Cancer Staging Classification Research Articles

A study of the clinical significance of mSEPT9 in monitoring recurrence and prognosis in patients with surgically treated colorectal cancer.

To explore the medical significance of methylated septin9 (mSEPT9) in monitoring recurrence and prognostic assessment in individuals with surgically treated colorectal cancer (CRC). To investigate the role of Septin9 in colorectal cancer, we utilized the TIMER2.0 database to analyze its differential expression between tumor tissues and adjacent normal tissues. Colorectal cancer RNA-seq data from the TCGA database was downloaded and curated. The clinical relevance of mSEPT9 in colorectal cancer was explored by examining the correlation between Septin9 methylation levels and clinical characteristics using UALCAN and MethSurv software. Peripheral blood samples were obtained from 130 CRC subjects who underwent surgery for the detection of mSEPT9 and carcinoembryonic antigen (CEA) expression, along with collection of clinicopathological data such as age, gender, tumor site, TNM stage, and tumor differentiation. Patients were followed up for at least 3 years post-surgery until the death or final follow-up dates (31/12/2022). Additionally, peripheral blood samples were collected from 30 colorectal cancer surgery patients for mSEPT9 detection before and 7 days after surgery. Through bioinformatic database analysis, we identified higher expression levels of SEPT9 mRNA in most tumor tissues compared to normal tissues. Similarly, both paired and unpaired CRC tissues exhibited elevated expression of Septin9 when compared to normal tissues. Following GO and KEGG analysis of Septin9 target genes, we discovered their significant associations with ncRNA metabolic processes, ribonucleoprotein complex biogenesis, spliceosomes, and viral carcinogenesis. Furthermore, the overexpression of mSeptin9 was observed in CRC tissues, and it demonstrated a correlation with colon cancer staging and histologic classification. In our clinical sample study, The positive rate of mSEPT9 in CRC patients 7 days after surgery was 43.44% lower than that of preoperative. The differences in TNM stage, tumor differentiation degree, and preoperative CEA expression level between the preoperative mSEPT9 positive and negative groups of CRC were statistically significant (P < 0.05). Recurrence free survival (RFS) and overall survival (OS) were shorter in the preoperative mSEPT9-positive group, meaning preoperative mSEPT9 status was a risk factor for CRC recurrence and prognosis (P < 0.05). The sensitivity, specificity, and AUC value of preoperative mSEPT9 and CEA levels for predicting postoperative recurrence in CRC patients were 88% vs. 72%, 56.19% vs. 55.24%, and 0.721 vs. 0.636 respectively, well the AUC value of the combined prediction of postoperative recurrence was 0.758. The detection of mSEPT9 combined with CEA in preoperative plasma helps predict recurrence in colorectal cancer patients.

A Graph-Informed Modeling Framework Empowering Gene Pathway Discovery.

This study introduces a novel graph-informed modeling framework for improving the statistical analysis of gene expression data, particularly in the context of identifying differentially expressed gene pathways and gene expression-assisted disease classification in a high-dimensional data setting. By integrating gene regulatory network information into hypothesis testing for the difference between mean vectors and linear discriminant analysis, we aim to effectively capture and utilize previously validated external gene interaction information. Our method leverages a block-coordinate descent approach which enables us to incorporate mixed graph information into linear structural equation modeling, accommodating directed/undirected edges and potential cycles in gene regulatory networks. Extensive simulations under various data scenarios have demonstrated the effectiveness of our approach with improved power for gene pathway tests and disease classification over existing methods. An application to a lung cancer dataset from the Cancer Genome Atlas Program (TCGA) further exemplifies the potential of our graph-informed approach in empowering the detection of differentially expressed gene pathways and gene expression-based classification of different lung cancer stages. Our findings underscore the potential utility of incorporating gene regulatory network information in gene pathway analysis, setting the stage for future advancements in gene pathway discovery, disease diagnosis, and treatment strategies.

A Novel Ensemble Deep Learning Based Polyp Detection Using Colonoscopy Dataset

This work addresses the critical task of polyp detection and classification using the SUN colonoscopy video database, which consists of still images annotated with bounding boxes. These images categorize frames into polyp and non-polyp and encompass six distinct classes of polyps: Hyperplastic polyp, Sessile serrated lesion, Low-grade adenoma, Traditional serrated adenoma, High-grade adenoma, and Invasive carcinoma. The approach involves a two-stage classification process. Initially, MobileNetV2 is employed to distinguish between polyp and non-polyp frames. Subsequently, ResNet50 and GoogLeNet are utilized to classify the identified polyps into the six predefined categories. Data augmentation techniques are implemented to address the inherent imbalance in class distribution within the dataset, enhancing model performance and generalizability. The results highlight the effectiveness of GoogLeNet, which achieved an impressive accuracy of 98%, significantly outperforming ResNet50's accuracy of 76.16%. This substantial improvement underscores the potential of GoogLeNet in enhancing the accuracy of polyp classification. The significance of this work lies in its contribution to advancing automated polyp detection and cancer stage classification, crucial for early diagnosis and treatment. These findings provide a foundation for further research and development in this domain, with the potential to improve clinical outcomes through more accurate and timely identification of colorectal polyps.

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.

The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the Classification of Residual Tumor After Resection for the Forthcoming (Ninth) Edition of the TNM Classification of Lung Cancer

IntroductionThe goal of surgical resection is to completely remove a cancer; it is useful to have a system to describe how well this was accomplished. This is captured by the residual tumor (R) classification, which is separate from the TNM classification that describes the anatomic extent of a cancer independent of treatment. The traditional R-classification designates as R0 a complete resection, as R1 a macroscopically complete resection but with microscopic tumor at the surgical margin, and as R2 a resection that leaves gross tumor behind. For lung cancer, an additional category encompasses situations in which the presence of residual tumor is uncertain. MethodsThis paper represents a comprehensive review of evidence regarding these R categories and the descriptors thereof, focusing on studies published after the year 2000 and with adjustment for potential confounders. ResultsConsistent discrimination between complete, uncertain, and incomplete resection is revealed with respect to overall survival. Evidence regarding specific descriptors is generally somewhat limited and only partially consistent; nevertheless, the data suggest retaining all descriptors but with clarifications to address ambiguities. ConclusionOn the basis of this review, the R-classification for the ninth edition of stage classification of lung cancer is proposed to retain the same overall framework and descriptors, with more precise definitions of descriptors. These refinements should facilitate application and further research.

Challenges in Reducing Bias Using Post-Processing Fairness for Breast Cancer Stage Classification with Deep Learning.

Breast cancer is the most common cancer affecting women globally. Despite the significant impact of deep learning models on breast cancer diagnosis and treatment, achieving fairness or equitable outcomes across diverse populations remains a challenge when some demographic groups are underrepresented in the training data. We quantified the bias of models trained to predict breast cancer stage from a dataset consisting of 1000 biopsies from 842 patients provided by AIM-Ahead (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity). Notably, the majority of data (over 70%) were from White patients. We found that prior to post-processing adjustments, all deep learning models we trained consistently performed better for White patients than for non-White patients. After model calibration, we observed mixed results, with only some models demonstrating improved performance. This work provides a case study of bias in breast cancer medical imaging models and highlights the challenges in using post-processing to attempt to achieve fairness.

The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groups in the Forthcoming (Ninth) Edition of the TNM Classification for Lung Cancer

IntroductionThe TNM classification of lung cancer is periodically revised. The International Association for the Study of Lung Cancer collected and analyzed a new database to inform the forthcoming ninth edition of the TNM classification. The results are herewith presented. MethodsAfter exclusions, 76,518 patients from a total of 124,581 registered patients were available for analyses: 58,193 with clinical stage, 39,192 with pathologic stage, and 62,611 with best stage NSCLC. The proposed new N2 subcategories (N2a, involvement of single ipsilateral mediastinal or subcarinal nodal station, and N2b, involvement of multiple ipsilateral mediastinal nodal stations with or without involvement of the subcarinal nodal station) and the new M1c subcategories (M1c1, multiple extrathoracic metastases in one organ system, and M1c2, multiple extrathoracic metastases in multiple organ systems) were considered in the survival analyses. Several potential stage groupings were evaluated, using multiple analyses, including recursive partitioning, assessment of homogeneity within and discrimination between potential groups, clinical and statistical significance of survival differences, multivariable regression, and broad assessment of generalizability. ResultsT1N1, T1N2a, and T3N2a subgroups are assigned to IIA, IIB, and IIIA stage groups, respectively. T2aN2b and T2bN2b subgroups are assigned to IIIB. M1c1 and M1c2 remain in stage group IVB. Analyses reveal consistent ordering, discrimination of prognosis, and broad generalizability of the proposed ninth edition stage classification of lung cancer. ConclusionsThe proposed stages for the ninth edition TNM improve the granularity of nomenclature about anatomic extent that has benefits as treatment approaches become increasingly differentiated and complex.

Cancer Guard: Early Detection of Breast Cancer

Breast cancer stands as the most prevalent form of cancer among women, globally contributing to the highest number of cancer-related deaths. Timely detection of abnormalities significantly enhances the prospects of successful treatment and reduces mortality rates. Hence an automatic detection will be very useful for medical practitioner. This research introduces a novel framework for enhancing breast cancer detection and stage classification by integrating image processing techniques such as Gray Level Co-occurrence Matrix (GLCM) and Convolutional Neural Network (CNN) techniques. Initially, mammographic images undergo preprocessing to improve quality, followed by GLCM feature extraction for capturing textural information. With the help of GLCM technique, accuracy of the network can be increased by extracting various features. A CNN model is then employed for automatic feature learning and classification. This framework enhances the accuracy of distinguishing between malignant and benign tissues and extends to stage detection, enabling classification into various stages. Experimental results demonstrate the effectiveness of the proposed approach in achieving high precision and recall rates, suggesting potential for clinical integration to improve patient outcomes and streamline healthcare workflows.

Oral Cancer Stage Classification Using Machine Learning

Oral cancer (OC)is frequently occurring cancer that affects various areas within the oral cavity. Despite the advancement of sophisticated diagnostic and therapeutic techniques, the morbidity and mortality rates associated with oral cancer continue to be concerning. OC can affect a person’s quality of life. Beforehand identification of oral cancer can greatly reduce the need for aggressive treatment. In recent times machine learning ways has shown promising results in the identification of oral cancer. In this study, we have applied machine learning techniques such as SVM, XG Boost, and Random Forest (RF) in the stage classification of oral cancer. Our dataset contains a collection of oral images obtained from different levels of oral cancer patients from SMS hospital. We extracted various features from these images, such as color, texture, and shape, and used them as input data in our machine-learning model. We have calculated various measures like accuracy, precision, and F1 score and found that accuracy Random Forest have outperformed than SVM and XG Boost by achieving accuracy as 87.1%.

Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare.

Skin conditions in humans can be challenging to diagnose. Skin cancer manifests itself without warning. In the future, these illnesses, which have been an issue for many, will be identified and treated. With the rapid expansion of big data healthcare framework summarization and precise prediction in early stage skin cancer diagnosis, the fuzzy AHP technique produces the best results in both of these fields. Big data is a potent technology that enhances the standard of research and generates better results more rapidly. This essay gives a way to group the stages of skin cancer treatment based on this information. The combination of support vector machine multi-class classification and fuzzy selector with radial basis function-based binary migration classification of virtual machines is put through a number of experiments. The connections have been categorized. These examinations have determined whether the tumors are malignant or benign and how malignant they are. The images of spots on the skin acquired from laboratory images make up the data set used for processing. We have talked about how to handle and process large datasets in the area of classification using MATLAB, like skin spot images. Our technique outperforms competing approaches by maintaining stability even as the size of the data set grows rapidly and with little error. In comparison to other methods, the suggested approach meets the accuracy criterion for correct classifications with a score of 90.86%. As a result, the proposed solution is viewed as a potentially useful tool for identifying mass stages and categorizing skin cancer severity.

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

Platelet count and breast cancer stage.

The relationship between increased platelet count and cancer classification stage has long been established. The prevalence of thrombocytosis varies from 10% to 57% in cancer patients. The pathogenesis of thrombocytosis in malignancy is uncertain. However, there is evidence that tumor cells secrete humoral factors that can cause thrombocytosis. Preoperative thrombocytosis is a poor prognostic variable in malignancies. This study investigated the correlation between platelet count and breast cancer stage. This cross-sectional study was conducted from February 2020 to January 2021. Patient data were collected from medical records. The study population comprised breast cancer patients at Dr. Wahidin Sudirohusodo Makassar. The staging examinations were based on the tumor, node, metastasis (TNM) classification according to the American Joint Committee on Cancer (AJCC) 8th Edition. The study group comprised 171 breast cancer patients of varying ages. Metastasis was present in five (2.92%) patients and absent in 166 (97.8%) patients. Analyses found no statistically significant differences between the three staging groups based on the platelet count (p =0.952). There was no statistically significant relationship between increased platelet count and staging according to the TNM classification in breast cancer patients.

Machine Learning‐Based Lung Cancer Detection Using Multiview Image Registration and Fusion

The exact lung cancer identification is a critical problem that has attracted the researchers’ attention. The practice of multiview single image and segmentation has been widely used for the last 2 years to improve the identification of lung cancer disease. The utilization of machine learning (ML) and deep learning (DL) techniques can significantly expedite the process of cancer detection and stage classification, enabling researchers to study a larger number of patients in a shorter time frame and at a reduced cost applying the image segmentation approach herein, the multiresolution rigid registration mechanism is applied to enhance the segmentation further. Techniques like principle component averaging and discrete wavelet transform are verified for the image fusion development. To review the performance of the suggested technique, the image database resource initiative‐based lungs image database consortium is tested in this paper which includes 4,682 computed tomography scan images of 61 patients with nodules sizes from 3 to 30 mm. According to the study finding, the outperformed results of our model are obtained in terms of feature mutual information, and peak signal‐to‐noise ratio, which were recorded at 0.80 and 19.25, respectively. Moreover, the detection and stages of cancer (STG‐1, STG‐2, STG‐3, and STG‐4) of lung nodules are also assessed by using the ResNet‐18 convolutional neural network classifier. With only 1.8 FP/scan, the achieved accuracy and sensitivity for detection are 98.2% and 96.4%, respectively. The study’s findings show that our proposed strategy outperforms existing models significantly. Therefore, the proposed models have the potential to be implemented in clinical settings to provide support to doctors in the early diagnosis of cancer, while minimizing the occurrence of false positives in scans.

LCSCNet: A multi-level approach for lung cancer stage classification using 3D dense convolutional neural networks with concurrent squeeze-and-excitation module

Lung cancer, the deadliest disease worldwide, poses a massive threat to humankind. Various researchers have designed Computer-Aided-Diagnosis systems for the early-stage detection of lung cancer. However, patients are primarily diagnosed in advanced stages when treatment becomes complicated and dependent on multiple factors like size, nature, location of the tumor, and proper cancer staging. TNM (Tumor, Node, and Metastasis) staging provides all this information. This study aims to develop a novel and efficient approach to classify lung cancer stages based on TNM standards. We propose a multi-level 3D deep convolutional neural network, LCSCNet (Lung Cancer Stage Classification Network). The proposed network architecture consists of three similar classifier networks to classify three labels, T, N, and M-labels. First, we pre-process the data, in which the CT images are augmented, and the label files are processed to get the corresponding TNM labels. For the classification network, we implement a dense convolutional neural network with a concurrent squeeze & excitation module and asymmetric convolutions for classifying each label separately. The overall stage is determined by combining all three labels. The concurrent squeeze & excitation module helps the network focus on the essential information of the image, due to which the classification performance is enhanced. The asymmetric convolutions are introduced to reduce the computation complexity of the network. Two publicly available datasets are used for this study. We achieved average accuracies of 96.23% for T-Stage, 97.63% for N-Stage, and 96.92% for M-Stage classification. Furthermore, an overall stage classification accuracy of 97% is achieved.

Long-Term Prognosis and Prognostic Indicators of Stage IA Lung Adenocarcinoma.

The 8th edition of the TNM stage classification of lung cancer was developed based on an evaluation of the 5-year prognosis using an international database. Since recurrence after 5 years postoperatively is known to develop, the applicability of the stage classification beyond 5 years after treatment needs to be evaluated. Postoperative prognosis and prognostic indicators were analyzed using data for 648 patients of pathological stage IA adenocarcinoma, who underwent complete resection between 2007 and 2012. The median age was 66 years (interquartile range 60-73 years), and the median follow-up duration was 100 months (interquartile range 70-116 months). Overall survival probabilities for pathological stage IA1, IA2, and IA3 patients were 100%, 96.3%, and 91.5% at 5 postoperative years, and 94.2%, 89.8%, and 83.5% at 10 postoperative years, respectively (IA1 vs IA2: p = 0.05; IA2 vs IA3: p = 0.05). Multivariate analysis for overall survival of patients who survived without recurrence for 5 postoperative years revealed that age (hazard ratio 3.21, p = 0.02) was the only factor that was significantly associated with long-term survival. Stage classification (IA1, IA2, or IA3) was not an associated factor. The incidence of secondary primary lung cancer continued to increase, resulting in an estimated probability of 8.6% at 10 postoperative years. For patients who survived without recurrence for 5 postoperative years, age, not stage classification, was associated with survival thereafter. The long-term follow-up strategy does not need to be modified according to the stage classification, and screening for secondary primary lung cancer should be considered.

Clinicopathological Features, Staging Classification, and Clinical Outcomes of Esophageal Melanoma: Evaluation of a Pooled Case Series.

Studies that have attempted to validate the staging systems and the predictors of survival for patients with primary malignant melanoma of the esophagus (PMME) have been underpowered given their scarcity and small scale. We aimed to review a large number of PMME cases to know more about its clinicopathological features, TNM staging systems, and survival predictors of PMME. Case reports on PMME were extracted from PubMed/Medline through bibliography search and our center. A total of 287 PMME cases were identified. The majority of the patient population was male (72.08%). The most common location of PMME was the lower esophagus (50.62%) and middle esophagus (35.39%). Among the patients, 82.28% received surgical intervention. The median overall survival (OS) duration was 15 months (0.5–244). The American Joint Commission on Cancer staging classification (AJCC) for the mucosal melanoma of the upper aerodigestive tract with stage IVB and IVC integrated in stage IVA showed better distribution of OS than that for esophageal carcinoma. T stage, N stage, and surgery had significant impacts on OS duration in univariate analysis. However, only T stage and N stage were identified as independent factors for OS duration in the multivariate Cox models. PMME is an aggressive tumor with poor prognosis. The AJCC staging system for mucosal melanoma with stage IVB and IVC integrated in stage IVA may be a better option for staging PMME patients. T stage and N stage are independent factors for OS.

Retraction Note to: Classification of lung cancer stages with machine learning over big data healthcare framework

Retraction Note to: Classification of lung cancer stages with machine learning over big data healthcare framework

Prognostic Utility of Tumor Stage versus American Thyroid Association Risk Class in Thyroid Cancer.

To evaluate the prognostic strengths of American Joint Committee on Cancer (AJCC) staging and American Thyroid Association (ATA) risk classification in well-differentiated thyroid cancer (DTC), and their implications in guiding medical decision-making and epidemiological study designs. The 2004-2017 National Cancer Database was queried for DTC patients. Cox proportional hazards (CPH) and Kaplan-Meier analyses modeled patient mortality and overall survival, respectively. Each CPH model was evaluated by its concordance index, measure of explained randomness (MER), Akaike information criterion (AIC), and area under receiver operating characteristic curve (AUC). Overall, 134,226 patients were analyzed, with an average age of 48.1 ± 15.1 years (76.9% female). Univariate CPH models using AJCC staging demonstrated higher concordance indices, MERs, and AUCs than those using ATA risk classification (all p < 0.001). Multivariable CPH models using AJCC staging demonstrated higher concordance indices (p=0.049), MERs (p=0.046), and AUCs (p=0.002) than those using ATA risk classification. The AICs of multivariable AJCC staging and ATA risk models were 7.564 × 104 and 7.603 × 104 , respectively. AJCC stage I tumors were associated with greater overall survival than those classified as ATA low risk, whereas AJCC stages II-III and stage IV tumors demonstrated worse survival than ATA intermediate- and high-risk tumors, respectively (all p < 0.001). AJCC staging may be a more predictive system for patient survival than ATA risk. The prognostic utility of these two systems converges when additional demographic and clinical factors are considered. AJCC staging was found to classify patients across a wider range of survival patterns than the ATA risk stratification system. 4 Laryngoscope, 133:205-211, 2023.

Comprehensive Analysis of PDLIM3 Expression Profile, Prognostic Value, and Correlations with Immune Infiltrates in Gastric Cancer.

Protein PDZ and LIM domain 3 (PDLIM3) is a cytoskeletal protein, colocalizing with α-actinin on the Z line of mature muscle fibers. It plays a key role in dilated cardiomyopathy (DCM), muscular dystrophy, and tumor progression. However, correlations between PDLIM3 expression, prognosis, and tumor-infiltrating immune cells in gastric cancer are unknown. Therefore, we leveraged the Oncomine, GEPIA, GEO, and HPA databases to evaluate PDLIM3 expression in tumors. We also quantified PDLIM3 expression in 15 matched pairs of gastric tumor and nontumor tissues by immunohistochemistry. The Kaplan-Meier method was employed to determine the relationship between PDLIM3 expression and clinical outcomes. GO and KEGG analyses were performed to illuminate the molecular mechanisms of action of PDLIM3. TIMER2.0 and GEPIA were applied to investigate correlations between PDLIM3 expression and gene marker subsets signifying immune infiltration, with TIMER2.0 exploring the correlations between PDLIM3 and related signaling pathways. Gastric cancer tissues were found to express more PDLIM3 than nontumor tissues. PDLIM3 overexpression was associated with shorter OS and PFS of gastric cancer patients (OS HR = 2.02, P = 9.8e − 10; PFS HR = 1.77, P = 7.5e − 06). PDLIM3 was also positively correlated with worse OS and PFS according to gastric cancer staging, Her-2 overexpression, differentiation grade, and Lauren classification. PDLIM3 was shown to be associated with immunological responses by GO, while it was related to PI3K/Akt signal pathways by KEGG analysis. Furthermore, increased PDLIM3 expression was significantly correlated with greater infiltration of CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells. PDLIM3 expression had significant positive correlations with a variety of immune marker subsets. Finally, correlations were found between PDLIM3 and crucial markers of signaling pathways involving PI3K/Akt and p38 MAPK. Thus, upregulation of PDLIM3 was significantly associated with poor prognosis, immune cell infiltration, and activation of two key signal pathways in gastric cancer. We propose that PDLIM3 could be used as a biomarker to predict prognosis and immune cell infiltration in gastric cancer.

210P Worsening of breast cancer patients’ condition: What happened during COVID-19? A meta-analysis

COVID-19 has impacted every aspect of our lives. Flooding of hospitals with COVID-19 patients have affected quality of care given to breast cancer patients. Our aim is to assess the impact of COVID-19 on presenting cases of breast cancer and worsening of breast cancer stage and/or TNM classification.