Survival Rate Of Cancer Patients Research Articles

Testicular niche repair after gonadotoxic treatments: Current knowledge and future directions.

The testicular niche, which includes the germ cells, somatic cells, and extracellular matrix, plays a crucial role in maintaining the proper functions of the testis. Gonadotoxic treatments, such as chemotherapy and radiation therapy, have significantly improved the survival rates of cancer patients but have also been shown to have adverse effects on the testicular microenvironment. Therefore, repairing the testicular niche after gonadotoxic treatments is essential to restore its function. In recent years, several approaches, such as stem cell transplantation, gene therapy, growth factor therapy, and pharmacological interventions have been proposed as potential therapeutic strategies to repair the testicular niche. This comprehensive review aims to provide an overview of the current understanding of testis damage and repair mechanisms. We will cover a range of topics, including the mechanism of gonadotoxic action, repair mechanisms, and treatment approaches. Overall, this review highlights the importance of repairing the testicular niche after gonadotoxic treatments and identifies potential avenues for future research to improve the outcomes for cancer survivors.

Identifying Biomarkers and Therapeutic Targets by Multiomic Analysis for HNSCC: Precision Medicine and Healthcare Management.

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the major types of cancer, with 900,000 cases and over 400,000 deaths annually. It constitutes 3-4% of all cancers in Europe and western countries. As early diagnosis is the key to treating the disease, reliable biomarkers play an important role in the precision medicine of HNSCC. Despite treatments, the survival rate of cancer patients remains unchanged, and this is mainly due to the failure to detect the disease early. Thus, the objective of this study is to identify reliable biomarkers for head and neck cancers for better healthcare management. Methods: In this study, all available, curated human genes were screened for their expression against HNSCC TCGA patient samples using genomic and proteomic data by various bioinformatic approaches and datamining. Docking studies were performed using AutoDock or online virtual screening tools for identifying potential ligands. Results: Sixty genes were short-listed, and most of them show a consistently higher expression in head and neck patient samples at both the mRNA and the protein level. Irrespective of human papillomavirus (HPV) status, all of them show a higher expression in cancer samples. The higher expression of 30 genes shows adverse effects on patient survival. Out of the 60 genes, 12 genes have crystal structures and druggable potential. We show that genes such as GTF2H4, HAUS7, MSN, and MNDA could be targets of Pembrolizumab and Nivolumab, which are approved monoclonal antibodies for HNSCC. Conclusion: Sixty genes are identified as potential biomarkers for head and neck cancers based on their consistent and statistically significantly higher expression in patient samples. Four proteins have been identified as potential drug targets based on their crystal structure. However, the utility of these candidate genes has to be further tested using patient samples.

Radiogenomic analysis of ultrasound phenotypic features coupled to proteomes predicts metastatic risk in primary prostate cancer

BackgroundPrimary prostate cancer with metastasis has a poor prognosis, so assessing its risk of metastasis is essential.MethodsThis study combined comprehensive ultrasound features with tissue proteomic analysis to obtain biomarkers and practical diagnostic image features that signify prostate cancer metastasis.ResultsIn this study, 17 ultrasound image features of benign prostatic hyperplasia (BPH), primary prostate cancer without metastasis (PPCWOM), and primary prostate cancer with metastasis (PPCWM) were comprehensively analyzed and combined with the corresponding tissue proteome data to perform weighted gene co-expression network analysis (WGCNA), which resulted in two modules highly correlated with the ultrasound phenotype. We screened proteins with temporal expression trends based on the progression of the disease from BPH to PPCWOM and ultimately to PPCWM from two modules and obtained a protein that can promote prostate cancer metastasis. Subsequently, four ultrasound image features significantly associated with the metastatic biomarker HNRNPC (Heterogeneous nuclear ribonucleoprotein C) were identified by analyzing the correlation between the protein and ultrasound image features. The biomarker HNRNPC showed a significant difference in the five-year survival rate of prostate cancer patients (p < 0.0053). On the other hand, we validated the diagnostic efficiency of the four ultrasound image features in clinical data from 112 patients with PPCWOM and 150 patients with PPCWM, obtaining a combined diagnostic AUC of 0.904. In summary, using ultrasound imaging features for predicting whether prostate cancer is metastatic has many applications.ConclusionThe above study reveals noninvasive ultrasound image biomarkers and their underlying biological significance, which provide a basis for early diagnosis, treatment, and prognosis of primary prostate cancer with metastasis.

Investigating the Risk Factors Affecting the Survival Rate of Breast Cancer Patients Using Cured Model Based on Defective Distribution

Background: The analysis methods for breast cancer (BC) data have also advanced alongside medical advancements in the treatment of the disease. Objectives: This study tried to investigate the factors affecting the survival rate of BC patients using the cured model based on Kumaraswamy's defective distribution. Methods: A retrospective study collected data on 2 574 BC patients between September 2013 and September 2020, including demographic, clinicopathological, and biological characteristics. The best model for predicting cure was chosen based on AIC. Results: The selected cure model revealed that age (P = 0.046), tumor histologic grade (P = 0.0.38), tumor size (P = 0.0.41), HER2 status (P = 0.001), KI67 levels (P = 0.027), P53 status (P = 0.029), and hormone therapy (P = 0.039) were significant variables. The estimated cured rate of this data was 0.82. Conclusions: Considering that the advanced cured model has the highest accuracy in identifying the factors affecting the survival rate of BC patients and more risk factors have become significant in this model, it is recommended to pay special attention to patients aged over 60 with poorly differentiated historical grade, T3 tumor size, HER2 positive status, KI67 levels below 20%, negative P53 status, and no hormone therapy received in the process of disease prognosis.

Ratio fluorescence/colorimetric dual mode aptasensor and smartphones-assisted miniaturized device for early diagnosis of prostate cancer

Prostate specific antigen (PSA) is the most common tumor biomarker for prostate cancer (PCa), and its precise determination is essential to improving survival rate and reducing treatment cost of cancer patient. Here, we have proposed a novel ratio fluorescence/colorimetric dual mode aptasensor based on CdTe-apt + B-CDs + ZrFe MOF system for PSA determination. Specifically, as-prepared ZrFe MOF with good peroxidase like activity can quench the fluorescence of B-CDs + CdTe-apt system via inner filter effect (IFE), while the existence of PSA can restore fluorescence of CdTe-apt but virtually unchanged fluorescence of B-CDs due to specific identification between PSA and aptamer. Similarly, the addition of PSA can recover the peroxidase like activity of ZrFe MOF which is significantly suppressed by PSA aptamer. Based on this, the proposed ratio fluorescence/colorimetric dual mode aptasensor has achieved satisfactory results in PSA analysis of serum specimens. Valuably, an inexpensive, portable but credible smartphones-assisted miniaturized device is expected to be exploited for the early diagnosis of PCa, inspiring the design and construction of advanced portable devices for self-monitoring and home screening of various cancers.

Forecasting Survival Rates in Metastatic Colorectal Cancer Patients Undergoing Bevacizumab-Based Chemotherapy: A Machine Learning Approach

Background: Antibiotics can play a pivotal role in the treatment of colorectal cancer (CRC) at various stages of the disease, both directly and indirectly. Identifying novel patterns of antibiotic effects or responses in CRC within extensive medical data poses a significant challenge that can be addressed through algorithmic approaches. Machine Learning (ML) emerges as a promising solution for predicting clinical outcomes using clinical and heterogeneous cancer data. In the pursuit of our objective, we employed ML techniques for predicting CRC mortality and antibiotic influence. Methods: We utilized a dataset to examine the accuracy of death prediction in metastatic colorectal cancer. In addition, we analyzed the association between antibiotic exposure and mortality in metastatic colorectal cancer. The dataset comprised 147 patients, nineteen independent variables, and one dependent variable. Our analysis involved testing different classification-supervised ML, including an oversampling pool for classification models, Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machine, Random Forest, XGBboost Classifier, a consensus of all models, and a consensus of top models (meta models). Results: The consensus of the top models’ classifier exhibited the highest accuracy among the algorithms tested (93%). This model met the standards for good accuracy, surpassing the 90% threshold considered useful in ML applications. Consistent with the accuracy results, other metrics are also good, including precision (0.96), recall (0.93), F-Beta (0.94), and AUC (0.93). Hazard ratio analysis suggests that there is no discernible difference between patients who received antibiotics and those who did not. Conclusions: Our modelling approach provides an alternative for analyzing and predicting the relationship between antibiotics and mortality in metastatic colorectal cancer patients treated with bevacizumab, complementing classic statistical methods. This methodology lays the groundwork for future use of datasets in cancer treatment research and highlights the advantages of meta models.

Therapeutic Approaches to Increase the Survival Rate of Cancer Patients in the Younger and Older Population.

Various developments have been observed in the treatment of cancer patients, such as higher survival rates and better treatment outcomes. However, expecting similar outcomes in older patients remains a challenge. The main reason for this conclusion is the exclusion of older people from clinical trials for cancer drugs, as well as other factors, such as comorbidity, side effects, age-related frailties and their willingness to undergo multiple treatments. However, the discovery of new techniques and drug combinations has led to a significant improvement in the survival of the elderly population after the onset of the disease. On the other hand, cancer treatments have not become more complex for the younger population when compared to the older population, as the younger population tends to respond well to treatment trials and their physiological conditions are stable in response to treatments. In summary, this review correlates recent cancer treatment strategies and the corresponding responses and survival outcomes of older and younger patients.

Risk factor analysis and nomogram construction in patients with distant metastatic prostate cancer at different PSA levels: a study based on the SEER database.

Prostate cancer (PCa) is the most common malignant tumor in the male genitourinary system. Once PCa has metastasized, it is very difficult to cure. The purpose of this study was to investigate the prognostic risk factor analysis of patients with different prostate-specific antigen (PSA) levels in distant metastatic PCa. At the same time, we construct effective models for predicting the survival rate of prostate cancer patients. Data on prostate cancer patients with the presence of distant metastases were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. PCa patients with distant metastases were categorized into two groups based on PSA levels, one with PSA <20 ng/mL and the other with PSA ≥20 ng/mL. Univariate and multivariate COX regression analyses were used to identify independent factors affecting the prognosis of the patients. A nomogram was constructed using the independent prognostic factors, and the results were evaluated using calibration curves, timeROC curves, and Kaplan-Meier curves. In the PSA <20 ng/mL group, there were a total of 1,832 patients. COX regression analysis showed that age, marital status, N stage, grade, Gleason score, and medical household income inflation were independent prognostic factors for overall survival (OS) in patients. In addition, we found that age, marital status, N stage, bone metastasis, grade, and Gleason score were independent prognostic factors for cancer-specific survival (CSS) in patients. In the PSA ≥20 ng/mL group, there were a total of 5,314 patients. It was found that age, ethnicity, marital status, bone metastasis, first malignant primary indicator, grade, Gleason score, and medical household income inflation were patients' independent prognostic factors for OS. For CSS, we found that age, ethnicity, marital status, T stage, radiotherapy, bone metastasis, Gleason score, and Median household income inflation were independent prognostic factors. Constructing a nomogram can accurately predict the prognosis of this group of patients. We found different independent prognostic factors for different PSA levels in patients with distant metastatic PCa. A new nomogram was constructed to predict OS and CSS in patients, which helps in clinical-assisted decision-making.

MiR-29a-KLF4 signaling inhibits breast tumor initiation by regulating cancer stem cells

Cancer stem cells (CSCs) are known for their potent ability to drive tumor initiation and recurrence, yet the molecular mechanisms regulating CSCs are still unclear. Our study found a positive correlation between increased levels of miR-29a and better survival rates in early-stage breast cancer patients, but a negative correlation in late-stage patients, suggesting a dual function of miR-29a in regulating breast cancer. Furthermore, miR-29a showed significant downregulation in the ALDH+ breast cancer stem cell population compared to non-stem cancer cells. Overexpression of miR-29a in human breast cancer cells reduced the proportion of CSCs, suppressed their ability to form mammospheres, and inhibited the expression of stemness genes SOX2, KLF4, and hTERT in vitro. Conversely, knockdown of miR-29a in breast cancer cells showed opposite effects. Tumor xenograft experiments revealed that miR-29a overexpression significantly inhibited tumorigenesis initiated by MDA-MB-231 cell transplantation in nude mice. We further demonstrated that Krüppel-like factor 4 (KLF4), a key gene that regulates cell stemness, was a direct target of miR-29a in breast cancer cells. miR-29a suppressed the expression of KLF4 at both mRNA and protein levels. Reintroduction of KLF4 into breast cancer cells rescued the miR-29a-induced CSC suppression phenotype. In summary, our study is the first to demonstrate that miR-29a-KLF4 signaling inhibits breast tumor initiation by regulating CSCs, which provides novel therapeutic targets for preventing breast tumor initiation.

Checkpoint CD24 function on tumor and immunotherapy.

CD24 is a protein found on the surface of cells that plays a crucial role in the proliferation, invasion, and spread of cancer cells. It adheres to cell membranes through glycosylphosphatidylinositol (GPI) and is associated with the prognosis and survival rate of cancer patients. CD24 interacts with the inhibitory receptor Siglec-10 that is present on immune cells like natural killer cells and macrophages, leading to the inhibition of natural killer cell cytotoxicity and macrophage-mediated phagocytosis. This interaction helps tumor cells escape immune detection and attack. Although the use of CD24 as a immune checkpoint receptor target for cancer immunotherapy is still in its early stages, clinical trials have shown promising results. Monoclonal antibodies targeting CD24 have been found to be well-tolerated and safe. Other preclinical studies are exploring the use of chimeric antigen receptor (CAR) T cells, antibody-drug conjugates, and gene therapy to target CD24 and enhance the immune response against tumors. In summary, this review focuses on the role of CD24 in the immune system and provides evidence for CD24 as a promising immune checkpoint for cancer immunotherapy.

Serum EZH2 is a novel biomarker for bladder cancer diagnosis and prognosis.

The primary objective of this study was to examine the levels of serum EZH2 in patients diagnosed with bladder cancer, and subsequently evaluate its potential as a biomarker for both the diagnosis and prognosis of bladder cancer. Blood samples were obtained from 115 bladder cancer patients and 115 healthy persons. We measured the EZH2 concentrations in the serum of these subjects via enzyme-linked immunosorbent assay (ELISA). To assess the diagnostic performance of serum EZH2 in detecting bladder cancer, we plotted receiver operating characteristic (ROC) curves and calculated their corresponding area under the curve (AUC). We also used the Cox regression model and log-rank test to investigate the correlation between EZH2 levels and clinicopathological characteristics, and survival rates of bladder cancer patients. Serum EZH2 levels were significantly higher in bladder cancer patients when compared to those in healthy persons. Serum EZH2 levels exhibited a significant correlation with TNM stage, lymph node metastasis, muscle invasion, and tumor size. At a cutoff value of 8.23 ng/mL, EZH2 was able to differentiate bladder cancer patients from healthy persons, with an AUC of 0.87, a sensitivity of 81.31%, and a specificity of 78.42%. High EZH2 levels correlated with poor overall survival rates and progression-free survival rates of bladder cancer patients. Serum EZH2 levels were elevated in bladder cancer patients, and patients with higher serum EZH2 levels exhibited a poorer prognosis. This indicates that serum EZH2 could be a novel biomarker for bladder cancer diagnosis and prognosis. Such findings could improve the prognosis of bladder cancer patients by facilitating early detection and continuous monitoring.

Research progress of perioperative immunotherapy for locally advanced gastric cancer

Gastric cancer is a highly heterogeneous disease, and its occurrence and development are the result of genetic factors, environmental factors, and host factors. As one of the main types of cancer in China, radical gastrectomy is the only chance for patients to be cured, but only 50% to 60% of initially diagnosed gastric cancer patients are suitable for radical gastrectomy, especially in locally advanced and advanced patients. The results of MAGIC and ACCORD07/FFCD9703 confirm the status of combination chemotherapy with fluorouracil as the standard of perioperative treatment for locally advanced gastric cancer. Gastric cancer is one of the most common diseases in China, with high heterogeneity and poor prognosis. Immunotherapy has always been a difficulty in medical research. However, the 5-year overall survival rate for gastric cancer patients remains low. Immunotherapy is effective in the first-line treatment of advanced gastric cancer. At the same time, immunotherapies such as immune checkpoint inhibitors, tumor vaccines, and cell therapy also show certain safety and effectiveness in the perioperative treatment of locally advanced gastric cancer patients. This paper reviews the research progress of immunotherapy in the perioperative treatment of locally advanced gastric cancer.

Prediction and feature analysis of breast cancer based on machine learning technology

Breast cancer, whose incidence rate is increasing year by year, is one of the malignant tumours with the highest incidence rate in women. Every year, an increment of about 1300000 people suffers from breast cancer and 400000 people die from it globally. For the sake of those who may be at risk of breast cancer, it is of critical importance to establish a model that can make predictions of breast cancer. This study utilizes the Random Forest algorithm and Logistic Regression algorithm to construct an analysis model. The study is conducted on a breast cancer dataset that contains data derived from Wisconsin. Specifically, the research conducts feature selection and manages to work out the relationship between various features and tumour types and selects the 5 most significant features. Based on the data of those 5 features, the accuracy of the two models is compared and the Logistic Regression Model is further optimized to reach a higher prediction accuracy. This study is highly significant in the medical community since the model it created can help with breast cancer prediction, allowing for early intervention and a higher survival rate for possible breast cancer patients.

High expression of COX6B2 in gastric cancer is associated with poor long-term prognosis and promotes cell proliferation and cell cycle progression by inhibiting p53 signaling

To investigate the effect of COX6B2 expression in gastric cancer tissues on the patients' long-term prognosis and its underlying mechanism. Based on the public databases and the medical records of patients, we analyzed the expression level of COX6B2 in gastric cancer and adjacent tissues and its influence on long-term prognosis of the patients. Enrichment analysis were used to predict the possible role of COX6B2 in gastric cancer. The effects of lentivirusmediated COX6B2 knockdown on biological behaviors of gastric cancer cells were examined using CCK-8 assay, flow cytometry, and Western blotting. TCGA database and the results of immunohistochemistry, Western blotting and realtime PCR all demonstrated a significantly higher expression of COX6B2 in gastric cancer tissues (P < 0.05). Kaplan-Meier plotter database and Kaplan-Meier curves showed that the patients with high COX6B2 expression had significantly shorter postoperative survival (P < 0.05). A high expression of COX6B2 in gastric cancer tissues was closely correlated with clinicopathologic stage, CEA and CA19-9 (P < 0.05). A high expression of COX6B2, CEA level≥5 μg/L and CA19-9 level≥37 kU/L were independent risk factors affecting postoperative 5-year survival rate of gastric cancer patients (P < 0.05), and COX6B2 expression level had a predictive value for long-term prognosis of the patients (P < 0.05). GO and KEGG enrichment analyses showed that COX6B2 was mainly involved in the regulation of cell cycle. In the in vitro cell experiment, COX6B2 overexpression significantly promoted gastric cancer cell proliferation, increased the percentage of G1/S phase cells and inhibited the cellular expressions of p53 and p21 (P < 0.05). s COX6B2 is highly expressed in gastric cancer and is closely correlated with a poor long-term prognosis of the patients possibly by promoting gastric cancer cell proliferation and regulating cell cycle.

Splenectomy as Part of Maximal-Effort Cytoreductive Surgery in Advanced Epithelial Ovarian Cancer.

A splenectomy is frequently performed during debulking surgery for advanced ovarian cancer. Its impact on perioperative and survival outcomes remains questionable as current evidence is conflicting. In the present study, we sought to determine the factors that affect survival rates in ovarian cancer patients that undergo a splenectomy as part of maximal-effort cytoreduction. A retrospective chart review was conducted that included all epithelial ovarian cancer patients that had surgical cytoreduction for advanced epithelial ovarian cancer. Differences among splenectomized and non splenectomized patients were evaluated as well as the impact of known risk factors on survival outcomes of splenectomized patients. Overall, 245 patients were identified and 223 were included in the present series, of whom 91 had a splenectomy. Recurrence rates as well as death rates were comparable among splenectomized and non-splenectomized patients; however, both the disease-free survival (log-rank = 0.001), as well as the overall survival of splenectomized patients (log-rank = 0.006), was shorter. Thrombotic events as well as rates of pulmonary embolism were comparable. Sepsis was more common among splenectomized patients. The site of splenic metastases did not influence patients' survival. Among splenectomized patients, those offered primary debulking had longer progression-free survival (log-rank = 0.042), although their overall survival did not differ compared to patients submitted to interval debulking. Complete debulking significantly improved the overall survival compared to optimal debulking (log-rank = 0.047). Splenectomized patients that developed sepsis had worse overall survival (log-rank = 0.005). The findings of our study support the feasibility of splenectomy in advanced epithelial ovarian cancer; however, its impact on patients' survival is considerable. Therefore, every effort should be made to avoid splenic injury which will result in unintended splenectomy for non-oncological reasons.

Preoperative controlling nutritional status as an optimal prognostic nutritional index to predict the outcome for colorectal cancer.

The controlling nutritional status (CONUT) score effectively reflects a patient's nutritional status, which is closely related to cancer prognosis. This study investigated the relationship between the CONUT score and prognosis after radical surgery for colorectal cancer, and compared the predictive ability of the CONUT score with other indexes. To analyze the predictive performance of the CONUT score for the survival rate of colorectal cancer patients who underwent potentially curative resection. This retrospective analysis included 217 patients with newly diagnosed colorectal. The CONUT score was calculated based on the serum albumin level, total lymphocyte count, and total cholesterol level. The cutoff value of the CONUT score for predicting prognosis was 4 according to the Youden Index by the receiver operating characteristic curve. The associations between the CONUT score and the prognosis were performed using Kaplan-Meier curves and Cox regression analysis. Using the cutoff value of the CONUT score, patients were stratified into CONUT low (n = 189) and CONUT high groups (n = 28). The CONUT high group had worse overall survival (OS) (P = 0.013) and relapse-free survival (RFS) (P = 0.015). The predictive performance of CONUT was superior to the modified Glasgow prognostic score, the prognostic nutritional index, and the neutrophil-to-lymphocyte ratio. Meanwhile, the predictive performances of CONUT + tumor node metastasis (TNM) stage for 3-year OS [area under the receiver operating characteristics curve (AUC) = 0.803] and 3-year RFS (AUC = 0.752) were no less than skeletal muscle mass index (SMI) + TNM stage. The CONUT score was negatively correlated with SMI (P < 0.01). As a nutritional indicator, the CONUT score could predict long-term outcomes after radical surgery for colorectal cancer, and its predictive ability was superior to other indexes. The correlation between the CONUT score and skeletal muscle may be one of the factors that play a predictive role.

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer

Esophageal cancer, known for its high incidence and low five-year survival rate, poses significant treatment challenges. A key aspect of this challenge is the close link between mitochondria and resistance to chemoradiotherapy (CRT). Currently, there is a scarcity of biomarkers for predicting CRT response and prognosis in esophageal cancer. Our study addresses this gap by developing a prognostic model that incorporates mitochondria-related CRT resistance (MRCRTR) genes, including CTSL, TBL1X, CLN8, MMP1, PDPN, and MRPL37. Survival analysis using Kaplan–Meier curves reveals that patients with high MRCRTR scores have lower survival rates than those with low scores. Utilizing a nomogram, we successfully predict the one-, two-, and three-year overall survival rates for esophageal cancer patients. Cox regression analysis confirms the MRCRTR score as an independent prognostic factor. Furthermore, our single-cell and correlation analyses suggested that MRCRTR genes might influence CRT resistance by modulating the immune microenvironment and impacting angiogenesis. Our pan-cancer analysis also indicates the potential applicability of MRCRTR scores to head and neck squamous cell carcinoma. The validation of these findings, conducted with samples from Xiang-ya Hospital, aligns closely with our bioinformatics results. Our study not only explores the role of MRCRTR genes in predicting the prognosis of esophageal cancer but also enhances the understanding of the interplay between CRT, mitochondria, and patient outcomes.

Abstract B042: Analysis of predictive indicators of invasive breast cancer: Modeling survival rates

Abstract Objective: We aim to investigate indicators of risk for invasive breast cancer (IBC) and to predict and assess the impact of these risk indicators on survival rates in breast cancer (BC) patients who received various therapies. Background: It has been identified that several indicators including BC subtypes, age at diagnosis, history of breast disease, menopausal status, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth receptor (HER2) status, tumor stage, tumor size, among others, can cause genomic alterations and driver mutations in BC, leading to invasive carcinogenesis. To assure improved BC outcomes, we investigate the influence of BC indicators on survival rates in patients receiving hormone therapy, chemotherapy and radiotherapy. Methods: This study uses the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which included 31 clinical indicators, m-RNA levels z-score for 331 genes, and mutation in 175 genes for 1904 BC patients. Both bivariate and multivariate associations were examined using logistic regression and Cox proportional hazard models. The multivariate logistic regression analysis is used to determine predictors of IBC. The Cox proportional hazard model was also used to evaluate the magnitude of the impacts of clinical and demographic variables on IBC, as well as the effects of therapy on survival. All analyses for this investigation were carried out using SAS. A p-value &amp;lt; 0.05 was considered statistically significant. Results: We examined 1,043 patients (with the median age 61±13.1 years) from a total of 1904 for this study: 78.8% of patients had breast invasive ductal carcinoma; 87.4% were HER2 negative; 22.8%, 60.7%, and 67.6% received chemotherapy, hormone therapy, and radiotherapy treatments, respectively; 56.9% died but only 34.6% died from the disease; the average overall survival months was 127.3±77.6 months; and the most common age group was "65+ years" (39.4%). Chemotherapy (p=0.0474), neoplasm histologic grade (p=0.0015), Nottingham prognostic index (p&amp;lt;.0001), radiation (p=0.0068), and BReast CAncer gene 1 (BRCA1) (p=0.0251) were all associated with IBC. Overall survival was also correlated to age at diagnosis (p=0.0002), neoplasm histologic grade (p&amp;lt;.0001), lymph nodes (p&amp;lt;.0001), mutation count (p&amp;lt;.0001), Nottingham prognostic index (p&amp;lt;.0001), radiotherapy (p=0005), tumor size (p&amp;lt;.0001), tumor stage (p&amp;lt;.0001), BRCA1 (p=0.250), PTEN (p&amp;lt;.0001), and TP53 (p=0.0248). The overall accuracy of the model for each IBC and survival status was 82.1% and 74.2%, respectively. Conclusion: Our analysis demonstrated the importance of predictive analysis using the logistic regression model and survival analysis using the Cox proportional hazard model in cancer research. Predictive and survival analysis results aided in predicting the invasiveness and survival rate of BC among the study participants. In general, predictive and survival analyses are more accurate and may be used to make more realistic treatment decisions and precision oncology. Citation Format: Olumide Arigbede, Gebre-Egziabhe Kiros. Analysis of predictive indicators of invasive breast cancer: Modeling survival rates [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B042.

Evaluating the Prognostic Factors and Survival Rates of Endometrial Cancer Patients in a Tertiary Referral Hospital in Northeast Thailand.

This study aims to determine the 5-year and 10-year overall survival rates, mortality incidence, median survival time, and factors influencing the survival of endometrial cancer (EC) patients' post-diagnosis at the largest hospital in northeast Thailand. We particularly focus on the impact of access to health insurance schemes. We conducted a retrospective analysis of data from EC patients admitted to Srinagarind Hospital between 2010 and 2019. Overall survival was estimated using the Kaplan-Meier method. Multivariate Cox regression analysis identified factors associated with survival, with results expressed as adjusted hazard ratios (AHR) and 95% confidence intervals (CI). Among the 673 patients, the 5-year overall survival rate stood at 76.43% (95% CI: 72.72-79.70), and the 10-year rate at 67.86% (95% CI: 62.98-72.25). Notably, advanced age (≥60 years), stage III and IV cancer, and non-endometrioid histopathology were found to significantly increase post-diagnosis mortality risk (AHR = 2.39, 3.13, 4.62; 95% CI: 1.03-5.53, 2.07-4.74, 2.66-8.04; p-value <0.05, <0.001, <0.001). Surprisingly, we observed no significant correlation between health insurance schemes and mortality risk, suggesting that different insurance programs did not significantly affect EC patient survival in this study. health insurance schemes had no significant impact on endometrial cancer patient outcomes in Thailand, likely due to comprehensive coverage. Treatment modalities, notably surgery, showed no statistically significant differences, possibly due to early diagnosis. High-risk groups may benefit from adjuvant therapy. Early surgical intervention is crucial, with its association with disease stage emphasized. These findings inform cancer care decisions and healthcare policy development.<br />.