Identification of tumor heterogeneity associated with KRAS/TP53 co-mutation status in lung adenocarcinoma based on single-cell RNA sequencing.

Lung cancer stands as the predominant cause of cancer-related mortality globally. Lung adenocarcinoma (LUAD), being the most prevalent subtype, garners extensive attention due to its notable heterogeneity, which significantly influences tumor development and treatment approaches. This research leverages single-cell RNA sequencing (scRNA-seq) datasets to delve into the impact of KRAS/TP53 co-mutation status on LUAD. Moreover, utilizing the TCGA-LUAD dataset, we formulated a novel predictive risk model, comprising seven prognostic genes, through LASSO regression, and subjected it to both internal and external validation sets. The study underscores the profound impact of KRAS/TP53 co-mutational status on the tumor microenvironment (TME) of LUAD. Crucially, KRAS/TP53 co-mutation markedly influences the extent of B cell infiltration and various immune-related pathways within the TME. The newly developed predictive risk model exhibited robust performance across both internal and external validation sets, establishing itself as a viable independent prognostic factor. Additionally, in vitro experiments indicate that MELTF and PLEK2 can modulate the invasion and proliferation of human non-small cell lung cancer cells. In conclusion, we elucidated that KRAS/TP53 co-mutations may modulate TME and patient prognosis by orchestrating B cells and affiliated pathways. Furthermore, we spotlight that MELTF and PLEK2 not only function as prognostic indicators for LUAD, but also lay the foundation for the exploration of innovative therapeutic approaches.

Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma.

Introduction: Female 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. Methods: SOSCRGs 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. Results: A 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. Conclusion: Multidimensional 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.

Prognosis stratification of patients with breast invasive carcinoma based on cysteine metabolism-disulfidptosis affinity.

PURPOSE: The rise of female breast cancer has created a significant global public health issue that requires effective solutions. Disulfidptosis, a recently identified form of cell death characterized by an excessive accumulation of disulfides, has unique initiatory and regulatory mechanisms. The formation of disulfide bonds is a metabolic event typically associated with cysteines. This study aims to explore the potential of the affinity between cysteine metabolism and disulfidptosis in risk stratification for breast invasive carcinoma (BRCA). METHODS: We used correlation analysis to decipher co-relation genes between cysteine metabolism and disulfidptosis (CMDCRGs). Both LASSO regression analysis and multivariate Cox regression analysis were employed to construct the prognostic signature. Additionally, we conducted investigations concerning subtype identification, functional enhancement, mutation landscape, immune infiltration, drug prioritization, and single-cell analysis. RESULTS: We developed and validated a six-gene prognostic signature as an independent prognostic predictor for BRCA. The prognostic nomogram, based on risk score, demonstrated a favorable capability in predicting survival outcomes. We identified distinct gene mutations, functional enhancements, and immune infiltration patterns between the two risk groups. Four clusters of drugs were predicted as potentially effective for patients in the low-risk group. We identified seven cell clusters within the tumor microenvironment of breast cancer, and RPL27A was found to be widely expressed in this environment. CONCLUSION: Multidimensional analyses confirmed the clinical utility of the cysteine metabolism-disulfidptosis affinity-based signature in risk stratification and guiding personalized treatment for patients with BRCA.

A degradome-based prognostic signature that correlates with immune infiltration and tumor mutation burden in breast cancer.

Introduction: Female 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. Methods: A 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. Results: A 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. Conclusion: Multidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

New insights into COL26A1 in thyroid carcinoma: prognostic prediction, functional characterization, immunological drug target and ceRNA network.

Background: Thyroid carcinoma (THCA) is one of the most commonly diagnosed malignancies. Collagen is the main component in extracellular matrix. Rising studies 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 in THCA. Methods: Based on the transcriptome data from The Cancer Genome Atlas (TCGA) and other public databases, we conducted investigations of COL26A1 in THCA with respects to diagnostic/prognostic prediction, functional characterization, immune infiltration, chemical drug target and non-coding RNA regulatory network. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were used to verify the expression of COL26A1 in THCA. Results: COL26A1 was significantly upregulated in THCA, and the high COL26A1 expression inferred poor prognosis [hazard ratio (HR) =4.76; 95% confidence interval (CI): 1.36-16.73; P=0.015]. The diagnostic area under the curve (AUC) of COL26A1 achieved 0.736 (95% CI: 0.669-0.802). COL26A1 was also identified as an independent prognostic predictor for THCA (HR =3.928; 95% CI: 3.716-4.151; P<0.001). Besides, logistic regression analysis indicated that age >45 years [odds ratio (OR) =1.532; 95% CI: 1.081-2.176; P=0.017], pathological stage III (OR =2.055; 95% CI: 1.314-3.184; P=0.001), tall cell subtype (OR =5.533; 95% CI: 2.420-14.957; P<0.001), residual tumor R1 (OR =1.844; 95% CI: 1.035-3.365; P=0.041) and extrathyroidal extension (OR =1.800; 95% CI: 1.225-1.660; P=0.003) were risk factors associated with high COL26A1 expression in THCA. Functional characterizations implied that COL26A1 was associated with immunological processes and oncogenic signaling pathways. High COL26A1 expression was accompanied by more abundant infiltration of immune cells and higher stromal/immune score. In addition, most immune checkpoints were significantly positively co-expressed with COL26A1, including PD-1, PD-L1 and CTLA4. Drugs including trichloroethylene, acetamide and thioacetamide etc. that can decrease the expression of COL26A1 were also identified. The predicted long noncoding RNA (lncRNA)-microRNA (miRNA)-COL26A1 regulatory axes were successfully deciphered. qRT-PCR and western blot verified the upregulation of COL26A1 in THCA. Conclusions: Our work has primarily appraised COL26A1 as a promising biomarker for diagnosis/prognosis and immuno/therapeutic target in THCA.

Emerging role of BAD and DAD1 as potential targets and biomarkers in cancer.

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.