As key regulators of apoptosis, BAD and defender against apoptotic cell death 1 (DAD1) are associated with cancer initiation and progression. Multiple studies have demonstrated that BAD and DAD1 serve critical roles in several types of cancer and perform various functions, such as participating in cellular apoptosis, invasion and chemosensitivity, as well as their role in diagnostic/prognostic judgement, etc. Investigating the detailed mechanisms of the cancerous effects of the two proteins will contribute to enriching the options for targeted therapy, and may improve clinical treatment of cancer. The present review summarizes research advances regarding the associations of BAD and DAD1 with cancer, and a hypothesis on the feasible relationship and interaction mechanism between the two proteins is proposed. Furthermore, the present review highlights the potential of the two proteins as therapeutic targets and valuable diagnostic and prognostic biomarkers.

IntroductionFemale breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.MethodsA total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.ResultsA 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.ConclusionMultidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

IntroductionFemale breast cancer has risen to be the most common malignancy worldwide, causing a huge disease burden for both patients and society. Both senescence and oxidative stress attach importance to cancer development and progression. However, the prognostic roles of senescence and oxidative stress remain obscure in breast cancer. In this present study, we attempted to establish a predictive model based on senescence-oxidative stress co-relation genes (SOSCRGs) and evaluate its clinical utility in multiple dimensions.MethodsSOSCRGs were identified via correlation analysis. Transcriptome data and clinical information of patients with breast invasive carcinoma (BRCA) were accessed from The Cancer Genome Atlas (TCGA) and GSE96058. SVM algorithm was employed to process subtype classification of patients with BRCA based on SOSCRGs. LASSO regression analysis was utilized to establish the predictive model based on SOSCRGs. Analyses of the predictive model with regards to efficacy evaluation, subgroup analysis, clinical association, immune infiltration, functional strength, mutation feature, and drug sensitivity were organized. Single-cell analysis was applied to decipher the expression pattern of key SOSCRGs in the tumor microenvironment. Additionally, qPCR was conducted to check the expression levels of key SOSCRGs in five different breast cancer cell lines.ResultsA total of 246 SOSCRGs were identified. Two breast cancer subtypes were determined based on SOSCRGs and subtype 1 showed an active immune landscape. A SOSCRGs-based predictive model was subsequently developed and the risk score was clarified as independent prognostic predictors in breast cancer. A novel nomogram was constructed and exhibited favorable predictive capability. We further ascertained that the infiltration levels of immune cells and expressions of immune checkpoints were significantly influenced by the risk score. The two risk groups were characterized by distinct functional strengths. Sugar metabolism and glycolysis were significantly upregulated in the high risk group. The low risk group was deciphered to harbor PIK3CA mutation-driven tumorigenesis, while TP53 mutation was dominant in the high risk group. The analysis further revealed a significantly positive correlation between risk score and TMB. Patients in the low risk group may also sensitively respond to several drug agents. Single-cell analysis dissected that ERRFI1, ETS1, NDRG1, and ZMAT3 were expressed in the tumor microenvironment. Moreover, the expression levels of the seven SOSCRGs in five different breast cancer cell lines were quantified and compared by qPCR respectively.ConclusionMultidimensional evaluations verified the clinical utility of the SOSCRGs-based predictive model to predict prognosis, aid clinical decision, and risk stratification for patients with breast cancer.

Backgrounds: Collagen is the main component in extracellular matrix. More and more researches have determined the oncogenic effect of collagen in cancer progression, which is intriguing to be further explored. Collagen type ⅩⅩⅥ alpha 1 chain (COL26A1) is a newly discovered collagen subtype, functions of which still remain poorly demonstrated. No studies have reported the roles of COL26A1 in human cancers ever before. Thus we tried to initially explore the potential associations between COL26A1 and thyroid carcinoma (THCA), in an attempt to enrich the relevant literature, serving as basis for further elucidation and experimental validation. Results: We processed a series of bioinformatic analysis for exploration, which mainly distributed to 4 aspects: Prognostic/diagnostic prediction, functional characterization, immunological target and ceRNA network. We found that high expression level of COL26A1 infers poor prognosis for patients with THCA. The aberrant expression of COL26A1 could be applied as diagnostic and prognostic biomarker with a certain degree of accuracy. Besides, a novel nomogram containing several independent prognostic factors was established to predict the survival probability of patients with THCA. Functional characterizations implied that COL26A1 is tightly associated with immunological processes, as well as several oncogenic signaling pathways. Subsequently, we determined that high COL26A1 expression is accompanied by higher infiltration levels of multiple immune cells and higher stromal/immune score. Also, the significantly positive correlations of COL26A1 expression with infiltration levels of multiple immune cells and stromal/immune score were determined. In addition, we identified that COL26A1 is significantly positively co-expressed with most immune checkpoints, including PD1, PD-L1, TIGIT, LAG3 and CTLA4. The drugs that can decrease the expression level of COL26A1 were also identified. The predicted lncRNA-miRNA-COL26A1 regulatory axes were displayed. Conclusions: Our work has primarily appraised COL26A1 as a promising biomarker for diagnosis/prognosis and target for immunotherapy in THCA.