Survival Rate Of Cancer Patients Research Articles

Absolute quantification of a plasma tRNA-derived fragment for the diagnosis and prognosis of gastric cancer.

The transition from a healthy gastric mucosa to gastric cancer is a multi-step process. Early screening can significantly improve the survival rate of gastric cancer patients. A reliable liquid biopsy for gastric cancer prediction is urgently needed and since tRNA-derived fragments (tRFs) are abundant in various body fluids, tRFs are possible new biomarkers for gastric cancer. A total of 438 plasma samples from patients with different gastric mucosal lesions as well as healthy individuals were collected. A specific reverse transcription primer, a forward primer, a reverse primer, and a TaqMan probe were designed. A standard curve was constructed and an absolute quantitation method was devised for detection of tRF-33-P4R8YP9LON4VDP in plasma samples of individuals with differing gastric mucosa lesions. Receiver operating characteristic curves were constructed to evaluate the diagnostic values of tRF-33-P4R8YP9LON4VDP for individual with differing gastric mucosa. A Kaplan-Meier curve was established to calculate the prognostic value of tRF-33-P4R8YP9LON4VDP for advanced gastric cancer patients. Finally, a multivariate Cox regression analysis was performed to assess the independent prognostic value of tRF-33-P4R8YP9LON4VDP for advanced gastric cancer patients. A detection method for plasma tRF-33-P4R8YP9LON4VDP was successfully established. Levels of plasma tRF-33-P4R8YP9LON4VDP were shown to reflect a gradient change from healthy individuals to gastritis patients to early and advanced gastric cancer patients. Significant differences were found among individuals with differing gastric mucosa, with reduced levels of tRF-33-P4R8YP9LON4VDP significantly related to a poor prognosis. tRF-33-P4R8YP9LON4VDP was found to be an independent predictor of an unfavorable survival outcome. In this study, we developed a quantitative detection method for plasma tRF-33-P4R8YP9LON4VDP that exhibited hypersensitivity, convenience, and specificity. Detection of tRF-33-P4R8YP9LON4VDP was found to be a valuable means by which to monitor different gastric mucosa and to predict patient prognosis.

Inhibitory Effect and Mechanism of Ursolic Acid on Cisplatin-Induced Resistance and Stemness in Human Lung Cancer A549 Cells.

The survival rate of lung cancer patients remains low largely due to chemotherapy resistance during treatment, and cancer stem cells (CSCs) may hold the key to targeting this resistance. Cisplatin is a chemotherapy drug commonly used in cancer treatment, yet the mechanisms of intrinsic cisplatin resistance have not yet been determined because lung CSCs are hard to identify. In this paper, we proposed a mechanism relating to the function of ursolic acid (UA), a new drug, in reversing the cisplatin resistance of lung cancer cells regulated by CSCs. Human lung cancer cell line A549 was selected as the model cell and treated to become a cisplatin-resistant lung cancer cell line (A549-CisR), which was less sensitive to cisplatin and showed an enhanced capability of tumor sphere formation. Furthermore, in the A549-CisR cell line expression, levels of pluripotent stem cell transcription factors Oct-4, Sox-2, and c-Myc were increased, and activation of the Jak2/Stat3 signaling pathway was promoted. When UA was applied to the cisplatin-resistant cells, levels of the pluripotent stem cell transcription factors were restrained by the inhibition of the Jak2/Stat3 signaling pathway, which reduced the enrichment of tumor stem cells, and in turn, reversed cisplatin resistance in lung cancer cells. Hence, as a potential antitumor drug, UA may be able to inhibit the enrichment of the lung CSC population by inhibiting the activation of the Jak2-Stat3 pathway and preventing the resistance of lung cancer cells to cisplatin.

Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information

Breast cancer is the world’s second-largest cause of cancer mortality among women. With the progress of artificial intelligence (AI) in healthcare, the survival rate of breast cancer patients has risen in recent years due to early diagnosis and effective prognosis. However, substantial AI research necessitates a large quantity of high-quality data to perform credible state-of-the-art research. To that end, this study investigates the potentiality of deep generative models including, the tabular variational autoencoder (TVAE) and the conditional generative adversarial network (CTGAN), to generate high-quality synthetic tabular data of breast tumors and support the diagnosis and prognosis of breast cancer. Additionally, this study proposes an integrated interpretable deep-learning framework that includes the synthetic generation of breast cancer data leading to the classification of breast cancer using the interpretable deep attention-based model TabNet based on the domain of breast cancer research at every stage of the research framework. The research findings are justified using benchmark breast cancer datasets. After rigorous investigation, it was found that the TVAE model outperformed the synthetic generation of breast tumor data with a Chi-Squared test(CS test) score of 0.916 (prognosis) and 0.964 (diagnosis) and a Kolmogorov Smirnov test(KS test) score of 0.887 (prognosis) and 0.928 (diagnosis). In the classification stage, despite being trained with only synthetically generated data, the interpretable TabNet architecture outperformed all other machine-learning and deep-learning classifiers with an accuracy of 96.66 % in diagnosis and 82.83 % in prognosis.

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network.

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

Chinese Herbal Medicine Improved Survival in Stage IV Breast Cancer Patients: Data Mining of the Incorporated Taiwan Cancer Registry Database and Hospital Database.

According to recent research, breast cancer has become the most commonly diagnosed non-skin cancer type among the entire population. Meanwhile, Chinese herbal medicine (CHM) has become an important therapy to improve the survival rate and quality of life in metastatic cancer patients, as part of a wider trend toward more personalized medicine. However, there is a lack of research in terms of the association between stage IV breast cancer and CHM. Therefore, this study aimed to investigate the association between CHM and the survival rate in breast cancer patients at different cancer stages, with a particular focus on patients diagnosed with stage IV. Patients with an initial diagnosis of breast cancer identified from the Taiwan Cancer Registry Database (TCRD) and China Medical University Hospital (CMUH) database were included in this study. Demographic characteristics including gender, age, and comorbidities were evaluated. Between-group differences for continuous and categorical variables were evaluated by the Student's t-test and Chi-square test, respectively. Patients diagnosed with breast cancer were recruited and divided into cohorts of CHM users and non-users with a 1:1 propensity score matching. The survival of breast cancer patients was assessed by the Cox proportional hazard model. The cumulative incidence of survival was assessed by Kaplan-Meier analysis. The adjuvant CHM treatment was associated with a higher survival rate in stage IV breast cancer patients (HR: 0.45, 95% CI: 0.2853-0.7044]. Additionally, the use of CHM had a positive impact on survival in stage IV breast cancer patients who had received surgery (P: .0273, HR: 0.3406, 95% CI: 0.1309-0.8865), chemotherapy (P: .0004, HR: 0.3893, 95% CI: 0.231-0.656), and hormone therapy (P: .0013, HR: 0.3491, 95% CI: 0.1836-0.6636). In terms of the specific CHM associated with survival, Zhi-Gan-Cao-Tang (ZGCT), Phellodendron chinense Schneid. (huang-bai), and Paeonia lactiflora Pall. (chi-shao) were the 3 most commonly prescribed herbal medicines correlated with a higher survival rate among stage IV breast cancer patients. CHM associated with conventional management achieved significant survival benefits in patients with stage IV breast cancer. Additional randomized controlled trials for prospective study are recommended for further validation.

The Importance of Spirituality, Spiritual Intelligence, Hope and Social Support in Dealing with Cancer Patients: Some Comments

Spirituality has been recognized as an important factor similar to hope and social support for improved quality of life (QoL) and survival rate in cancer patients. Literature has also demonstrated a higher relationship of survival rate, with higher spiritual intelligence and hope levels in cancerous patients than non-patients. Spiritual intelligence needs to be explored more deeply, to better understand how this construct affects cancer survival rates.

Bone Health Care Pathway for Non-metastatic Prostate Cancer Patients on Radiation and Androgen Deprivation Therapy.

Survival rates of prostate cancer (PCa) patients have improved considerably as a result of earlier diagnosis and therapies, including radiotherapy (RT) and androgen deprivation therapy (ADT). Patients on ADT develop cancer treatment-induced bone loss (CTIBL) and a high risk of fragility fractures. Bone health (BH) assessment is strongly recommended, together with timely initiation of treatments, to counteract CTIBL and preserve bone strength. Therefore, we decided to develop an interdisciplinary pathway of care (IPC) dedicated to non-metastatic PCa patients on long-term ADT and RT. An interdisciplinary team allocated resources to support an IPC to manage patients' CTIBL and prevent fragility fractures. The team provided a diagnostic and therapeutic workflow according to patients' and professional perspectives, consistent with recommendations and healthcare policies. The hospital's quality department certified the IPC, the Ethical Committee approved procedures over the workflow. The Fracture Liaison Service (FLS) standards inspired services and professionals' activities and interactions. Preliminary data support the feasibility of the IPC from professionals' and patients' perspectives. Median age of the enrolled patients was 75 years, more than a half (58.9%) had low grade osteopenia or normal BMD (T-score ≥-1.5 standard deviation, SD), while 23.5% and 17.6% had osteoporosis and osteopenia, respectively. The IPC meets the requirements of a FLS concerning crucial indicators. Our IPC was a suitable approach to assure timely identification, assessment, initiation, and monitoring of adherence to anti-fracture treatments among non-metastatic PCa patients on long-term ADT and RT. Further data are required to show its effectiveness on fragility fracture prevention.

Grading Classification of Tongue Cancer in Oral Images Using CNN Transfer Learning

- Oral cancer is one of the top 10 diseases in cancer incidence worldwide and its prognosis is not good as early diagnosis is not made and so the survival rate from it has not been improved greatly. Oral cancer occurs most commonly in tongue, floor of mouth, and lower lip and 5-year survival rate is very low at 50% and if diagnosed early, the average time of survival becomes longer. Therefore, to improve the survival rate of oral cancer patients, early diagnosis and discovery and patient habit improvement including smoking are very important. Oral cancer can be diagnosed with imaging equipment like CT and MRI or through invasive biopsy, but it is not easy to make an approach and generally difficult to discover early. Therefore, to identify if early diagnosis would be possible using the intraoral images obtained through ordinary camera, the reliability of tongue cancer grade classification was evaluated by applying deep learning analysis. The collected images of oral cavity were classified into four groups: normal, inflammatory lesion, pre-cancerous lesion, and malignant tumor depending on clinical importance and three (3) types of image pretreatment were applied. In case of deep neural network, Inception-ResNet-v2 was used and 70%(1,307 images) of the pretreated images were used as training data and after image enhancement processing, transfer-learned with SGDM technique. As test data, the remaining 30% data (561 images) were used. In confusion matrix, it appeared that sensitivity was 90.37%, specificity 95.03%, accuracy 93.21%, positive predictability 92.06%, and negative predictability 93.93%, and in ROC curve, AUC was normal because normal was 0.9781, inflammatory lesion 0.9102, precancerous lesion 0.8944, and malignant tumor 0.9688, and the classification performance of inflammatory lesion or pre-cancerous lesion was inferior to that of malignant tumor. The accuracy depending on pretreatment did not show a statistically significant difference in HE-1 and HE-2 compared to standard image. The performance of the diagnostic classifier applying deep learning into intraoral images showed accuracy at the average rate of 82.9% and so showed more excellent performance than the precedent studies using different RGB images. Deep learning study using such RGB images tends to be rapidly commercialized as it has disclosed big data whose images were easy to obtain and that were preclassified and so noninvasive data can be easily obtained unlike biopsy or blood test. Intraoral image does not have preclassified and disclosed big data yet, but have a distortion in an artifact or lesion in obtaining an image and so if we can standardize the image acquisition and organize big data through correct preclassification in cooperation with clinicians although this is limited, it will be helpful for early diagnosis of tongue cancer.

Integrated proteome and phosphoproteome analysis of gastric adenocarcinoma reveals molecular signatures capable of stratifying patient outcome.

Metastasis is one of the main causes of low survival rate of gastric cancer patients. Exploring key proteins in the progression of gastric adenocarcinoma (GAC) may provide new candidates for prognostic biomarker development and therapeutic intervention. We applied quantitative mass spectrometry to compare the proteome and phosphoproteome of primary tumor tissues between GAC patients with and without lymph node metastasis (LNM). We then performed an integrated analysis of the proteomic and transcriptomic data to reveal the molecular features. We quantified a total of 5536 proteins, and we found 218 upregulated and 49 downregulated proteins in tumor samples from patients with LNM compared to those without LNM. Clustering analysis identified a number of hub proteins that have been previously shown to play important roles in gastric cancer progression. We also found that two extracellular proteins, TNXB and SPON1, are overexpressed in patients with LNM, which correlates with poor survival of GAC patients. Overexpression of TNXB and SPON1 was validated by western blotting and immunohistochemistry. Furthermore, treating gastric cancer cells with anti-TNXB antibody significantly reduced cell migration. Finally, quantitative phosphoproteomic analysis combined with activity-based kinase capture revealed a number of activated kinases in primary tumor tissues from patients with LNM, among which GSK3 might be a new target that warrants further study. Our study provides a snapshot of the proteome and phosphoproteome of GAC tumor tissues that have metastatic potential, and identifies potential biomarkers for GAC progression.

Atrous convolution aided integrated framework for lung nodule segmentation and classification

Lung cancer has been recognized as the most life-threatening cancer all over the world. Appropriate detection of lung nodule using Computed Tomography (CT) images helps in early stage recognition of lung cancer. Different computer-aided algorithms play an important role in the early diagnosis of lung cancer and can increase the five-year survival rate of lung cancer patients. However, due to structural similarity, manually recognizing the malignant nodule from the benign is time-consuming and challenging task. Recently different deep learning (DL) based Computer-aided diagnosis (CADx) systems have been developed for lung nodule characterization. In this work, an integrated nodule segmentation and characterization framework has been developed using the concept of atrous convolution. The proposed Atrous Convolution-based Convolutional Neural Network (ATCNN) framework can segment and characterize lung nodules by capturing multi-scale features from the HRCT images. Different variants of the ATCNN framework have been analyzed for lung nodule characterization. Among them, ATCNN with a two-layer atrous pyramid and residual connections (ATCNN2PR) has demonstrated the highest classification performance indices for nodule characterization. The new ATCNN2PR framework has obtained an average Dice Similarity Coefficient (DSC), Jaccard Index (JI), and Boundary F1 (BF) score of 0.9715, 0.9520, and 0.9584 for nodule segmentation and sensitivity, specificity, accuracy of 95.84%, 96.89%, and 95.97% for lung nodule characterization on LIDC-IDRI dataset. The proposed automatic trainable end-to-end system has outperforms other competing frameworks by capturing multi-scale features from High-Resolution Computed Tomography (HRCT) nodule images.

Long-term trend of colorectal cancer survival rate in Qidong, 1972-2016

Objective: The survival of colorectal cancer in Qidong City, Jiangsu Province from 1972 to 2016 was analyzed to provide a basis for the evaluation of prognosis and the formulation of prevention and control measures. Methods: Colorectal cancer data were obtained from the Qidong Cancer Registration and Reporting System, and the follow-up was up to December 31, 2021. The observed survival rate (OSR) and relative survival rate (RSR) were calculated by SURV 3.01 software, and the trend test was performed by Hakulinen's likelihood ratio test. The Joinpoint regression model was used to calculate the average annual percentage change (AAPC) of survival, and the ARIMA model was used to predict the trend of colorectal cancer survival. Results: There were 8 637 new cases of colorectal cancer in Qidong from 1972 to 2016. Dividing 1972-2016 into 9 periods at 5-year intervals, the 5-year OSR from 1972-1976 to 2012-2016 increased from 21.86% to 48.86%, and the 5-year RSR increased from 26.45% to 59.91%. The increasing trend of RSR was statistically significant (χ(2)=587.47, P<0.001). From 1972 to 2016, the survival rates of colorectal cancer in different sexes in Qidong were similar, and the 5-year RSR was 44.63% for men and 44.07% for women. Since the 1990s, the 5-year OSR and RSR for men have been lower than those for women. From 1972 to 2016, the 5-year RSR of colorectal cancer in Qidong was significantly improved in the 65-74 and ≥75-year-old groups, but the survival rate of the ≥75-year-old group was still the lowest (36.78%), followed by the 35-44-year-old group ( 43.04%). The time trend showed that the overall AAPC of colorectal cancer 5-year RSR in Qidong from 1972 to 2016 was 2.50% (t=16.45, P<0.001). The upward trend of different sexes was consistent, and the increase was greater in women (AAPC for males=2.18%, AAPC for females=2.54%, both P<0.05). The 5-year RSR of colorectal cancer in each age group showed an upward trend, and the AAPCs of the 35-44, 45-54, 55-64, 65-74, and ≥75-year-old groups were 1.54%, 1.83%, 2.00%, 3.51% and 4.35%, respectively (all P<0.05). The prediction results of colorectal cancer survival rate showed that the 5-year RSR of colorectal cancer in Qidong will increase to 71.62% by 2026. Conclusions: The overall survival rate of colorectal cancer patients in Qidong has been greatly improved, but there is still room for improvement. We should continue to pay attention to the early diagnosis and early treatment of colorectal cancer.

Cancer-Associated Fibroblast Diversity Shapes Tumor Metabolism in Pancreatic Cancer

Despite extensive research, the 5-year survival rate of pancreatic cancer (PDAC) patients remains at only 9%. Patients often show poor treatment response, due partly to a highly complex tumor microenvironment (TME). Cancer-associated fibroblast (CAF) heterogeneity is characteristic of the pancreatic TME, where several CAF subpopulations have been identified, such as myofibroblastic CAFs (myCAFs), inflammatory CAFs (iCAFs), and antigen presenting CAFs (apCAFs). In PDAC, cancer cells continuously adapt their metabolism (metabolic switch) to environmental changes in pH, oxygenation, and nutrient availability. Recent advances show that these environmental alterations are all heavily driven by stromal CAFs. CAFs and cancer cells exchange cytokines and metabolites, engaging in a tight bidirectional crosstalk, which promotes tumor aggressiveness and allows constant adaptation to external stress, such as chemotherapy. In this review, we summarize CAF diversity and CAF-mediated metabolic rewiring, in a PDAC-specific context. First, we recapitulate the most recently identified CAF subtypes, focusing on the cell of origin, activation mechanism, species-dependent markers, and functions. Next, we describe in detail the metabolic crosstalk between CAFs and tumor cells. Additionally, we elucidate how CAF-driven paracrine signaling, desmoplasia, and acidosis orchestrate cancer cell metabolism. Finally, we highlight how the CAF/cancer cell crosstalk could pave the way for new therapeutic strategies.

Pulmonary Nodule Detection Based on Multiscale Feature Fusion.

As cancer with the highest morbidity and mortality in the world, lung cancer is characterized by pulmonary nodules in the early stage. The detection of pulmonary nodules is an important method for the early detection of lung cancer, which can greatly improve the survival rate of lung cancer patients. However, the accuracy of conventional detection methods for lung nodules is low. With the development of medical imaging technology, deep learning plays an increasingly important role in medical image detection, and pulmonary nodules can be accurately detected by CT images. Based on the above, a pulmonary nodule detection method based on deep learning is proposed. In the candidate nodule detection stage, the multiscale features and Faster R-CNN, a general-purpose detection framework based on deep learning, were combined together to improve the detection of small-sized lung nodules. In the false-positive nodule filtration stage, a 3D convolutional neural network based on multiscale fusion is designed to reduce false-positive nodules. The experiment results show that the candidate nodule detection model based on Faster R-CNN integrating multiscale features has achieved a sensitivity of 98.6%, 10% higher than that of the other single-scale model, the proposed method achieved a sensitivity of 90.5% at the level of 4 false-positive nodules per scan, and the CPM score reached 0.829. The results are higher than methods in other works of literature. It can be seen that the detection method of pulmonary nodules based on multiscale fusion has a higher detection rate for small nodules and improves the classification performance of true and false-positive pulmonary nodules. This will help doctors when making a lung cancer diagnosis.

Probing Folate-Responsive and Stage-Sensitive Metabolomics and Transcriptional Co-Expression Network Markers to Predict Prognosis of Non-Small Cell Lung Cancer Patients

Tumour metabolomics and transcriptomics co-expression network as related to biological folate alteration and cancer malignancy remains unexplored in human non-small cell lung cancers (NSCLC). To probe the diagnostic biomarkers, tumour and pair lung tissue samples (n = 56) from 97 NSCLC patients were profiled for ultra-performance liquid chromatography tandem mass spectrometry (UPLC/MS/MS)-analysed metabolomics, targeted transcriptionomics, and clinical folate traits. Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Tumour lactate was identified as the top VIP marker to predict advance NSCLC (AUC = 0.765, Sig = 0.017, CI 0.58-0.95). Low folate (LF)-tumours vs. adjacent lungs displayed higher glycolytic index of lactate and glutamine-associated amino acids in enriched biological pathways of amino sugar and glutathione metabolism specific to advance NSCLCs. WGCNA classified the green module for hub serine-navigated glutamine metabolites inversely associated with tumour and RBC folate, which module metabolites co-expressed with a predominant up-regulation of LF-responsive metabolic genes in glucose transport (GLUT1), de no serine synthesis (PHGDH, PSPH, and PSAT1), folate cycle (SHMT1/2 and PCFR), and down-regulation in glutaminolysis (SLC1A5, SLC7A5, GLS, and GLUD1). The LF-responsive WGCNA markers predicted poor survival rates in lung cancer patients, which could aid in optimizing folate intervention for better prognosis of NSCLCs susceptible to folate malnutrition.

Immunotherapeutics in lung cancers: from mechanistic insight to clinical implications and synergistic perspectives.

Lung cancer is one of the highly lethal forms of cancer whose incidence has worldwide rapidly increased over the past few decades. About 80-85% of all lung cancer cases constitute non-small cell lung cancer (NSCLC), with adenocarcinoma, squamous cell carcinoma and large cell carcinoma as the main subtypes. Immune checkpoint inhibitors have led to significant advances in the treatment of a variety of solid tumors, significantly improving cancer patient survival rates. The cytotoxic drugs in combination with anti-PD-(L)1 antibodies is a new method that aims to reduce the activation of immunosuppressive and cancer cell prosurvival responses while also improving direct cancer cell death. The most commonly utilized immune checkpoint inhibitors for patients with non-small cell lung cancer are monoclonal antibodies (Atezolizumab, Cemiplimab, Ipilimumab, Pembrolizumab etc.) against PD-1, PD-L1, and CTLA-4. Among them, Atezolizumab (TECENTRIQ) and Cemiplimab (Libtayo) are engineered monoclonal anti programmed death ligand 1 (PD-L1) antibodies that inhibit binding of PD-L1 to PD-1 and B7.1. As a result, T-cell proliferation and cytokine synthesis are inhibited leading to restoring the immune homeostasis to fight cancer cells. In this review article, the path leading to the introduction of immunotherapeutic options in lung cancer treatment is described, with analyzing the benefits and shortages of the current immunotherapeutic drugs. In addition, possibilities to co-administer immunotherapeutic agents with standard cancer treatment modalities are also considered.

SiAKR1C3@PPA complex nucleic acid nanoparticles inhibit castration-resistant prostate cancer in vitro.

AKR1C3, as a crucial androgenic enzyme, implicates the androgen biosynthesis and promoting prostate cancer cell growth in vitro. This study provides a new gene therapy strategy for targeting AKR1C3 to treat castration-resistant prostate cancer. siAKR1C3@PPA is assembled from PEG3500, PAMAM, Aptamer-PSMA, and siRNA for AKR1C3. We analyzed the relationship between AKR1C3 expression and the survival rate of prostate cancer patients based on the GEPIA online database to perform disease-free survival, and found that AKR1C3 may be an important factor leading to poor prognosis in prostate cancer. Considering AKR1C3 as a therapeutic target for castration-resistant prostate cancer, we constructed a complex nucleic acid nanoparticle, siAKR1C3@PPA to investigate the inhibitory effect on castration-resistant prostate cancer. Aptamer-PSMA acts as a target to guide siAKR1C3@PPA into PSMA-positive prostate cancer cells and specifically down regulate AKR1C3. Cyclin D1 was decreased as a result of siAKR1C3@PPA treatment. Changes in Cyclin D1 were consistent with decreased expression of AKR1C3 in LNCaP-AKR1C3 cells and 22RV1 cells. Furthermore, in the LNCaP-AKR1C3 group, 1070 proteins were upregulated and 1015 proteins were downregulated compared to the LNCaP group according to quantitative 4D label-free proteomics. We found 42 proteins involved in cell cycle regulation. In a validated experiment, we demonstrated that PCNP and CINP were up-regulated, and TERF2 and TP53 were down-regulated by western blotting. We concluded that siAKR1C3@PPA may arrest the cell cycle and affect cell proliferation.

Computational Prediction of Resistance Induced Alanine-Mutation in ATP Site of Epidermal Growth Factor Receptor.

Epidermal growth factor receptor (EGFR) resistance to tyrosine kinase inhibitors can cause low survival rates in mutation-positive non-small cell lung cancer patients. It is necessary to predict new mutations in the development of more potent EGFR inhibitors since classical and rare mutations observed were known to affect the effectiveness of the therapy. Therefore, this research aimed to perform alanine mutagenesis scanning on ATP binding site residues without COSMIC data, followed by molecular dynamic simulations to determine their molecular interactions with ATP and erlotinib compared to wild-type complexes. Based on the result, eight mutations were found to cause changes in the binding energy of the ATP analogue to become more negative. These included G779A, Q791A, L792A, R841A, N842A, V843A, I853A, and D855A, which were predicted to enhance the affinity of ATP and reduce the binding ability of inhibitors with the same interaction site. Erlotinib showed more positive energy among G779A, Q791A, I853A, and D855A, due to their weaker binding energy than ATP. These four mutations could be anticipated in the development of the next inhibitor to overcome the incidence of resistance in lung cancer patients.

Protective Effect and Mechanism of Melatonin on Cisplatin-Induced Ovarian Damage in Mice.

Chemotherapeutics' development has enhanced the survival rate of cancer patients; however, adverse effects of chemotherapeutics on ovarian functions cause fertility loss in female cancer patients. Cisplatin (CP), an important chemotherapeutic drug for treating solid tumors, has adversely affected ovarian function. Melatonin (MT) has been shown to have beneficial effects on ovarian function owing to its antioxidative function. In this research, an animal model was established to explore the effect of MT on CP-induced ovarian damage. Immunohistochemical analysis and Western blot were also used to explore its mechanism. This study reported that MT protects mouse ovaries from CP-induced damage. Specifically, MT significantly prevented CP-induced ovarian reserve decline by maintaining AMH and BMP15 levels. We also found that MT ameliorated CP-induced cell cycle disorders by up-regulating CDC2 expression, and inhibited CP-induced ovarian inflammation by decreasing IL-1β and IL-18 levels. Moreover, MT protected the ovary from CP-induced mitochondrial damage, as reflected by restoring mitochondria-related protein expression. Furthermore, CP caused ovarian apoptosis, as indicated by up-regulated BAX expression. MT was also shown to activate the MAPK pathway. Our results showed that MT could ameliorate ovarian damage induced by CP, implying that MT may be a viable alternative to preserve female fertility during CP chemotherapy.

Newly Developed Targeted Therapies Against the Androgen Receptor in Triple-Negative Breast Cancer: A Review.

Among different types of breast cancers (BC), triple-negative BC (TNBC) amounts to 15% to 20% of breast malignancies. Three principal characteristics of TNBC cells are (i) extreme aggressiveness, (ii) absence of hormones, and (iii) growth factor receptors. Due to the lack or poor expression of the estrogen receptor, human epidermal growth factor receptor 2, and progesterone receptor, TNBC is resistant to hormones and endocrine therapies. Consequently, chemotherapy is currently used as the primary approach against TNBC. Expression of androgen receptor (AR) in carcinoma cells has been observed in a subset of patients with TNBC; therefore, inhibiting androgen signaling pathways holds promise for TNBC targeting. The new AR inhibitors have opened up new therapy possibilities for BC patients carrying AR-positive TNBC cells. Our group provides a comprehensive review of the structure and function of the AR and clinical evidence for targeting the cell's nuclear receptor in TNBC. We updated AR agonists, inhibitors, and antagonists. We also presented a new era of genetic manipulating CRISPR/Cas9 and nanotechnology as state-of-the-art approaches against AR to promote the efficiency of targeted therapy in TNBC. SIGNIFICANCE STATEMENT: The lack of effective treatment for triple-negative breast cancer is a health challenge. The main disadvantages of existing treatments are their side effects, due to their nonspecific targeting. Molecular targeting of cellular receptors, such as androgen receptors, increased expression in malignant tissues, significantly improving the survival rate of breast cancer patients.

The essential role of YAP in ERα36-mediated proliferation and the epithelial-mesenchymal transition in MCF-7 breast cancer cells.

Purpose: Most breast cancers are hormone-receptor-positive, and thus the first-line therapy for them is an anti-estrogen medication such as tamoxifen. If metastasis occurs or resistance to tamoxifen develops, the 5-year survival rates for breast cancer patients significantly decrease. Hence, a better understanding of the molecular mechanisms that contribute to breast cancer aggressiveness is of great importance. ERα36 is an estrogen receptor variant that is known to be upregulated in breast cancer patients receiving tamoxifen treatment or in triple-negative breast cancer cells. However, the specific molecular mechanism underlying ERα36-induced tamoxifen-resistance is not yet fully understood. Methods: ERα36-overexpressing MCF-7 cells were constructed by either plasmid transfection using ERα36 vector or retroviral infection using ERα36-V5-His vector. Target-gene expression was assessed by Western blot analysis and real-time PCR, and YAP activation was evaluated by luciferase assays and immunofluorescence. Cell proliferation and formation of three-dimensional spheroids were evaluated using the IncuCyte S3 Live Cell Analysis System. Results: We found that the expression patterns of Hippo signaling-related genes were significantly changed in ERα36-overexpressing MCF-7 cells compared to MCF-7 cells, which were also similarly observed in tamoxifen-resistant MCF-7 cells. Specifically, the protein expression level and activity of YAP, the core downstream protein of the Hippo pathway, were significantly increased in ERα36-overexpressing MCF-7 cells compared with MCF-7 cells. The aggressive phenotypes acquired by ERα36 overexpression in MCF-7 cells were destroyed by YAP knockout. On this basis, we propose that ERα36 regulates YAP activity by a new mechanism involving Src kinase. Conclusion: Our results suggest that YAP targeting may be a new therapeutic approach to the treatment of advanced breast cancers overexpressing ERα36.