Predictor Of Prognosis In Patients Research Articles

Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.

Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.

CDKL3 is a promising biomarker for diagnosis and prognosis prediction in patients with hepatocellular carcinoma.

Cyclin-dependent kinase-like 3 (CDKL3) has been identified as an oncogene in certain types of tumors. Nonetheless, its function in hepatocellular carcinoma (HCC) is poorly understood. In this study, we conducted a comprehensive analysis of CDKL3 based on data from the HCC cohort of The Cancer Genome Atlas (TCGA). Our analysis included gene expression, diagnosis, prognosis, functional enrichment, tumor microenvironment and metabolic characteristics, tumor burden, mRNA expression-based stemness, alternative splicing, and prediction of therapy response. Additionally, we performed a cell counting kit-8 assay, TdT-mediated dUTP nick-end Labeling staining, migration assay, wound healing assay, colony formation assay, and nude mouse experiments to confirm the functional relevance of CDKL3 in HCC. Our findings showed that CDKL3 was significantly upregulated in HCC patients compared to controls. Various bioinformatic analyses suggested that CDKL3 could serve as a potential marker for HCC diagnosis and prognosis. Furthermore, CDKL3 was found to be involved in various mechanisms linked to the development of HCC, including copy number variation, tumor burden, genomic heterogeneity, cancer stemness, and alternative splicing of CDKL3. Notably, CDKL3 was also closely correlated with tumor immune cell infiltration and the expression of immune checkpoint markers. Additionally, CDKL3 was shown to independently function as a risk predictor for overall survival in HCC patients by multivariate Cox regression analysis. Furthermore, the knockdown of CDKL3 significantly inhibited cell proliferation in vitro and in vivo, indicating its role as an oncogene in HCC. Taken together, our findings suggest that CDKL3 shows promise as a biomarker for the detection and treatment outcome prediction of HCC patients.

Effect of the thyroid transcription factor 1 expression and treatment discontinuation due to adverse events on progression-free survival in patients with advanced non-squamous non-small cell lung cancer treated with pembrolizumab plus pemetrexed and platinum chemotherapy: a Japanese four-hospital, retrospective study.

Although a significant improvement in progression-free survival (PFS) has been reported in the thyroid transcription factor 1 (TTF-1) positive patients under treatment for non-squamous non-small cell lung cancer (NS-NSCLC), including immune checkpoint inhibitor therapy, the association between TTF-1 expression and adverse event occurrence remains unclear. Therefore, this study investigated the impact of TTF-1 and its adverse events on PFS during pembrolizumab plus pemetrexed and platinum chemotherapy for NS-NSCLC. Patients who received the pembrolizumab plus pemetrexed and platinum chemotherapy from 1/1/2018 to 12/31/2022 and whose TTF-1 expression was measured were included in the study. This was a retrospective study conducted using electronic medical records. The mean age of the 79 patients was 67.5 ± 8.4 years, with 75.95% patients being male. Among them, 59.49% were TTF-1 positive. PFS comparison between TTF-1-positive and -negative patients showed a trend toward longer PFS for TTF-1 positive patients, though the results were statistically insignificant (P = 0.190). Proportional hazards analysis indicated significant PFS extension from treatment interruption, as adverse events related to cancer therapy stopped (hazard ratio [HR] = 0.32, P = 0.005) and the number of anticancer agents used (HR = 0.01, P < 0.001). Additionally, pembrolizumab plus pemetrexed and platinum chemotherapy for TTF-1-positive NS-NSCLC significantly extended PFS after treatment discontinuation as related adverse events stopped (827 vs. 210 days, P = 0.021). Measurement of TTF-1 may accordingly serve as a predictor of treatment response to the pembrolizumab plus pemetrexed and platinum chemotherapy. It may also be a predictor of patient prognosis when treatment is discontinued due to related adverse events.

A 3 M Evaluation Protocol for Examining Lymph Nodes in Cancer Patients: Multi-Modal, Multi-Omics, Multi-Stage Approach.

Through meticulous examination of lymph nodes, the stage and severity of cancer can be determined. This information is invaluable for doctors to select the most appropriate treatment plan and predict patient prognosis; however, any oversight in the examination of lymph nodes may lead to cancer metastasis and poor prognosis. In this review, we summarize a significant number of articles supported by statistical data and clinical experience, proposing a standardized evaluation protocol for lymph nodes. This protocol begins with preoperative imaging to assess the presence of lymph node metastasis. Radiomics has replaced the single-modality approach, and deep learning models have been constructed to assist in image analysis with superior performance to that of the human eye. The focus of this review lies in intraoperative lymphadenectomy. Multiple international authorities have recommended specific numbers for lymphadenectomy in various cancers, providing surgeons with clear guidelines. These numbers are calculated by applying various statistical methods and real-world data. In the third chapter, we mention the growing concern about immune impairment caused by lymph node dissection, as the lack of CD8 memory T cells may have a negative impact on postoperative immunotherapy. Both excessive and less lymph node dissection have led to conflicting findings on postoperative immunotherapy. In conclusion, we propose a protocol that can be referenced by surgeons. With the systematic management of lymph nodes, we can control tumor progression with the greatest possible likelihood, optimize the preoperative examination process, reduce intraoperative risks, and improve postoperative quality of life.

CCL21 and CLDN11 Are Key Driving Factors of Lymph Node Metastasis in Gastric Cancer.

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. Understanding the molecular mechanisms of GC metastasis is crucial for improving patient survival outcomes. RNA sequencing and analysis were performed on tissue samples from primary and lymph node metastatic lesions of gastric cancer. Differential gene analysis and functional pathway analysis were conducted. Immune infiltrating environment and protein expression levels were evaluated using immunohistochemistry. Cell experiments were conducted to investigate the role of CCL21 in GC metastasis. ACTG2, CNN1, DES, MUC6, and PGC were significantly upregulated in primary tumor cells, while CCL21, MS4A1, CR2, CLDN11, and FDCSP were significantly upregulated in metastatic tumor cells. Functional pathway analysis revealed enrichment in pathways related to immune response. CLDN11 and CCL21 were found to play important roles in promoting gastric cancer metastasis. Cell experiments confirmed the role of CCL21 in promoting GC cell growth and metastasis. CCL21 is highly expressed in GC tissues and binds to CCR7, leading to upregulation of CLDN11. This results in GC-lymph node metastasis and abnormal activation of immune cells (B cells and CD4+ T cells). Inhibition of CCL21 and CLDN11 proteins may be a promising strategy for treating GC and preventing lymph node metastasis. These findings provide specific molecular markers for early lymph node metastases of GC, which can aid in developing treatment strategies and predicting patient prognosis.

CRYL1 is a Potential Prognostic Biomarker of Clear Cell Renal Cell Carcinoma Correlated with Immune Infiltration and Cuproptosis.

Clear cell renal cell carcinoma (ccRCC) is a widespread urogenital neoplasm. However, the therapeutic efficacy of these methods is unsatisfactory. In-depth screening of biomarkers could aid early diagnosis and therapy and predict patient prognosis. The GEO datasets were selected with specific criteria. Differentially expressed gene (DEG), weighted gene coexpression network analysis (WGCNA), protein-protein interaction, LASSO, random forest, and Cox regression analyses were applied to identify the independent prognostic biomarkers. Survival analysis, correlation with clinical features, gene set enrichment analysis (GSEA), GO enrichment, immune infiltration analysis, and correlation with cuproptosis-related genes were carried out to determine the prognostic value and possible molecular mechanisms of the TSVR. Wound healing assays, transwell assays, cell colony formation experiments, flow cytometry, and immunohistochemistry (IHC) analysis were used to validate the functional attributes of CRYL1. Four GEO datasets were included to screen for hub genes. DEG combined with WGCNA showed a key module with 300 genes having the strongest correlation with "survival state" (R2 = -0.24 and P = 7e-8); six genes were identified by LASSO, random forest, and Cytoscape. Finally, CRYL1 (hazard ratio (HR) = 2.01, P < 0.001) was selected as an independent prognostic biomarker. The higher CRYL1 expression group had better DFS and overall survival (OS). GSEA demonstrated that the CRYL1-related DEGs were enriched mainly in the metabolism of sugar, fat, and amino acids. CRYL1 is positively correlated with FDX1 and the LIAS pathway, which are important molecule involved in cuproptosis. CRYL1 affects the infiltration abundance of four immune cells and can predict a positive OS. Wound healing, transwell, cell colony formation, and flow cytometry assays demonstrated that CRYL1 silencing enhances migration and proliferation and leads to a decreased apoptotic ratio. IHC analysis suggested that CRYL1 was highly expressed in adjacent tissues. CRYL1 is a robust predictive marker for clinicopathological characteristics and survival status in ccRCC patients.

Prognostic value of large amino acid transporter type 1 (LAT1) expression in pulmonary adenocarcinoma: A tissue microarray study

BackgroundLarge amino acid transporter type 1 (LAT1) provides cancer cells with essential amino acids for both protein synthesis and cell growth and may predict patient prognosis. Additionally, LAT1 inhibition can be a therapeutic target. This study aimed to examine the prognostic significance of LAT1 expression in lung cancer, paying special attention to adenocarcinoma subtypes. MethodsTissue microarrays (TMA) of 1,560 total cores obtained from surgically resected lung cancer specimens between 1995 and 2008 at our hospital were used. Overall, 795 cases of adenocarcinoma were identified, and 717 underwent further evaluation. Immunohistochemical staining of whole slides and TMA cores were assessed to set H-score cutoff value.. Immunohistochemical expression of LAT1 was examined based on the subtypes of adenocarcinoma. Statistical analyses explored the prognostic significance of LAT1. ResultsAdenocarcinoma accounted for 71.8% of all cases (n = 795), and 216 cases (27.1%) expressed LAT1. The 795 cases were categorized into five subtypes: lepidic (n = 29, 3.6%), papillary (n = 601, 75.6%), acinar (n = 58, 7.3%), and solid (n = 9, 1.1%); 717 of the 795 cases were further assessed according to the exclusion criteria. The LAT1-positive ratio increased as the architectural grade increased. Notably, in papillary adenocarcinoma, the LAT1-positive group had significantly lower overall survival compared to the negative group (10-year survival: 45.6% vs. 60.8%, p < 0.001). ConclusionLAT1 expression was higher in high-grade subtypes of pulmonary adenocarcinoma. Moreover, LAT1 expression is useful for predicting prognosis, particularly in papillary adenocarcinoma, facilitating prognostic stratification of papillary adenocarcinoma.

Dendritic Cell-Related Gene Signatures in Hepatocellular Carcinoma: An Analysis for Prognosis and Therapy Efficacy Evaluation.

This study aimed to identify dendritic cells (DCs) related genes in hepatocellular carcinoma (HCC) patients, establish DC-related subtypes and signatures, and correlate them with prognosis and treatment response. DC-related genes were screened using Weighted Gene Co-expression Network Analysis (WGCNA) based on RNA sequencing from the TCGA (374 samples), GSE14520 (242 samples), and GSE76427 datasets (115 samples), following immune infiltration assessment by the TIME method. Two DC-related subtypes in HCC were identified through unsupervised clustering. A DC-related signature (DCRS) predictive of overall survival was constructed using LASSO and Cox regression models, and validated across the three datasets. Additionally, genetic mutation characteristics, immune infiltration levels, and treatment sensitivity were explored in DCRS risk groups. The expression levels of DCRS genes and risk scores were validated in the transcriptome of 13 HCC patients receiving combined targeted therapy and immunotherapy in the Guangxi cohort using Wilcoxon test. A signature consisting of 13 genes related to DCs was constructed, and the superior prognostic consistency of the low DCRS risk group was validated across the TCGA (P=0.003), GSE76427 (P=0.005), and GSE14520 (P=0.047) datasets. Furthermore, in the 147-sample transarterial chemoembolization (TACE) treatment dataset GSE104580, the response group exhibited lower risk scores than the non-response group (P=0.01), whereas in the 140-sample Sorafenib treatment dataset GSE109211 (P=0.041) and the 17-sample anti-PD-1 treatment dataset GSE202069 (P=0.027), the risk scores were higher in the response group. We also validated the gene expression levels of DCRS and the higher risk scores in the response group of the Guangxi cohort (P=0.034). A DCRS consisting of 13 genes was established in HCC, facilitating the prediction of patient prognosis and responsiveness to TACE, targeted therapy, and immunotherapy.

Risk factors and a model for prognosis prediction after intravenous thrombolysis with alteplase in acute ischemic stroke based on propensity score matching.

Background: Alteplase intravenous thrombolysis is effective for treating acute ischemic stroke (AIS) within 4.5 h. Nevertheless, the prognosis remains poor for some patients.Objective: To investigate the risk factors for poor prognosis in patients undergoing intravenous thrombolysis with alteplase following AIS based on propensity score matching and to develop a predictive model.Result: Multivariate logistic regression analysis showed that baseline blood glucose (OR = 1.20, 95%CI, 1.03-1.39), baseline NIH Stroke Scale score (OR = 1.23, 95%CI, 1.12-1.35), and hyperlipidemia (OR = 6.60, 95%CI 1.74-25.00) were risk factors for poor prognosis in patients with AIS undergoing alteplase intravenous thrombolysis. Using these factors, a nomogram model was constructed for predicting patient prognosis at 3 months. The areas under the receiver operating characteristic curve (AUCs) of the training and validation groups were 0.792 (95CI% 0.715-0.870) and 0.885 (95CI% 0.798-0.972), respectively, showing good differentiation. The Hosmer Lemeshow goodness-of-fit test showed that the model had good fit. The calibration curve fitted well with the ideal curve, and the decision curve analysis curve showed that the model had good clinical applicability when the threshold probability was between 10%-80%.Conclusion: The established nomogram could successfully predict the 3-month prognosis of patients with AIS after undergoing alteplase intravenous thrombolysis. The model thus has clinical application value.

Unraveling the role of the circadian clock genes in cervical squamous cell carcinoma and endocervical adenocarcinoma: A prognostic indicator for prognostic, immunotherapy response, and chemotherapy sensitivity.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) account for a significant proportion of gynecological malignancies and represent a major global health concern. Globally, CESC is ranked as the fourth most common cancer among women. Conventional treatment of this disease has a less favorable prognosis for most patients. However, the discovery of early molecular biomarkers is therefore important for the diagnosis of CESC, as well as for slowing down their progression process. Methods: To identify differentially expressed genes strongly associated with prognosis, univariate Cox proportional hazard analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used. Using multiple Cox proportional hazard regression, a multifactorial model for prognostic risk assessment was then created. Results: The expression of biological clock-related genes, which varied considerably among distinct subtypes and were associated with significantly diverse prognoses, was used to categorize CESC patients. These findings demonstrate how the nomogram developed based on the 7-CRGs signature may assist physicians in creating more precise, accurate, and successful treatment plans that can aid CESC patients at 1, 3, and 5 years. Conclusions: By using machine learning techniques, we thoroughly investigated the impact of CRGs on the prognosis of CESC patients in this study. By creating a unique nomogram, we were able to accurately predict patient prognosis. At the same time, we showed new perspectives on the development of CESC and its treatment by analyzing the associations of the prognostic model with immunity, enrichment pathways, chemotherapy sensitivity, and so on. This research provides a new direction for clinical treatment.

Relationship between neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) in peripheral blood and prognosis after castration therapy for prostate cancer

Abstract Background: The relationship between neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) in peripheral blood and prognosis after castration therapy for prostate cancer remains unclear. Methods: A total of 186 patients with prostate cancer treated between January 2018 and March 2021 were selected as the study subjects. All patients underwent castration therapy. Patient follow-up records for 2 years were examined to assess progression-free survival. NLR, PLR, and PSA levels were measured in the participants’ blood. Logistic regression analysis was used to identify factors affecting the occurrence of castration-resistant prostate cancer. Kaplan-Meier survival curves were plotted to analyze progression-free survival, and ROC curves were plotted to assess the predictive value of NLR and PLR for progression-free survival. Results: In the stable group, NLR, PLR, PSA levels, bone metastasis ratio, and Gleason score ≥8 were significantly lower than in the progression group. T3 stage, N0 stage, and M0 stage were significantly higher in the progression group, with statistical significance (P &lt; 0.05). NLR, PLR, and PSA levels were all significantly linearly correlated (P &lt; 0.05). High NLR, high PLR, high PSA, high bone metastasis, Gleason score &lt;8, T3 stage, and N0 stage were independent risk factors for poor prognosis after castration therapy for prostate cancer, with statistical significance (P &lt; 0.05). Patients with low NLR had significantly better progression-free survival than the high NLR group, and patients with low PLR had significantly better progression-free survival than the high PLR group, with statistical significance (P &lt; 0.05). The area under the curve for NLR and PLR in predicting progression-free survival after castration therapy for prostate cancer was both greater than 0.90, indicating high clinical utility. Conclusion: Peripheral blood NLR and PLR after castration therapy for prostate cancer are highly correlated with patient prognosis quality and can serve as important potential indicators for predicting patient prognosis quality.

The prognostic value of preoperative peripheral blood inflammatory biomarkers in extrahepatic cholangiocarcinoma: a systematic review and meta-analysis.

Recent evidence indicates that inflammation plays a major role in the pathogenesis and progression of CCA. This meta-analysis seeks to evaluate the prognostic implications of preoperative inflammatory markers, specifically NLR, PLR, and LMR, in patients with eCCA. By focusing on these preoperative biomarkers, this study aims to provide valuable insights into their prognostic value and potential utility in clinical practice. For this analysis, comprehensive searches were conducted in PubMed, Embase, and Web of Science databases from inception to May 2024. The primary outcomes of interest focused on the association between the levels of NLR, PLR, and LMR and the prognosis of eCCA patients. Statistical analyses were conducted using STATA 17.0 software. The meta-analysis, involving 20 retrospective studies with 5553 participants, revealed significant correlations between preoperative biomarkers and the prognosis of eCCA patients. Elevated NLR, PLR, and decreased LMR levels were extensively studied regarding overall survival (OS) in eCCA patients. Elevated NLR was an independent predictor of poor OS (HR 1.86, p < 0.001), similar to elevated PLR (HR 1.76, p < 0.001), while decreased LMR predicted poor OS (HR 2.16, p < 0.001). Subgroup analyses based on eCCA subtypes and curative surgery status showed consistent results. In conclusion, our study emphasizes the clinical significance of assessing NLR, PLR, and LMR preoperatively to predict patient prognosis. Elevated NLR and PLR values, along with decreased LMR values, were linked to poorer overall survival (OS). Large-scale prospective cohort studies are required to confirm their independent prognostic value in eCCA. https://www.crd.york.ac.uk/prospero/, identifier CRD42024551031.

Crosstalk of cuproptosis-related subtypes, establishment of a prognostic signature, and immune infiltration characteristics in gastric cancer

BackgroundCuproptosis is a novel form of cellular demise that occurs through a unique pathway involving lipoylated proteins in the tricarboxylic acid (TCA) cycle and is closely linked to mitochondrial metabolism. Nevertheless, the comprehensive elucidation of the impact of carcinogenesis-associated genes (CRGs) on prognosis, tumor microenvironment (TME), and therapeutic response in patients with gastric cancer (GC) remains unclear. MethodsIn total, 1374 GC samples were gathered from three Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas database. The samples were then stratified into different subtypes through unsupervised clustering of the 13 CRG profiles. The CRG_score was developed to quantify CRG patterns of individual tumors. Subsequently, we investigated the associations among the various groups and clinicopathological features, immune infiltration features, TME mutation status, and response to immunotherapy. ResultsThe GC samples were divided into two clusters based on their distinct clinicopathological features, prognosis, and immune characteristics. Using LASSO and Cox regression analyses, 9 genes were identified for constructing a prognostic signature related to cuproptosis. The novel signature displayed outstanding durability and prognostic capability for the overall lifespan of individuals. Additionally, the expression levels of signature genes in GC tissues and adjacent normal tissues were tested by qRT-PCR. Moreover, we developed a remarkably dependable nomogram to enhance the practicality of the CRG_score in clinical settings. High tumor mutation burden, increased microsatellite instability-high, immune activation, along with good survival probability and increased immunoreactivity to immune checkpoint inhibitors, were distinguishing features of low CRG_scores. ConclusionsThe findings of this study revealed the possible impacts of CRGs on the TME, clinical and pathological characteristics, and outlook of patients with GC. This signature was strongly linked to the immune response against GC and has the potential to serve as a valuable tool for predicting patient prognosis.

Naples Prognostic Score: A Novel Predictor of Survival in Patients with Triple-Negative Breast Cancer.

This study investigated the correlation between the Naples prognostic score (NPS), clinicopathological traits, and the postoperative prognoses of patients with triple-negative breast cancer (TNBC). Based on NPS, a predictive nomogram was developed to estimate the long-term survival probabilities of patients with TNBC post-surgery. We retrospectively examined the clinical records of 223 women with TNBC treated at Ningbo Medical Center, Lihuili Hospital between January 1, 2016 and December 31, 2020. Blood tests and biochemical analyses were conducted before surgery. The prognostic nutritional index (PNI), controlling nutritional status (CONUT), neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and NPS were determined based on blood-related markers. A Kaplan-Meier survival analysis assessed the association between NPS, PNI, CONUT score, overall survival (OS), and breast cancer-specific survival (BCSS). Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) and C index. The patients were randomly divided into the training and the validation group (6:4 ratio). A nomogram prediction model was developed and evaluated using the R Software for Statistical Computing (RMS) package. NPS outperformed other scores in predicting inflammation outcomes. Patients with an elevated NPS had a poorer prognosis (P<0.001). Lymph node ratio (LNR), surgical method, postoperative chemotherapy, and NPS independently predicted OS, whereas M stage, LNR, and NPS independently predicted BCSS outcome. The OS and BCSS predicted by the nomogram model aligned well with the actual OS and BCSS. The decision curve analysis showed significant clinical utility for the nomogram model. In this study, NPS was an important prognostic indicator for patients with TNBC. The nomogram prognostic model based on NPS outperformed other prognostic scores for predicting patient prognosis. The model demonstrated a clear stratification ability for patient prognosis, which emphasized the potential benefits of early intervention for high-risk patients.

Comprehensive analysis of PPP4C's impact on prognosis, immune microenvironment, and immunotherapy response in lung adenocarcinoma using single-cell sequencing and multi-omics.

Elevated PPP4C expression has been associated with poor prognostic implications for patients suffering from lung adenocarcinoma (LUAD). The extent to which PPP4C affects immune cell infiltration in LUAD, as well as the importance of associated genes in clinical scenarios, still requires thorough investigation. In our investigation, we leveraged both single-cell and comprehensive RNA sequencing data, sourced from LUAD patients, in our analysis. This study also integrated datasets of immune-related genes from InnateDB into the framework. Our expansive evaluation employed various analytical techniques; these included pinpointing differentially expressed genes, constructing WGCNA, implementing Cox proportional hazards models. We utilized these methods to investigate the gene expression profiles of PPP4C within the context of LUAD and to clarify its potential prognostic value for patients. Subsequent steps involved validating the observed enhancement of PPP4C expression in LUAD samples through a series of experimental approaches. The array comprised immunohistochemistry staining, Western blotting, quantitative PCR, and a collection of cell-based assays aimed at evaluating the influence of PPP4C on the proliferative and migratory activities of LUAD cells. In lung cancer, elevated expression levels of PPP4C were observed, correlating with poorer patient prognoses. Validation of increased PPP4C levels in LUAD specimens was achieved using immunohistochemical techniques. Experimental investigations have substantiated the role of PPP4C in facilitating cellular proliferation and migration in LUAD contexts. Furthermore, an association was identified between the expression of PPP4C and the infiltration of immune cells in these tumors. A prognostic framework, incorporating PPP4C and immune-related genes, was developed and recognized as an autonomous predictor of survival in individuals afflicted with LUAD. This prognostic tool has demonstrated considerable efficacy in forecasting patient survival and their response to immunotherapeutic interventions. The involvement of PPP4C in LUAD is deeply intertwined with the tumor's immune microenvironment. PPP4C's over-expression is associated with negative clinical outcomes, promoting both tumor proliferation and spread. A prognostic framework based on PPP4C levels may effectively predict patient prognoses in LUAD, as well as the efficacy of immunotherapy strategy. This research sheds light on the mechanisms of immune interaction in LUAD and proposes a new strategy for treatment.

Prognostic and immunotherapeutic significances of M2 macrophage-related genes signature in lung cancer.

Objective: We aimed to investigate the immunological significance of M2 macrophage-related genes in lung cancer (LC) patients, specifically focusing on constructing a risk score to predict patient prognosis and response to immunotherapy. Methods: We developed a novel risk score by identifying and incorporating 12 M2 macrophage-related genes. The risk score was calculated by multiplying the expression levels of risk genes by their respective coefficients. Through comprehensive enrichment analysis, we explored the potential functions distinguishing high- and low-risk groups. Moreover, we examined the relationship between patients in different risk groups and immune infiltration as well as their response to immunotherapy. The single-cell RNA sequencing data were acquired to ascertain the spatial pattern of RNF130 expression. The expression of RNF130 was examined using TCGA datasets and verified by HPA. The qRT-PCR was employed to examine RNF130 expression in LC cells. Finally, in vitro experiments were carried out to validate the expression and function of RNF130. Results: Our results indicated that the risk score constructed from 12 M2 macrophage-related genes was an independent prognostic factor. Patients in the high-risk group had a significantly worse prognosis compared to those in the low-risk group. Functional enrichment analysis showed a significant relationship between the risk score and immunity. Furthermore, we explored immune infiltration in different risk groups using seven immune algorithms. The results demonstrated a negative correlation between high-risk group patients and immune infiltration of B cells, CD4+ cells, and CD8+ cells. We further validated these findings using an immunotherapy response database, which revealed that high-risk patients were more likely to exhibit immune evasion and might have poorer immunotherapy outcomes. Additionally, drug sensitivity analysis indicated that patients in the high-risk group were more sensitive to certain chemotherapeutic and targeted drugs than those in the low-risk group. Single-cell analysis indicated that macrophages were the primary site of RNF130 distribution. The results from the TCGA and HPA database demonstrated a trend toward a low expression of RNF130 in LC. Finally, in vitro experiments further validated the expression and function of RNF130 in LC cells. Conclusions: The high-risk group constructed with M2 macrophage-related genes in LC was closely associated with poor prognosis, low immune cell infiltration, and poorer response to immunotherapy. This risk score can help differentiate and predict the prognosis and immune status of LC patients, thereby aiding in the development of precise and personalized immunotherapy strategies.

Evaluation of emergency department admissions of mass casualty patients using the revised trauma score, injury severity score, and trauma and injury severity score.

There is a need for studies evaluating prognostic scoring systems in mass trauma patients in conflict regions to predict patient prognosis for emergency surgical prioritization. In this study, we aimed to evaluate scoring systems such as the Revised Trauma Score (RTS), Injury Severity Score (ISS), and Trauma and Injury Severity Score (TRISS) in trauma patients admitted due to mass trauma in Northern Syria. This study was a retrospective evaluation of patients admitted due to mass trauma to the emergency departments of hospitals in Northern Syria. The diagnostic efficiency of RTS, ISS, and TRISS scoring systems was evaluated in these admissions in the first half of 2021. The most common causes of mass trauma were bomb blast (67.3%), gunshot (28.8%), and 14 (3.9%) patients admitted with other causes. When the odds ratio (OR) was analyzed, a one-unit increase in the RTS score increased the odds of survival by a factor of 6.133, and a one-unit increase in the TRISS score increased the odds of survival by a factor of 1.057. Differently, it was found that each 1-unit increase in ISS decreased the patient's probability of survival by 0.856 units. When RTS, TRISS, and ISS scores were analyzed, the area under the ROC curve was statistically significant for all of them (p<0.001) and all of them had a diagnostic value for mortality with sensitivities of 99.0%, 94.8%, and 91.9%; specificities of 87.8%, 90.5%, and 88.6; AUC of 0.958, 0.975, and 0.958, respectively. The use of trauma scoring systems, especially TRISS, may be useful for prioritizing patients in mass casualty settings in the presence of overcapacity.

Construction and validation of molecular subtype and signature of immune cell-related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients.

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers. ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation. WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS. ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

Multi-parameter MRI radiomic features may contribute to predict progression-free survival in patients with WHO grade II meningiomas.

This study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas. Kaplan-Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC). Our study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834-1.000; Neural Network: 0.836-1.000). The Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients' conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial.

WITHDRAWN: Characterization of mutational landscape reveals influencing factors for therapeutic response and prognosis of colorectal cancer patients with synchronous liver metastases underwent simultaneous resection of primary and metastatic lesions

BackgroundPatients with stage IV colorectal cancer (CRC) with liver metastasis represent a specific group that can be treated with surgery. However, the influence of genomic alterations on the therapeutic response and prognosis was not clear in CRC patients underwent simultaneous surgery of primary and metastatic lesions. MethodsFifty-two patients underwent simultaneous surgery on primary and metastatic lesions were retrospectively recruited. The mutational landscape of primary lesion was established by whole-exome sequencing(WES). Non-parametric test, Fisher's exact test, multivariate analyses, Kaplan-Meier analyses were performed to identify risk factors for response and prognosis, and a Nomogram model was established. Analyses were performed and figures were plotted using the Graphpad PRISM 5.0 and the R software. ResultsSeveral top mutated genes were identified from the mutational landscape of primary lesions, including APC, TP53, KRAS and TTN, and many co-mutations, mutually exclusive mutations and aberrant functions or pathways were revealed. KRAS(P = 0.047) and TTN(P < 0.001) exhibited significant differences in tumor mutational burden(TMB) between mutant and wild-type groups. TP53(P = 0.045), MUC12(P = 0.012) and CEL(P = 0.032) mutational status significantly stratified the patient therapeutic response, in which MUT12 was an independent risk factor(P = 0.02). CRC location(P = 0.014), patient therapeutic response(P < 0.001), and the mutational status of ANKRD20A4(P = 0.006), EVC(P = 0.05), FHOD3(P = 0.05), MYO15A(P = 0.008) and POTEE(P < 0.001) showed significant stratification on patient prognosis, in which cancer location, response to therapy and ANKRD20A4 and EVC mutational status were independent risk factors. These factors were used to establish a Nomogram model to predict the individual patient prognosis. Internal and external validation verified the effectiveness of the model. ConclusionsIndependent risk factors including cancer location, response to therapy and ANKRD20A4 and EVC mutational status were identified and were used in the establishment of a Nomogram model for patient prognosis prediction.