Genome Instability-Derived Genes Are Novel Prognostic Biomarkers for Triple-Negative Breast Cancer

Emerging role and function of SPDL1 in human health and diseases

SPDL1 (spindle apparatus coiled-coil protein 1), also referred to as CCDC99, is a recently identified gene involved in cell cycle regulation. SPDL1 encodes a protein, hSpindly, which plays a critical role in the maintenance of spindle checkpoint silencing during mitosis. hSpindly coordinates microtubule attachment by promoting kinesin recruitment and mitotic checkpoint signaling. Moreover, the protein performs numerous biological functions in vivo and its aberrant expression is closely associated with abnormal neuronal development, pulmonary interstitial fibrosis, and malignant tumor development. In this review, we provide an overview of studies that reveal the characteristics of SPDL1 and of the protein encoded by it, as well as its biological and tumor-promoting functions.

FEN1 Inhibition as a Potential Novel Targeted Therapy against Breast Cancer and the Prognostic Relevance of FEN1

To improve breast cancer treatment and to enable new strategies for therapeutic resistance, therapeutic targets are constantly being studied. Potential targets are proteins of DNA repair and replication and genomic integrity, such as Flap Endonuclease 1 (FEN1). This study investigated the effects of FEN1 inhibitor FEN1-IN-4 in combination with ionizing radiation on cell death, clonogenic survival, the cell cycle, senescence, doubling time, DNA double-strand breaks and micronuclei in breast cancer cells, breast cells and healthy skin fibroblasts. Furthermore, the variation in the baseline FEN1 level and its influence on treatment prognosis was investigated. The cell lines show specific response patterns in the aspects studied and have heterogeneous baseline FEN1 levels. FEN1-IN-4 has cytotoxic, cytostatic and radiosensitizing effects, expressed through increasing cell death by apoptosis and necrosis, G2M share, senescence, double-strand breaks and a reduced survival fraction. Nevertheless, some cells are less affected by the cytotoxicity and fibroblasts show a rather limited response. In vivo, high FEN1 mRNA expression worsens the prognosis of breast cancer patients. Due to the increased expression in breast cancer tissue, FEN1 could represent a new tumor and prognosis marker and FEN1-IN-4 may serve as a new potent agent in personalized medicine and targeted breast cancer therapy.

Genome instability-derived genes as a novel prognostic signature for lung adenocarcinoma

Background: An increasing number of patients are being diagnosed with lung adenocarcinoma, but there remains limited progress in enhancing prognostic outcomes and improving survival rates for these patients. Genome instability is considered a contributing factor, as it enables other hallmarks of cancer to acquire functional capabilities, thus allowing cancer cells to survive, proliferate, and disseminate. Despite the importance of genome instability in cancer development, few studies have explored the prognostic signature associated with genome instability for lung adenocarcinoma. Methods: In the study, we randomly divided 397 lung adenocarcinoma patients from The Cancer Genome Atlas database into a training group (n = 199) and a testing group (n = 198). By calculating the cumulative counts of genomic alterations for each patient in the training group, we distinguished the top 25% and bottom 25% of patients. We then compared their gene expressions to identify genome instability-related genes. Next, we used univariate and multivariate Cox regression analyses to identify the prognostic signature. We also performed the Kaplan-Meier survival analysis and the log-rank test to evaluate the performance of the identified prognostic signature. The performance of the signature was further validated in the testing group, in The Cancer Genome Atlas dataset, and in external datasets. We also conducted a time-dependent receiver operating characteristic analysis to compare our signature with established prognostic signatures to demonstrate its potential clinical value. Results: We identified GULPsig, which includes IGF2BP1, IGF2BP3, SMC1B, CLDN6, and LY6K, as a prognostic signature for lung adenocarcinoma patients from 42 genome instability-related genes. Based on the risk score of the risk model with GULPsig, we successfully stratified the patients into high- and low-risk groups according to the results of the Kaplan-Meier survival analysis and the log-rank test. We further validated the performance of GULPsig as an independent prognostic signature and observed that it outperformed established prognostic signatures. Conclusion: We provided new insights to explore the clinical application of genome instability and identified GULPsig as a potential prognostic signature for lung adenocarcinoma patients.

The NF-κB Transcriptional Network Is a High-Dose Vitamin C-Targetable Vulnerability in Breast Cancer

Breast cancer (BC) is the most common cancer type among women with a distinct clinical presentation, but the survival rate remains moderate despite advances in multimodal therapy. Consequently, a deeper understanding of the molecular etiology is required for the development of more effective treatments for BC. The relationship between inflammation and tumorigenesis is well established, and the activation of the pro-inflammatory transcription factor nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) is frequently identified in BC. Constitutive NF-κB activation is linked to cell survival, metastasis, proliferation, and hormonal, chemo-, and radiotherapy resistance. Moreover, the crosstalk between NF-κB and other transcription factors is well documented. It is reported that vitamin C plays a key role in preventing and treating a number of pathological conditions, including cancer, when administered at remarkably high doses. Indeed, vitamin C can regulate the activation of NF-κB by inhibiting specific NF-κB-dependent genes and multiple stimuli. In this review, we examine the various NF-κB impacts on BC development. We also provide some insight into how the NF-κB network may be targeted as a potential vulnerability by using natural pro-oxidant therapies such as vitamin C.

Prognostic Capability of TNBC 3-Gene Score among Triple-Negative Breast Cancer Subtypes

Background: Triple-negative breast cancer (TNBC) is a complex and molecularly heterogeneous entity, with the poorest outcome compared with other breast cancer subtypes. Previously, we developed a TNBC 3-gene score with a significant prognostic capability. This study aims to test the 3-gene score in the different TNBC subtypes. Methods: Data from 204 TNBC patients treated with neoadjuvant chemotherapy were retrieved from public datasets and pooled (GSE25066, GSE58812, and GSE16446). After removing batch effects, cases were classified into Lehman’s TNBC subtypes and then the TNBC 3-gene score was used to evaluate the risk of distant recurrence in each subgroup. In addition, the association with tumor-infiltrating lymphocyte (TILs) levels was evaluated in a retrospective group of 72 TNBC cases. Results: The TNBC 3-gene score was able to discriminate patients with different risks within the pooled cohort (HR = 2.41 for high vs. low risk; 95%CI: 1.50−3.86). The score showed predictive capability in the immunomodulatory subtype (HR = 4.16; 95%CI: 1.63−10.60) and in the mesenchymal stem-like subtype (HR = 18.76; 95%CI: 1.68−208.97). In the basal-like 1, basal-like-2, and mesenchymal subtypes, the observed differential risk patterns showed no statistical significance. The score had poor predictive capability in the luminal androgen receptor subtype (p = 0.765). In addition, a low TNBC 3-gene score was related to a high level of TIL infiltration (p < 0.001). Conclusions: The TNBC 3-gene score is able to predict the risk of distant recurrence in TNBC patients, specifically in the immunomodulatory and mesenchymal stem-like subtype. Despite a small sample size in each subgroup, an improved prognostic capability was seen in TNBC subtypes with tumor-infiltrating components.

A novel risk model based on immune response predicts clinical outcomes and characterizes immunophenotypes in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is highly heterogeneous in prognosis. The current TNM staging system shows its limitation in accurate risk evaluation. Immune response and immune cell abundances in the tumor immune microenvironment (TIME) are critical for cancer progression, clinical outcome and therapeutic response in TNBC. However, there is a lack of an effective risk model based on the overall transcriptional alterations relevant to different immune responses. In this study, multiple bioinformatics and statistical approaches were used to develop an immune-related risk (IRR) signature based on the differentially expressed genes between the immune-active and immune-inactive samples. The IRR model showed great performance in risk stratification, immune landscape evaluation and immunotherapy response prediction. Compared with the low-IRR group, the high-IRR group exhibited a poorer prognosis, less cytotoxic cell infiltration, higher M2/M1 ratio and upregulated glycolytic activity. Moreover, the high-IRR group showed more resistance to immunotherapy than the low-IRR group. Our study reveals that the IRR model may be a promising tool to help clinicians assess risk and optimize treatment for TNBC patients.

SPDL1 Is an Independent Predictor of Patient Outcome in Colorectal Cancer

Spindle Apparatus Coiled-Coil Protein 1 (SPDL1) is a relatively recently identified coiled-coil domain containing protein and an important determinant of DNA fidelity by ensuring faithful mitosis. Hence, SPDL1 is suspected to underlie genomic (in-)stability in human cancers, yet its exact roles in these diseases remain largely underexplored. Given that genomic instability (GIN) is a crucial feature in colorectal cancer (CRC), we primarily asked whether the expression of this protein may account for differences in clinicopathological features and survival rates of CRC patients. Protein expression was evaluated by immunohistochemistry in the institutional tissue microarray (TMA), and gene expression by the analysis of publicly available datasets. To place the prognostic relevance in a predicted biological context, gene co-expression set around SPDL1 identified by public data mining was annotated and assessed for enrichment in gene ontology (GO) categories, BRITE hierarchies, and Reactome pathways. The comparison with adjacent normal tissue revealed a high expression of SPDL1 protein in a subset of tumor cases (48.84%), and these had better prognosis than the SPDL1-low expression counterpart even after adjustment for multiple confounders. SPDL1-high expression within tumors was associated with a median 56-month survival advantage, but not with any clinicopathological characteristics of our cohort. In the TCGA cohort, SPDL1 was overexpressed in tumor tissue and positively associated with improved survival, chromosome instability phenotype, and various GIN markers. In addition to the genes critically involved in the cell cycle and mitosis, a gene set co-expressed with SPDL1 contained checkpoint members of both chromosome segregation and DNA replication, as well as those associated with defective DNA repair, and retrograde vesicle-mediated transport. In conclusion, SPDL1 is an independent predictor of CRC patient survival in a possible connection with chromosomal instability.

Genomic Instability-Related LncRNA Signature Predicts the Prognosis and Highlights LINC01614 Is a Tumor Microenvironment-Related Oncogenic lncRNA of Papillary Thyroid Carcinoma

Background

Genomic instability (GI) is among the top ten characteristics of malignancy. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in GI. So far, the clinical value of GI-related lncRNAs (GIlncs) in papillary thyroid cancer (PTC) has not been clarified.

Methods

Integrative analysis of lncRNA expression and somatic mutation profiles was performed to identify GIlncs. Analysis of differentially expressed lncRNAs in the group with high- and low- cumulative number of somatic mutations revealed significant GIlncs in PTC. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify hub-GIlncs.

Results

A computational model based on four lncRNAs (FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1) was identified as a quantitative index using an in-silicon discovery cohort. GILS score was significantly associated with poor prognosis, as validated in the TCGA dataset and further tested in our local RNA-Seq cohort. Moreover, a combination of clinical characteristics and the composite GILS-clinical prognostic nomogram demonstrates satisfactory discrimination and calibration. Furthermore, the GILS score and FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 were also associated with driver mutations and multiple clinical-pathological variables, respectively. Moreover, RNA-Seq confirmed the expression patterns of FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 in PTC and normal thyroid tissues. Biological experiments demonstrated that downregulated or overexpressed LINC01614 affect PTC cell proliferation, migration, and invasion in vitro. Activation of the stromal and immune cell infiltration was also observed in the high LINC01614 group in the PTC microenvironment.

Conclusion

In summary, we identified a signature for clinical outcome prediction in PTC comprising four lncRNAs associated with GI. A better understanding of the GI providing an alternative evaluation of the progression risk of PTC. Our study also demonstrated LINC01614 as a novel oncogenic lncRNA and verified its phenotype in PTC.