate signatures and their ability to detect CRC and pan-cancers was comprehensively evaluated.

Results

We identified 1841 common DEGs in two independent datasets. Functional enrichment analysis revealed they were mainly related to extracellular structure, biosynthesis, and cell adhesion. The CRC classifier was established based on six genes screened by LASSO regression. Sensitivity, specificity, and area under the ROC curve (AUC) for CRC detection were 79.30%, 80.40%, and 0.85 (0.76–0.92) in the training set, and these indexes achieved 93.20%, 41.80%, and 0.73 (0.65–0.83) in the testing set. For validation set, the sensitivity, specificity, and AUC were 98.90%, 98.00%, and 0.97 (0.94–0.99). The average sensitivities exceeded 90.00% for CRCs with different clinical features. For adenomas detection, the sensitivity and specificity were 74.50% and 64.00%. Besides, the six genes obtained an average AUC of 0.855 for pan-cancer detection.

Conclusion

The six-gene signatures showed ability to detect CRC and pan-cancer samples, which could be served as potential diagnostic markers.

Bioinformatics Analysis Reveals an Association between Autophagy, Prognosis, Tumor Microenvironment, and Immunotherapy in Osteosarcoma

Autophagy is a catabolic pathway involved in the regulation of bone homeostasis. We explore clinical correlation of autophagy-related key molecules to establish risk signature for predicting the prognosis, tumor microenvironment (TME), and immunotherapy response of osteosarcoma. Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in osteosarcoma. Autophagy-related genes (ARGs) were extracted from the established risk signature of the Molecular Signatures Database of Gene Set Enrichment Analysis (GSEA) by univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Overall survival (OS), TME score, abundance of infiltrating immune cells, and response to immune-checkpoint blockade (ICB) treatment in patients with different risks were compared based on risk score. Nine ARGs were identified and risk signature was constructed. In all osteosarcoma datasets, the OS was significantly longer in the high-risk patients than low-risk onset. Risk signature significantly stratified clinical outcomes, including OS, metastatic status, and survival status. Risk signature was an independent variable for predicting osteosarcoma OS and showed high accuracy. A nomogram based on risk signature and metastases was developed. The calibration curve confirmed the consistency in 1-year, 3-year, and 5-year predicted OS and the actual OS. The risk score was related to 6 kinds of T cells and macrophages, myeloid-derived suppressor cell, natural killer cell, immune score, and stromal score in TME. The risk signature helped in predicting patients' response to anti-PD1/anti-PD-L1 treatment. The ARGs-led risk signature has important value for survival prediction, risk stratification, tumor microenvironment, and immune response evaluation of osteosarcoma.

WDR34 affects PI3K/Akt and Wnt/β-catenin pathways to regulates malignant biological behaviors of glioma cells

PURPOSE

Glioma is the most prevalent primary intracranial tumor globally. WDR34, a member of the WDR superfamily with five WD40 repeats, is involved in the pathogenesis of several tumors. However, the role of WDR34 in glioma progression is unknown.

METHODS

The expression and prognostic significance of WDR34 in glioma patients were analyzed using GEPIA. WDR34 expression was detected by qRT-PCR. Western blot was employed to determine the expression of Ki67, proliferating cell nuclear antigen (PCNA), matrix metallopeptidase (MMP)2, MMP9, phosphatase and tensin homolog, protein kinase B (Akt), phosphorylated Akt, β-catenin, and c-Myc. CCK-8, BrdU incorporation assay, Transwell invasion assay, flow cytometry analysis, and measurement of caspase-3 and caspase-9 activities were conducted to examine the effects of WDR34 knockdown on glioma cells.

RESULTS

WDR34 was upregulated in glioma, which predicted a poor prognosis in glioma patients. WDR34 knockdown inhibited cell proliferation and reduced the expression of Ki67 and PCNA in glioma cells. WDR34 knockdown repressed the invasive ability of glioma cells by decreasing MMP-2 and MMP-9 expression. WDR34 knockdown increased the apoptotic rate and caspase-3 and caspase-9 activities in glioma cells. The PI3K/Akt and Wnt/β-catenin pathways were inhibited after WDR34 knockdown in glioma cells. Moreover, overexpression of Akt or β-catenin reversed the function of WDR34 knockdown on proliferation, invasion, and apoptosis. WDR34 knockdown reduced tumor growth in vivo.

CONCLUSIONS

WDR34 knockdown inhibited malignant biological behaviors of glioma cells by inactivating the PI3K/Akt and Wnt/β-catenin signaling cascades.

High Throughput Transcriptome Data Analysis and Computational Verification Reveal Immunotherapy Biomarkers of Compound Kushen Injection for Treating Triple-Negative Breast Cancer

Background

Although notable therapeutic and prognostic benefits of compound kushen injection (CKI) have been found when it was used alone or in combination with chemotherapy or radiotherapy for triple-negative breast cancer (TNBC) treatment, the effects of CKI on TNBC microenvironment remain largely unclear. This study aims to construct and validate a predictive immunotherapy signature of CKI on TNBC.

Methods

The UPLC-Q-TOF-MS technology was firstly used to investigate major constituents of CKI. RNA sequencing data of CKI-perturbed TNBC cells were analyzed to detect differential expression genes (DEGs), and the GSVA algorithm was applied to explore significantly changed pathways regulated by CKI. Additionally, the ssGSEA algorithm was used to quantify immune cell abundance in TNBC patients, and these patients were classified into distinct immune infiltration subgroups by unsupervised clustering. Then, prognosis-related genes were screened from DEGs among these subgroups and were further overlapped with the DEGs regulated by CKI. Finally, a predictive immunotherapy signature of CKI on TNBC was constructed based on the LASSO regression algorithm to predict mortality risks of TNBC patients, and the signature was also validated in another TNBC cohort.

Results

Twenty-three chemical components in CKI were identified by UPLC-Q-TOF-MS analysis. A total of 3692 DEGs were detected in CKI-treated versus control groups, and CKI significantly activated biological processes associated with activation of T, natural killer and natural killer T cells. Three immune cell infiltration subgroups with 1593 DEGs were identified in TNBC patients. Then, two genes that can be down-regulated by CKI with hazard ratio (HR) > 1 and 26 genes that can be up-regulated by CKI with HR < 1 were selected as key immune- and prognosis-related genes regulated by CKI. Lastly, a five-gene prognostic signature comprising two risky genes (MARVELD2 and DYNC2I2) that can be down-regulated by CKI and three protective genes (RASSF2, FERMT3 and RASSF5) that can be up-regulated by CKI was developed, and it showed a good performance in both training and test sets.

Conclusions

This study proposes a predictive immunotherapy signature of CKI on TNBC, which would provide more evidence for survival prediction and treatment guidance in TNBC as well as a paradigm for exploring immunotherapy biomarkers in compound medicines.

A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

PURPOSE

To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data.

METHODS

Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model.

RESULTS

Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model.

CONCLUSION

Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.