Survival Rate Of Cancer Patients Research Articles

An Annual Symposium on Disparities in Milwaukee, WI, with a 2023 Focus on Older Adults with Cancer.

Cancer-related inequities are prevalent in Wisconsin, with lower survival rates for breast, colorectal, and lung cancer patients from marginalized communities. This manuscript describes the ongoing efforts at the Medical College of Wisconsin and potential pathways of community engagement to promote education and awareness in reducing inequities in cancer care. While some cancer inequities are related to aggressive disease biology, health-related social risks may be addressed through community-academic partnerships via an open dialogue between the community members and academic faculty. To develop potential pathways of community-academic partnerships, an annual Cancer Disparities Symposium concept evolved as a pragmatic and sustainable model in an interactive learning environment. In this manuscript, we describe the programmatic development and execution of the annual Cancer Disparities Symposium, followed by highlights from this year's meeting focused on geriatric oncology as discussed by the speakers.

Evaluation of High-Dimensional Data Classification for Skin Malignancy Detection Using DL-Based Techniques

Skin cancer can be detected through visual screening and skin analysis based on the biopsy and pathological state of the human body. The survival rate of cancer patients is low, and millions of people are diagnosed annually. By determining the different comparative analyses, the skin malignancy classification is evaluated. Using the Isomap with the vision transformer, we analyze the high-dimensional images with dimensionality reduction. Skin cancer can present with severe cases and life-threatening symptoms. Overall performance evaluation and classification tend to improve the accuracy of the high-dimensional skin lesion dataset when completed. In deep learning methodologies, the distinct phases of skin malignancy classification are determined by its accuracy, specificity, F1 recall, and sensitivity while implementing the classification methodology. A nonlinear dimensionality reduction technique called Isomap preserves the data’s underlying nonlinear relationships intact. This is essential for the categorization of skin malignancies, as the features that separate malignant from benign skin lesions may not be linearly separable. Isomap decreases the data’s complexity while maintaining its essential characteristics, making it simpler to analyze and explain the findings. High-dimensional datasets for skin lesions have been evaluated and classified more effectively when evaluated and classified using Isomap with the vision transformer.

USE OF SGLT2–INHIBITORS IN THE TREATMENT OF ANTHRACICLINE CARDIOTOXICITY

Abstract The survival rate of cancer patients has significantly increased in the last 25 years thanks to the extraordinary advances in anti-tumor therapies. However, many chemotherapeutic agents are potentially cardiotoxic. In particular, anthracycline-induced cardiotoxicity is a clinical condition caused by chemotherapy drugs that lead to morpho-functional alterations and cell death of cardiomyocytes; the most typical clinical manifestation of this cardiotoxicity is the development of left ventricular dysfunction, which over time can evolve towards full-blown heart failure. It is therefore characterized by a reversible asymptomatic stage that progresses to an irreversible stage, often difficult to treat. For an early individualization of this toxicity, a dedicated clinic has been established in our center for the monitoring of patients undergoing treatment with cardiotoxic chemotherapy. We describe the case of a 53-year-old patient with preserved biventricular systolic function undergoing neoadjuvant treatment for breast cancer, who at the end of the second infusion of anthracyclines presented with mild exertional dyspnoea associated with radiological signs of pulmonary congestion and an increase in the indices of myocardionecrosis and BNP. The echocardiogram revealed a sudden worsening of the left ventricular systolic function which was 48%. It was therefore decided to hospitalize the patient and initiate infusional diuretic therapy with a rapid clinical and instrumental response. An SGLT2 inhibitor with indication for the treatment of heart failure with moderately reduced ejection fraction was also added to the therapy. At the one-month check-up, there was an almost complete recovery of left ventricular function and a clear symptomatic benefit with associated normalization of laboratory markers of myocardial damage and heart failure, with the possibility for the patient to undergo surgery to remove the left ventricle. tumor mass. This clinical case could represent a starting point to start considering therapy with SGLT2 inhibitors for the prevention and early treatment of cardiac toxicities from some chemotherapeutics, in order to avoid the process of irreversibility of myocardial damage with subsequent poor response to anti-inflammatory therapies. – conventional compensation.

Characteristics of Orthodontic Treatment in Cancer Survivors: A Systematic Review.

Background: Survival rates of cancer patients have increased globally and across age groups. Challenges arising from craniofacial growth-development disturbances and dental abnormalities might warrant modifications to standard orthodontic pathways of care. Objective: The aim of this study was to systematically summarize and critically assess the available literature regarding the characteristics of orthodontic treatment in cancer survivors. Materials and Methods: A systematic search was conducted in seven databases for studies on malignant tumor survivors having undergone orthodontic intervention with fixed appliances following cancer treatment up to August 2023. The outcomes of interest included quantitative data regarding various characteristics of orthodontic treatment and the post-treatment period. The risk of bias was assessed individually with the Newcastle-Ottawa scale. Results: Out of 347 records, 4 cohort studies were eventually included in the qualitative synthesis. Leukemia was the most common malignancy type, with treatment involving mainly chemotherapy and/or radiotherapy. The duration of orthodontic treatment in cancer survivors varied. Occlusal results, quality of life, and satisfaction were comparable to healthy peers. However, in some survivors' groups, treatment was shorter and the final results were compromised. Root resorption and oral mucositis were reported among the treated cancer survivors. Reduced occlusal outcome stability during the retention period was also reported. Conclusions: Overall, the duration of orthodontic treatment varied among cancer survivors. The occlusal results achieved were similar to those of their healthy peers, though potentially less stable. Patient-reported outcomes did not differ significantly between cancer survivors and healthy individuals treated orthodontically.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n=10596, 70%) and a test set (n=4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

The state of cancer care in Russia: liver cancer - (From 22). Age-related features of the observed and relative one-year and five-year survival of patients. (clinical and population-based study). Part 2.

Survival rate calculations for liver cancer patients at the federal level in the Russian Federation are only conducted in the North-Western Federal District and a few other individual administrative territories. In the first part of our study, we presented a detailed analysis of the prevalence of liver cancer, noting that the levels of standardized incidence and mortality rates in Russia and the North-Western Federal District are virtually identical, allowing us to consider the results of our study as nationally representative. The conducted research confirmed the seriousness of the issue of liver cancer among the population. However, certain advancements in patient treatment were recorded, with a significant increase in survival rates, primarily for one-year survival. For example, in the North-Western Federal District, one-year survival increased from 13.6% in 2000 to 23.1% in 2022, representing a 69.8% increase. At the same time, five-year survival rates remained largely unchanged. Increases in survival rates were observed in most age groups. A significant deficiency in the initial patient record-keeping was identified - insufficiently accurate determination of the stage of liver cancer. One-year survival rates for male and female liver cancer patients (22.7% and 22.1%) were nearly equal, as were the survival rates for liver cancer patients residing in urban and rural areas. Therefore, further improvement in the outcomes of treatment for this complex patient group can only be achieved through the implementation of regional and federal screening and epidemiological surveillance programs among populations at high and extremely high risk of developing hepatocellular carcinoma.

Construction and validation of an innovative prognostic nomogram for overall survival in cervical cancer patients with lung metastasis: an analysis utilizing the SEER database.

To facilitate patient consultation and assist in clinical decision-making, we developed a predictive model to analyze the overall survival (OS) rate of cervical cancer patients with concurrent lung metastasis for 6 months, 1 year, or 2 years. We extracted data on patients diagnosed with cervical cancer and concurrent lung metastasis between 2010 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Through a random assignment process, these patients were allocated to either a training cohort or a validation cohort, maintaining a 7:3 ratio. Utilizing both univariate and multivariate Cox regression analyses, we determined the independent prognostic factors influencing OS. To enhance predictive accuracy, we developed a nomogram model incorporating these identified independent prognostic variables. Model effectiveness was subsequently assessed using various metrics, including receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). We gathered data on 1330 patients diagnosed with cervical cancer with lung metastases. An OS nomogram was developed, accounting for factors such as histological type, presence of metastases in other organs (brain, liver), surgical interventions, radiation therapy, and chemotherapy. The ROC curves, calibration plots, and DCA curves demonstrated the commendable predictive performance of the nomogram in assessing the prognosis of cervical cancer patients with lung metastases in both the training and validation cohorts. By utilizing clinical data from the SEER database, we have effectively devised a nomogram capable of predicting the 6-month, 1-year, and 2-year survival rates of cervical cancer patients with lung metastases. The nomogram boasts high accuracy, offering precise prognostic predictions. Its implementation can guide the formulation of individualized follow-up and treatment plans for enhanced patient care.

Leptomeningeal carcinomatosis, an emerging disease in internal medicine departments with a generally poor prognosis: two case reports

As a result of improved diagnostic methods and higher cancer patient survival rates, leptomeningeal carcinomatosis is being found in internal medicine departments more frequently. Melanoma, lung cancer, and breast adenocarcinoma are the three cancers that are most commonly associated. When the main tumor diffuses through the bloodstream, cerebrospinal fluid, or direct diffusion and causes multiple focal neurological symptoms, it is known as leptomeningeal involvement; the prognosis is usually not good. Despite multimodal treatment, leptomeningeal carcinomatosis is an advanced form of cancer that frequently results in rapid death. Treatment is typically palliative and consists primarily of intrathecal or systemic radiation and/or chemotherapy. New experimental therapies and immunotherapy are promising means of lowering morbidity and mortality. Clinical cases of leptomeningeal carcinomatosis are reported; both cases were identified at the Ospedale dei Castelli’s Department of Internal Medicine and resulted from primary breast pathology.

Do immune checkpoint inhibitor-related endocrinopathies affect survival rates in cancer patients? Presentation of two case reports

Do immune checkpoint inhibitor-related endocrinopathies affect survival rates in cancer patients? Presentation of two case reports

Transcriptome analysis revealed SMURF2 as a prognostic biomarker for oral cancer.

The activation of TGF-β pathway can facilitate tumorigenesis. Understanding the TGF-related genes (TRGs) in oral cancer and determining their prognostic value is of utmost importance. The TRGs were selected to develop a prognostic model based on lasso regression. Oral cancer patients were classified into high-risk and low-risk groups based on the risk model. Subsequently, multivariate COX regression was employed to identify the prognostic marker. Additionally, the expression of SMURF2 was validated using quantitative real-time polymerase chain reaction (qRT-PCR) and the Human Protein Atlas (HPA) database. To investigate the relationship between SMURF2 expression and immune cell infiltrations, we conducted single-sample Gene Set Enrichment Analysis (ssGSEA) analyses. We identified 16 differentially expressed TRGs in oral cancer, all of which showed upregulation. From these, we selected eight TRGs as prognostic signatures. Furthermore, the high-risk group demonstrated lower infiltration levels of immune cells, immune score, and higher tumor purity. Interestingly, we also found that SMURF2 serves as an independent prognostic biomarker. SMURF2 was upregulated in oral cancer, as confirmed by public databases and qRT-PCR analysis. Importantly, our results indicate a close association between SMURF2 expression and the immune microenvironment. The 8-TRG signature prognosis model that we constructed has the ability to predict the survival rate and immune activity of oral cancer patients. SMURF2 could be effective in recognizing prognosis and evaluating immune efficacy for oral cancer.

New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model

ObjectiveThe high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients.MethodsDifferentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights.ResultsHigh METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1.ConclusionThe random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

Epigallocatechin gallate protects against fat and muscle atrophy in B16BL6 melanoma-bearing mice on a high-fat diet

AimsEpidemiological evidence indicates that there is a substantial association between body mass index (BMI) and at least ten forms of cancer, including melanoma, and BMI imbalance contributes to the poor survival rate of cancer patients before and after therapy. Nevertheless, few pharmacological studies on models of obesity and cancer have been reported. In this study, we administered epigallocatechin gallate (EGCG) to B16BL6 tumor-bearing mice that received a high-fat diet (HFD) to examine its impact. MethodsB16BL6 tumor-bearing mice were fed a HFD. Body weight and food intake were documented every week. We conducted a Western blot analysis to examine the protein levels in the tumor, gastrocnemius (GAS), and tibialis anterior (TA) muscles, as well as the inguinal and epididymal white adipose tissues (iWAT and eWAT). Key findingsEGCG has been shown to have anti-cancer effects equivalent to those of cisplatin, a chemotherapy drug. Furthermore, EGCG protected against the loss of epidydimal white adipose tissue by regulating protein levels of lipolysis factors of adipose triglyceride lipase and hormone-sensitive lipase as well as WAT browning factors of uncoupling protein 1, as opposed to cisplatin. EGCG was shown to reduce the protein levels of muscular atrophy factors of muscle RING-finger protein-1, whereas cisplatin did not contribute to rescuing the atrophy of TA and GAS muscles. ConclusionTaken together, our findings indicate that EGCG has a preventive effect against cachexia symptoms and has anti-cancer effects similar to those of cisplatin in tumor-bearing mice fed a high-fat diet.

Injectable bio-multifunctional hyaluronic acid-based hydrogels loaded with poly ADP-ribose polymerase inhibitors for ovarian cancer therapy

The recent use of PARP inhibitors (PARPi) in the maintenance treatment of ovarian tumor has significantly improved the survival rates of cancer patients. However, the current oral administration of PARP inhibitors fails to realize optimal therapeutic effects due to the low bioavailability in cancerous tissues, and often leads to a range of systemic adverse effects including hematologic toxicities, digestive system reactions, and neurotoxicities. Therefore, the demand for an advanced drug delivery system that can ensure effective drug administration while minimizing these unfavorable reactions is pressing. Injectable hydrogel emerges as a promising solution for local administration with the capability of sustainable drug release. In this study, we developed an injectable hydrogel made from aminated hyaluronic acid and aldehyde-functionalized pluronic127 via Schiff base reaction. This hydrogel exhibits excellent injectability with short gelation time and remarkable self-healing ability, and is applied to load niraparib. The drug-loaded hydrogel (HP@Nir hydrogel) releases drugs sustainably as tested in vitro as well as displays significant anti-proliferation and anti-migratory properties on human epithelial ovarian cancer cell line. Notably, HP@Nir hydrogel effectively suppresses the growth of ovarian cancer, without significant adverse reactions as demonstrated in animal studies. Additionally, the developed hydrogel is gradually degraded in vivo for around 20 d, while maintaining good biocompatibility. Overall, the injectable hydrogel loaded with niraparib provides a secure and efficient strategy for the treatment and management of ovarian cancer.

Effect of the Affordable Care Act on Cervical Cancer Survival Rates in Californian Women [ID 2683402

INTRODUCTION: Following the Affordable Care Act (ACA), insurance coverage among women in California surged. While ACA aimed to boost health care access and utilization, whether this has improved cancer-specific survival rates for cervical cancer patients remains questioned. In the present study, we evaluate the effect of the Affordable Care Act (ACA) on cancer-specific survival rates among women with cervical cancer by comparing pre-ACA (2000–2009) and post-ACA (2010–2020) periods. METHODS: This study utilized data from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2020. We bifurcated the study duration into pre-ACA (2000–2009) and post-ACA (2010–2020). The primary outcome investigated was cancer-specific survival between these periods. Our objective was to assess the effect of ACA's implementation on cancer-specific survival among women diagnosed with cervical cancer. Analytical adjustments were made for patient age, race/ethnicity, disease stage at presentation, income, marital status, and metropolitan residence status. We also considered whether cervical cancer was the primary site and incorporated treatment modalities such as curative surgery, radiotherapy, and chemotherapy into our analysis. RESULTS: Of 29,402 cervical cancer patients, 15,962 were diagnosed pre-ACA and 13,440 post-ACA. More women over 50 were diagnosed post-ACA (49.4%) than pre-ACA (44.9%). Late-stage diagnoses rose slightly post-ACA (42.7% versus 40.8%). Curative surgery decreased post-ACA (53.7% from 62.7%), but chemotherapy increased (48.6% from 39.9%). After adjusting for treatment modalities, post-ACA survival rates in California mirrored those of the pre-ACA period (hazard ratio [HR] 1.06; 95% CI, 0.98–1.15). CONCLUSION: The implementation of the ACA led to notable changes in treatment patterns and demographics for cervical cancer patients in California. Although health care access increased, cancer-specific survival rates were similar to the pre-ACA period when adjusting for treatment modalities.

Construction and Validation of a MRI‑Based Radiomic Nomogram to Predict Overall Survival in Patients with Local Advanced Cervical Cancer: A Multicenter Study

BackgroundCervical cancer is the fourth most common cancer among women. Radiomics has emerged as a new approach providing valuable information for cancer management. The aim of this study was to construct a radiomics nomogram to accurately predict survival outcomes in patients with locally advanced cervical cancer. MethodsThis retrospective study enrolled a total of 582 locally advanced cervical cancer patients from three center (training cohort: n=228; internal validation cohort: n=98; external validation cohort: n=256). Radiomic features were extracted from pretreatment MRI images. Least absolute shrinkage and selection operator logistic regression (LASSO) was applied to select radiomic features and calculated the radiomic scores. Univariate and multivariate Cox proportional hazards regression analyses were used to identify the independent prognostic clinic-radiological factors for cervical cancer, which were incorporated into the nomogram. ResultsA total of 6 radiomic features were found to be associated with overall survival of locally advanced cervical cancer patients. The AUC of radiomic scores in the training cohort was 0.634-0.708 for the training cohort, 0.725-0.762 for internal validation cohort and 0.788-0.881 for the external validation cohort. Age, parametrial invasion, and radiomic score were the independent prognostic indicators for cervical cancer patients (Age: HR=1.041, 95% CI=1.012-1.071, p=0.006; Parametrial invasion: HR=4.755, 95% CI=1.493-15.144, p=0.008; HR=2.324, 95% CI=1.050-5.143, p=0.037). The nomogram model incorporating these factors showed favorable discrimination in predicting the overall survival rates of cervical cancer patients, with the AUC values of 0.809, 0.808, and 0.862 for 1-, 2-, and 3-year predictions. The decision curve analysis (DCA) indicated that the nomogram model achieved the highest clinical net benefit across the entire range of reasonable threshold probabilities. ConclusionThe nomogram, incorporating clinicopathological factors and radiomic features derived from MRI images, showed satisfactory discrimination in predicting the overall survival rates of locally advanced cervical cancer patients.

Hsa-miR-CHA2, a novel microRNA, exhibits anticancer effects by suppressing cyclin E1 in human non-small cell lung cancer cells

Despite considerable therapeutic advancements, the global survival rate for lung cancer patients remains poor, posing challenges in developing an effective treatment strategy. In many cases, microRNAs (miRNAs) exhibit abnormal expression levels in cancers, including lung cancer. Dysregulated miRNAs often play a crucial role in the development and progression of cancer. Therefore, understanding the mechanisms underlying aberrant miRNA expression during carcinogenesis may provide crucial clues to develop novel therapeutics. In this study, we identified and cloned a novel miRNA, hsa-miR-CHA2, which is abnormally downregulated in non-small cell lung cancer (NSCLC)-derived cell lines and tissues of patients with NSCLC. Furthermore, we found that hsa-miR-CHA2 regulates the post-transcriptional levels of Cyclin E1 (CCNE1) by binding to the 3′-UTR of CCNE1 mRNA. CCNE1, a cell cycle regulator involved in the G1/S transition, is often amplified in various cancers. Notably, hsa-miR-CHA2 overexpression led to the alteration of the Rb-E2F pathway, a significant signaling pathway in the cell cycle, by targeting CCNE1 in A549 and SK-LU-1 cells. Subsequently, we confirmed that hsa-miR-CHA2 induced G1-phase arrest and exhibited an anti-proliferative effect by targeting CCNE1. Moreover, in subcutaneous xenograft mouse models, intra-tumoral injection of polyplexed hsa-miR-CHA2 mimic suppressed tumor growth and development. In conclusion, hsa-miR-CHA2 exhibited an anticancer effect by targeting CCNE1 both in vitro and in vivo. These findings suggest the potential role of hsa-miR-CHA2 as an important regulator of cell proliferation in molecular-targeted therapy for NSCLC.

The association between the expression level of nuclear paraspeckle assembly transcript 1 and the survival rate of head and neck cancer patients after treatment

Background/purposeThe long non-coding RNA (lncRNA) nuclear paraspeckle assembly transcript 1 (NEAT1) exhibits diverse and complicated functions in cancer progression. Despite reports suggesting both tumor-suppressive and oncogenic effects in various cancers, its specific role in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study aimed to investigate the association between NEAT1 expression levels and survival outcomes in HNSCC patients. Materials and methodsPaired tissue samples of tumor and non-cancerous matching tissues (NCMT) from 92 HNSCC patients were collected. NEAT1 expression was analyzed using RT-qPCR. Clinical characteristics, treatment received, and survival rates of the patients were assessed to determine the correlation with NEAT1 expression and explore its association with alcohol, betel quid, and cigarette use. Additionally, we examined the effect of arecoline on NEAT1 expression in normal human oral keratinocytes (NHOK) and fibroblasts (NHOF). ResultsThe study revealed a significant downregulation of NEAT1 expression in oral cancer tissues compared to NCMT. Meanwhile, arecoline increased NEAT1 expression in NHOK and NHOF cells. However, patients with downregulated NEAT1 expression exhibited higher overall survival rates, particularly in those who did not receive chemotherapy or radiotherapy. ConclusionNEAT1 expression levels are associated with survival outcomes in HNSCC patients, with upregulated expression indicating a worse prognosis, suggesting this lncRNA might contribute to cancer aggressiveness, especially in the absence of active treatment. These findings indicate NEAT1 may serve as a potential prognostic biomarker in HNSCC, but further research is required to elucidate its role in cancer progression and its potential as a therapeutic target.

Survivin as a Therapeutic Target for the Treatment of Human Cancer.

Survivin was initially identified as a member of the inhibitor apoptosis (IAP) protein family and has been shown to play a critical role in the regulation of apoptosis. More recent studies showed that survivin is a component of the chromosome passenger complex and acts as an essential mediator of mitotic progression. Other potential functions of survivin, such as mitochondrial function and autophagy, have also been proposed. Survivin has emerged as an attractive target for cancer therapy because its overexpression has been found in most human cancers and is frequently associated with chemotherapy resistance, recurrence, and poor survival rates in cancer patients. In this review, we discuss our current understanding of how survivin mediates various aspects of malignant transformation and drug resistance, as well as the efforts that have been made to develop therapeutics targeting survivin for the treatment of cancer.

Monitoring ROS Responsive Fe3O4-based Nanoparticle Mediated Ferroptosis and Immunotherapy via 129Xe MRI.

The immune checkpoint blockade strategy has improved the survival rate of late-stage lung cancer patients. However, the low immune response rate limits the immunotherapy efficiency. Here, we report a ROS-responsive Fe3O4-based nanoparticle that undergoes charge reversal and disassembly in the tumor microenvironment, enhancing the uptake of Fe3O4 by tumor cells and triggering a more severe ferroptosis. In the tumor microenvironment, the nanoparticle rapidly disassembles and releases the loaded GOx and the immune-activating peptide Tuftsin under overexpressed H2O2. GOx can consume the glucose of tumor cells and generate more H2O2, promoting the disassembly of the nanoparticle and drug release, thereby enhancing the therapeutic effect of ferroptosis. Combined with Tuftsin, it can more effectively reverse the immune-suppressive microenvironment and promote the recruitment of effector T cells in tumor tissues. Ultimately, in combination with α-PD-L1, there is significant inhibition of the growth of lung metastases. Additionally, the hyperpolarized 129Xe method has been used to evaluate the Fe3O4 nanoparticle-mediated immunotherapy, where the ventilation defects in lung metastases have been significantly improved with complete lung structure and function recovered. The ferroptosis-enhanced immunotherapy combined with non-radiation evaluation methodology paves a new way for designing novel theranostic agents for cancer therapy.

Predicting rectal cancer prognosis from histopathological images and clinical information using multi-modal deep learning.

The objective of this study was to provide a multi-modal deep learning framework for forecasting the survival of rectal cancer patients by utilizing both digital pathological images data and non-imaging clinical data. The research included patients diagnosed with rectal cancer by pathological confirmation from January 2015 to December 2016. Patients were allocated to training and testing sets in a randomized manner, with a ratio of 4:1. The tissue microarrays (TMAs) and clinical indicators were obtained. Subsequently, we selected distinct deep learning models to individually forecast patient survival. We conducted a scanning procedure on the TMAs in order to transform them into digital pathology pictures. Additionally, we performed pre-processing on the clinical data of the patients. Subsequently, we selected distinct deep learning algorithms to conduct survival prediction analysis using patients' pathological images and clinical data, respectively. A total of 292 patients with rectal cancer were randomly allocated into two groups: a training set consisting of 234 cases, and a testing set consisting of 58 instances. Initially, we make direct predictions about the survival status by using pre-processed Hematoxylin and Eosin (H&E) pathological images of rectal cancer. We utilized the ResNest model to extract data from histopathological images of patients, resulting in a survival status prediction with an AUC (Area Under the Curve) of 0.797. Furthermore, we employ a multi-head attention fusion (MHAF) model to combine image features and clinical features in order to accurately forecast the survival rate of rectal cancer patients. The findings of our experiment show that the multi-modal structure works better than directly predicting from histopathological images. It achieves an AUC of 0.837 in predicting overall survival (OS). Our study highlights the potential of multi-modal deep learning models in predicting survival status from histopathological images and clinical information, thus offering valuable insights for clinical applications.