Identification of breast cancer recurrence risk factors based on functional pathways in tumor and normal tissues

Unveiling Potential Therapeutic Targets for Breast Cancer Recurrence: Differentially Expressed Genes and Pathways in Post-Surgery Patients

Various intrinsic and extrinsic factors, including genetic changes and environmental factors, have been reported to contribute to tumor recurrence. However, insufficient information about the significantly dysregulated genes and pathways responsible for cancer recurrence, even after surgical removal of tumors and chemotherapy. The aim of this research is to find out the fundamental genes linked with progression of cancer that may play a critical role in breast cancer recurrence. To achieve this, a microarray dataset of Affymetrix Human Genome U133 Plus 2.0 Array platform was used to identify downregulated and upregulated genes that associated with tumor recurrence in post-surgery patients. The study includes 20 specimen, 10 samples extracted at the time of diagnosis and 10 samples taken 30 minutes post-surgery and chemotherapy. Genes that stand out from the rest in their level of expression were further subjected to subsequent functional enrichment analysis and hub genes identification to pinpoint the key genes associated with recurrence. Results revealed that significantly overexpressed genes were found to be enriched in cancer progression-associated signaling pathways, for example, Wnt pathway and proteoglycans in cancer. Moreover, the identified key hub genes (COL1A1, IGF1, COL1A2, DCN, LUM, MMP2, JUN, CXCL12, THBS2, and LOX) majorly found to play a role in gene expression regulation, dysregulated immune system, epithelial-to-mesenchymal transition, and extracellular matrix remodeling thus promoting the development of cancer and increasing the chances of recurrence after surgery and chemotherapy. The findings have uncovered key therapeutic targets associated with tumor recurrence through potential ECM-related genes whose overexpression may significantly contribute to tumorigenesis in breast cancer survivors by epithelial-to-mesenchymal transition and targeting them may improve the chances of better survival breast cancer patients and increase the quality of life by reducing the chances of recurrence. However, the study is solely bioinformatics-based; therefore, future study will be experimental validations to bring forth these key genes as potential therapeutic targets.

In vivo metabolic imaging identifies lipid vulnerability in a preclinical model of Her2+/Neu breast cancer residual disease and recurrence

Recurrent cancer cells that evade therapy is a leading cause of death in breast cancer patients. This risk is high for women showing an overexpression of human epidermal growth factor receptor 2 (Her2). Cells that persist can rely on different substrates for energy production relative to their primary tumor counterpart. Here, we characterize metabolic reprogramming related to tumor dormancy and recurrence in a doxycycline-induced Her2+/Neu model of breast cancer with varying times to recurrence using longitudinal fluorescence microscopy. Glucose uptake (2-NBDG) and mitochondrial membrane potential (TMRE) imaging metabolically phenotype mammary tumors as they transition to regression, dormancy, and recurrence. “Fast-recurrence” tumors (time to recurrence ~55 days), transition from glycolysis to mitochondrial metabolism during regression and this persists upon recurrence. “Slow-recurrence” tumors (time to recurrence ~100 days) rely on both glycolysis and mitochondrial metabolism during recurrence. The increase in mitochondrial activity in fast-recurrence tumors is attributed to a switch from glucose to fatty acids as the primary energy source for mitochondrial metabolism. Consequently, when fast-recurrence tumors receive treatment with a fatty acid inhibitor, Etomoxir, tumors report an increase in glucose uptake and lipid synthesis during regression. Treatment with Etomoxir ultimately prolongs survival. We show that metabolic reprogramming reports on tumor recurrence characteristics, particularly at time points that are essential for actionable targets. The temporal characteristics of metabolic reprogramming will be critical in determining the use of an appropriate timing for potential therapies; namely, the notion that metabolic-targeted inhibition during regression reports long-term therapeutic benefit.

Cancer Stem Cells and Cell Cycle Genes as Independent Predictors of Relapse in Non-small Cell Lung Cancer: Secondary Analysis of a Prospective Study

Purpose

Cancer stem cells (CSCs) are described as resistant to chemotherapy and radiotherapy. It has been shown that CSCs influence disease-free survival in patients undergoing surgery for lung cancer (NCT04634630). We recently described an overexpression of CSCs recurrence-related genes (RG) in lung cancer. This study aims to investigate CSC frequency and RG expression as predictors of disease-free survival in lung cancer.

Experimental Design

This secondary analysis of a prospective cohort study involved 22 surgical tumor specimens from 22 patients harboring early (I-II) and locally advanced (IIIA) stages ACL and SCCL. Cell population frequency analysis of ALDH^high (CSCs) and ALDH^low (cancer cells) was performed on each tumor specimen. In addition, RG expression was assessed for 31 target genes separately in ALDH^high and ALDH^low populations. CSCs frequency and RG expression were assessed as predictors of disease-free survival by Cox analysis.

Results

CSCs frequency and RG expression were independent predictors of disease-free survival. CSC frequency was not related to disease-free survival in early-stage patients (HR = 0.84, 95%CI = 0.53-1.33, P = .454), whereas it was a risk factor for locally advanced-stage patients (HR = 1.22, 95%CI = 1.09-1.35, P = .000). RG expression—if measured in CSCs—was related to a higher risk of recurrence (HR = 1.19, 95%CI = 1.03-1.39, P = .021). The effect of RG expression measured in cancer cells on disease-free survival was lower and was not statistically significant (HR = 1.12, 95%CI = 0.94-1.33, P = .196).

Conclusions

CSCs frequency and RG expression are independent predictors of relapse in lung cancer. Considering these results, CSCs and RG may be considered for both target therapy and prognosis.

A Novel IGLC2 Gene Linked With Prognosis of Triple-Negative Breast Cancer

BACKGROUND

Immunoglobulin-related genes are associated with the favorable prognosis of triple-negative breast cancer (TNBC) patients. We aimed to analyze the function and prognostic value of immunoglobulin lambda constant 2 (IGLC2) in TNBC patients.

METHODS

We knocked down the gene expression of IGLC2 (IGLC2-KD) in MDA-MB-231 cells to evaluate the proliferation, migration, and invasion of tumors via 3-(4,5-Dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, wound healing, and transwell cell migration assay respectively. Relapse-free survival (RFS) and distant metastasis-free survival (DMFS) analyses were conducted using the KM plotter online tool. The GSE76275 data set was used to analyze the association of IGLC2 and clinical characteristics. A pathway enrichment analysis was conducted using the next-generation sequencing data of wild-type and IGLC2-KD MDA-MB-231 cells.

RESULTS

The low gene expression of IGLC2 was related to unfavorable RFS, DMFS. The high expression of IGLC2 was exhibited in the basal-like immune-activated (BLIA) TNBC molecular subtype, which was immune-activated and showed excellent response to immune therapy. IGLC2 was positively correlated with programmed death-ligand 1 (PD-L1) as shown by Spearman correlation (r = 0.25, p < 0.0001). IGLC2 had a strong prognostic effect on lymph node-negative TNBC (RFS range: 0.31, q value= 8.2e-05; DMFS = 0.16, q value = 8.2e-05) but had no significance on lymph node-positive ones. The shRNA-mediated silencing of IGLC2 increased the proliferation, migration, and invasion of MDA-MB-231 cells. The results of pathway enrichment analysis showed that IGLC2 is related to the PI3K-Akt signaling pathway, MAPK signaling pathway, and extracellular matrix-receptor interaction. We confirmed that MDA-MB-231 tumor cells expressed IGLC2, subverting the traditional finding of generation by immune cells.

CONCLUSIONS

IGLC2 linked with the proliferation, migration, and invasion of MDA-MB-231 cells. A high expression of IGLC2 was related to favorable prognosis for TNBC patients. IGLC2 may serve as a biomarker for the identification of TNBC patients who can benefit the most from immune checkpoint blockade treatment.

Evaluation of breast cancer stem cells in human primary breast carcinoma and their role in aggressive behavior of the disease

BACKGROUND AND AIM

To delineate the underlying molecular mechanisms responsible for the intratumoral enrichment of breast cancer stem cells (BCSCs) in aggressive breast tumors, we evaluated the frequency and characteristics of BCSCs within the tumor tissue in primary human breast carcinomas. We assessed the expression profiles of various genes in cancer cells (CC) and stromal cells (SC) from these tumors to delineate the role played by the cellular niche in de novo origin or expansion of intra-tumoral cancer stem cells (CSC).

METHOD

The study included primary tumor and adjacent normal breast tissue specimens from chemotherapy-naïve breast carcinoma patients. The BCSCs, identified as Lin^-CD44⁺CD24^- and aldehyde dehydrogenase 1 A1 positive, were enumerated. The flow-cytometrically sorted stromal, and CC were processed for gene expression profiling using a custom-designed polymerase chain reaction array of genes known to facilitate disease progression.

RESULTS

The frequency of BCSCs within the tumor mass correlated significantly with histopathological and molecular grades of tumors, indicating a direct relationship of BCSC with the aggressive behavior of breast cancer. Further, a significantly increased expression of the genes associated with growth factors, cytokines and matricellular proteins in tumors were found in high BCSCs compared to Lo-BCSC tumors, suggesting the possible contribution of stromal and CC in an intratumoral expansion of CSCs. Similarly, a significant upregulation of genes associated with hypoxia and angiogenesis in Hi-BCSCs tumors further supported the role of a hypoxic environment.

CONCLUSION

Overall, the findings suggest the molecular crosstalk between SC and CC potentially (directly or indirectly) contributes to the expansion of CSC.

RELEVANCE FOR PATIENTS

The current study highlights the importance of CSC as a potential future predictive/prognostic marker for aggressive breast cancer. The present study predicts the potential risk stratification based on the frequency of BCSCs in primary breast tumors and existing prognostic factors.

Genetic heterogeneity and prognostic impact of recurrent ANK2 and TP53 mutations in mantle cell lymphoma: a multi-centre cohort study

The molecular features of mantle cell lymphoma (MCL), including its increased incidence, and complex therapies have not been investigated in detail, particularly in East Asian populations. In this study, we performed targeted panel sequencing (TPS) and whole-exome sequencing (WES) to investigate the genetic alterations in Korean MCL patients. We obtained a total of 53 samples from MCL patients from five Korean university hospitals between 2009 and 2016. We identified the recurrently mutated genes such as SYNE1, ATM, KMT2D, CARD11, ANK2, KMT2C, and TP53, which included some known drivers of MCL. The mutational profiles of our cohort indicated genetic heterogeneity. The significantly enriched pathways were mainly involved in gene expression, cell cycle, and programmed cell death. Multivariate analysis revealed that ANK2 mutations impacted the unfavourable overall survival (hazard ratio [HR] 3.126; P = 0.032). Furthermore, TP53 mutations were related to worse progression-free survival (HR 7.813; P = 0.043). Among the recurrently mutated genes with more than 15.0% frequency, discrepancies were found in only 5 genes from 4 patients, suggesting comparability of the TPS to WES in practical laboratory settings. We provide the unbiased genetic landscape that might contribute to MCL pathogenesis and recurrent genes conferring unfavourable outcomes.

Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis

Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n = 920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups according to node (+/−) and recurrence (+/−). We identified a sub-network containing two candidate genes (LST1 and IGHM) and six novel genes (IGHA1, IGHD, IGHG1, IGHG3, IGLC2, and IGLJ3) related to B cell-specific immunoglobulin. These novel genes were correlated with recurrence under the control of node status and were found to function as tumor suppressors; higher mRNA expression indicated a lower risk of recurrence (hazard ratio, HR = 0.87, p = 0.001). We created an immune index score by performing principle component analysis and divided the genes into low and high groups. This discrete index significantly predicted relapse-free survival (RFS) (high: HR = 0.77, p = 0.019; low: control). Public tool KM Plotter and TCGA-BRCA gene expression data were used to validate. We confirmed these genes are correlated with RFS and distal metastasis-free survival (DMFS) in triple-negative breast cancer (TNBC) and general breast cancer.

Subgroup analysis reveals molecular heterogeneity and provides potential precise treatment for pancreatic cancers

Background

The relationship between molecular heterogeneity and clinical features of pancreatic cancer remains unclear. In this study, pancreatic cancer was divided into different subgroups to explore its specific molecular characteristics and potential therapeutic targets.

Patients and methods

Expression profiling data were downloaded from The Cancer Genome Atlas database and standardized. Bioinformatics techniques such as unsupervised hierarchical clustering was used to explore the optimal molecular subgroups in pancreatic cancer. Clinical pathological features and pathways in each subgroup were also analyzed to find out the potential clinical applications and initial promotive mechanisms of pancreatic cancer.

Results

Pancreatic cancer was divided into three subgroups based on different gene expression features. Patients included in each subgroup had specific biological features and responded significantly different to chemotherapy.

Conclusion

Three distinct subgroups of pancreatic cancer were identified, which means that patients in each subgroup might benefit from targeted individual management.

Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

A pathways-based prediction model for classifying breast cancer subtypes

Breast cancer is highly heterogeneous and is classified into four subtypes characterized by specific biological traits, treatment responses, and clinical prognoses. We performed a systemic analysis of 698 breast cancer patient samples from The Cancer Genome Atlas project database. We identified 136 breast cancer genes differentially expressed among the four subtypes. Based on unsupervised clustering analysis, these 136 core genes efficiently categorized breast cancer patients into the appropriate subtypes. Functional enrichment based on Kyoto Encyclopedia of Genes and Genomes analysis identified six functional pathways regulated by these genes: JAK-STAT signaling, basal cell carcinoma, inflammatory mediator regulation of TRP channels, non-small cell lung cancer, glutamatergic synapse, and amyotrophic lateral sclerosis. Three support vector machine (SVM) classification models based on the identified pathways effectively classified different breast cancer subtypes, suggesting that breast cancer subtype-specific risk assessment based on disease pathways could be a potentially valuable approach. Our analysis not only provides insight into breast cancer subtype-specific mechanisms, but also may improve the accuracy of SVM classification models.