Elevated RACGAP1 Expression Enhances Malignant Potential in Lung Adenocarcinoma and Serves as a Prognostic Factor.

Background: While RACGAP1 is identified as a potential oncogene, its specific role in lung adenocarcinoma (LUAD) remains unclear. Methods: First, we conducted a comprehensive analysis of the role of RACGAP1 across 33 types of cancer. Subsequently, we investigated the expression levels of RACGAP1 and its impact on prognosis using data from The Cancer Genome Atlas (TCGA) database. We utilized single-cell sequencing data to explore the tumor-related processes of RACGAP1 in LUAD and validated our findings through experimental verification. Employing a consensus clustering (CC) approach, we subdivided LUAD patients into two subtypes based on RACGAP1 cell cycle-related genes (RrCCGs). These subtypes exhibited significant differences in tumor characteristics, lymph node metastasis, and recurrence. Furthermore, we evaluated the prognostic influence of RrCCGs using univariate Cox regression and least absolute shrinkage and selection operator regression models (LASSO), successfully establishing a prognostic model. Results: RACGAP1 is frequently overexpressed in various tumors and can impact the prognosis of patients with LUAD. Additionally, experimental evidence has demonstrated that low expression of RACGAP1 favors tumor cell apoptosis and restoration of the cell cycle, while high expression promotes invasion and metastasis. Through CC analysis of RrCCGs, patients were classified into two groups, with survival analysis revealing distinct prognoses and stages between the two groups. Furthermore, Cox and LASSO regression successfully constructed a prognostic model with robust predictive capability.

Musculoskeletal Health and Work: Development and Internal-External Cross-Validation of a Model to Predict Risk of Work Absence and Presenteeism in People Seeking Primary Healthcare.

PURPOSE: To develop and validate prediction models for the risk of future work absence and level of presenteeism, in adults seeking primary healthcare with musculoskeletal disorders (MSD). METHODS: Six studies from the West-Midlands/Northwest regions of England, recruiting adults consulting primary care with MSD were included for model development and internal-external cross-validation (IECV). The primary outcome was any work absence within 6 months of their consultation. Secondary outcomes included 6-month presenteeism and 12-month work absence. Ten candidate predictors were included: age; sex; multisite pain; baseline pain score; pain duration; job type; anxiety/depression; comorbidities; absence in the previous 6 months; and baseline presenteeism. RESULTS: For the 6-month absence model, 2179 participants (215 absences) were available across five studies. Calibration was promising, although varied across studies, with a pooled calibration slope of 0.93 (95% CI: 0.41-1.46) on IECV. On average, the model discriminated well between those with work absence within 6 months, and those without (IECV-pooled C-statistic 0.76, 95% CI: 0.66-0.86). The 6-month presenteeism model, while well calibrated on average, showed some individual-level variation in predictive accuracy, and the 12-month absence model was poorly calibrated due to the small available size for model development. CONCLUSIONS: The developed models predict 6-month work absence and presenteeism with reasonable accuracy, on average, in adults consulting with MSD. The model to predict 12-month absence was poorly calibrated and is not yet ready for use in practice. This information may support shared decision-making and targeting occupational health interventions at those with a higher risk of absence or presenteeism in the 6 months following consultation. Further external validation is needed before the models' use can be recommended or their impact on patients can be fully assessed.

A prognostic model for anoikis-related genes in pancreatic cancer.

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

Identifying and evaluating a disulfidptosis-related gene signature to predict prognosis in colorectal adenocarcinoma patients.

Disulfidptosis, a regulated form of cell death, has been recently reported in cancers characterized by high SLC7A11 expression, including invasive breast carcinoma, lung adenocarcinoma, and hepatocellular carcinoma. However, its role in colon adenocarcinoma (COAD) has been infrequently discussed. In this study, we developed and validated a prognostic model based on 20 disulfidptosis-related genes (DRGs) using LASSO and Cox regression analyses. The robustness and practicality of this model were assessed via a nomogram. Subsequent correlation and enrichment analysis revealed a relationship between the risk score, several critical cancer-related biological processes, immune cell infiltration, and the expression of oncogenes and cell senescence-related genes. POU4F1, a significant component of our model, might function as an oncogene due to its upregulation in COAD tumors and its positive correlation with oncogene expression. In vitro assays demonstrated that POU4F1 knockdown noticeably decreased cell proliferation and migration but increased cell senescence in COAD cells. We further investigated the regulatory role of the DRG in disulfidptosis by culturing cells in a glucose-deprived medium. In summary, our research revealed and confirmed a DRG-based risk prediction model for COAD patients and verified the role of POU4F1 in promoting cell proliferation, migration, and disulfidptosis.

Single cell RNA-sequencing in uveal melanoma: advances in heterogeneity, tumor microenvironment and immunotherapy.

Uveal melanoma (UM) is a highly aggressive and fatal tumor in the eye, and due the special biology of UM, immunotherapy showed little effect in UM patients. To improve the efficacy of immunotherapy for UM patients is of great clinical importance. Single-cell RNA sequencing(scRNA-seq) provides a critical perspective for deciphering the complexity of intratumor heterogeneity and tumor microenvironment(TME). Combing the bioinformatics analysis, scRNA-seq could help to find prognosis-related molecular indicators, develop new therapeutic targets especially for immunotherapy, and finally to guide the clinical treatment options.

A novel nomogram to predict glucocorticoid response in thyroid-associated ophthalmopathy: findings from a pilot study.

PURPOSE: Aimed to create a nomogram using clinical and eye-specific metrics to predict the efficacy of intravenous glucocorticoid (IVGC) therapy in patients with active and moderate-to-severe Thyroid-Associated Ophthalmopathy (TAO). METHODS: This study was conducted on 84 eyes from 42 moderate-to-severe TAO patients who received systemic IVGC therapy, and 42 eyes from 21 controls. Data were collected retrospectively from June 2020 to December 2021. The least absolute shrinkage and selection operator (LASSO) method was used to identify predictive factors for "unresponsiveness" to IVGC therapy. These factors were then analyzed using logistic regression to create a nomogram. The model's discriminative ability was robustly assessed using a Bootstrap resampling method with 1000 iterations for receiver operating characteristic (ROC) curve analysis. RESULTS: The LASSO analysis identified six factors with non-zero coefficients as significant, including Schirmer I test values, Meibomian gland (MG) diameter, MG length, disease duration, whole capillary vessel density (VD) in the radial peripapillary capillary (RPC), and whole macular VD for the superficial retinal capillary plexus (SRCP). The subsequent logistic regression model highlighted MG length, whole macular VD for SRCP, and disease duration as independent predictors of IVGC therapy response. The constructed nomogram demonstrated an area under the curve (AUC) of 0.82 (95% CI: 0.73-0.91), affirming the model's consistent and reliable ability to distinguish between responsive and non-responsive TAO patients. CONCLUSION: Our nomogram, combining MG length (<4.875 mm), SRCP VD (<50.25%), and disease duration (>5.5 months), reliably predicts lower IVGC therapy effectiveness in active, moderate-to-severe TAO patients.

Development and validation of a nomogram to predict overall survival in patients with glioma: a population-based study.

AIM: The objective is to investigate the prognostic factors associated with gliomas and to develop and assess a predictive nomogram model connected to survival that may serve as an additional resource for the clinical management of glioma patients. METHOD: From 2010 to 2015, participants included in the study were chosen from the Surveillance Epidemiology and End Results (SEER) database. Gliomas were definitively diagnosed in each of them. They were divided into the training group and the validation cohort at random (7/3 ratio) using a random number table. To identify the independent predictive markers for overall survival (OS), Cox regression analysis was utilized. Subsequently, the training cohort's survival-related nomogram predictive model for OS was created by incorporating the fundamental patient attributes. Following that, the training cohort's model underwent internal validation. The nomogram model's authenticity and reliability were assessed through the computation of receiver operating characteristic (ROC) curves and concordance index (C-index). To evaluate the degree of agreement between the observed and predicted values in the training and validation cohorts, calibration plots were created. RESULT: Age, primary site, histological type, surgery, chemotherapy, marital status, and grade were the independent predictive factors for OS in the training cohort, according to Cox regression analysis. Moreover, the nomogram model for predicting 1-year, 3-year, and 5-year OS was built using these variables. The C-indexes of OS for glioma patients in the training cohort and internal validation cohort were found to be 0.779 (95% CI=0.769-0.789) and 0.776 (95% CI=0.760-0.792), respectively, according to the results. The ROC curves also demonstrated good discrimination. Additionally, calibration plots demonstrated a fair amount of agreement. CONCLUSIONS: In summary, the nomogram prediction model of OS demonstrated a moderate level of reliability in its predictive performance, offering valuable reference data to enable doctors to quickly and easily determine the survival likelihood of patients with gliomas.

Identification and validation of a prognostic model based on four genes related to satellite nodules in hepatocellular carcinoma.

Satellite nodules is a key clinical characteristic which has prognostic value of hepatocellular carcinoma (HCC). Currently, there is no gene-level predictive model for Satellite nodules in liver cancer. For the 377 HCC cases collected from the dataset of Cancer Genome Atlas (TCGA), their original pathological data were analyzed to extract information regarding satellite nodules status as well as other relevant pathological data. Then, this study employed statistical modeling for prognostic model establishment in TCGA, and validation in International Cancer Genome Consortium (ICGC) cohorts and GSE76427. Through rigorous statistical analyses, 253 differential satellite nodules-related genes (SNRGs) were identified, and four key genes related to satellite nodules and prognosis were selected to construct a prognostic model. The high-risk group predicted by our model exhibited an unfavorable overall survival (OS) outlook and demonstrated an association with adverse worse clinical characteristics such as larger tumor size, higher alpha-fetoprotein, microvascular invasion and advanced stage. Moreover, the validation of the model's prognostic value in the ICGC and GSE76427 cohorts mirrored that of the TCGA cohort. Besides, the high-risk group also showed higher levels of resting Dendritic cells, M0 macrophages infiltration, alongside decreased levels of CD8+ T cells and γδT cells infiltration. The prognostic model based on SNRGs can reliability predict the OS of HCC and is likely to have predictive value of immunotherapy for HCC.

Development and validation of a nomogram to predict poor efficacy of imatinib in the treatment of newly diagnosed chronic phase chronic myeloid leukemia patients.

Background: Imatinib is the most widely used tyrosine kinase inhibitor (TKI) in patients with newly diagnosed chronic-phase chronic myeloid leukemia(CML-CP). However, failure to achieve optimal response after imatinib administration, and subsequent switch to second-generation TKI therapy results in poor efficacy and induces drug resistance. In the present study, we developed and validated a nomogram to predict the efficacy of imatinib in the treatment of patients newly diagnosed with CML-CP in order to help clinicians truly select patients who need 2nd generation TKI during initial therapy and to supplement the risk score system. Methods: We retrospectively analyzed 156 patients newly diagnosed with CML-CP who met the inclusion criteria and were treated with imatinib at the Second Affiliated Hospital of Xi'an Jiao Tong University from January 2012 to June 2022. The patients were divided into a poor-response cohort (N = 60)and an optimal-response cohort (N = 43) based on whether they achieved major molecular remission (MMR) after 12 months of imatinib treatment. Using univariate and multivariate logistic regression analyses, we developed a chronic myeloid leukemia imatinib-poor treatment (CML-IMP) prognostic model using a nomogram considering characteristics like age, sex, HBG, splenic size, and ALP. The CML-IMP model was internally validated and compared with Sokal, Euro, EUTOS, and ELTS scores. Results: The area under the curve of the receiver operator characteristic curve (AUC)of 0.851 (95% CI 0.778-0.925) indicated satisfactory discriminatory ability of the nomogram. The calibration plot shows good consistency between the predicted and actual observations. The net reclassification index (NRI), continuous NRI value, and the integrated discrimination improvement (IDI) showed that the nomogram exhibited superior predictive performance compared to the Sokal, EUTOS, Euro, and ELTS scores (P < 0.05). In addition, the clinical decision curve analysis (DCA) showed that the nomogram was useful for clinical decision-making. In predicting treatment response, only Sokal and CML-IMP risk stratification can effectively predict the cumulative acquisition rates of CCyR, MMR, and DMR (P<0.05). Conclusion: We constructed a nomogram that can be effectively used to predict the efficacy of imatinib in patients with newly diagnosed CML-CP based on a single center, 10-year retrospective cohort study.

Identification of a prognostic DNA repair gene signature in esophageal cancer.

Background: DNA repair plays a crucial role in the development and progression of different types of cancers. Nevertheless, little is known about the role of DNA repair-related genes (DRRGs) in esophageal cancer (EC). The present study aimed to identify a novel DRRGs prognostic signature in EC. Methods: Gene set enrichment analysis (GSEA) was performed to screen 150 genes related to DNA repair, which is the most important enrichment gene set in EC. Univariate and multivariate Cox regression analyses were used to screen DRRGs closely associated with prognosis. The difference in the expression of hub DRRGs between tumor and normal tissues was analyzed. Combined with clinical indicators (including age, gender, and tumor stage), we evaluated whether the 4-DRRGs signature was an independent prognostic factor. In addition, we evaluated the prediction accuracy using a receiver operating characteristic (ROC) curve and visualized the model's performance via a nomogram. Results: Four-DRRGs (NT5C3A, TAF9, BCAP31, and NUDT21) were selected by Cox regression analysis to establish a prognostic signature to effectively classify patients into high- and low-risk groups. The area under the curve (AUC) of the time-dependent ROC of the prognostic signature for 1- and 3-year was 0.769 and 0.720, respectively. Compared with other clinical characteristics, the risk score showed a robust ability to predict the prognosis in EC, especially in the early stage of EC. Furthermore, we constructed a nomogram to interpret the clinical application of the 4-DRRGs signature. Conclusions: In conclusion, we identified a prognostic signature based on the DRRGs for patients with EC, which can contribute independent value in identifying clinical outcomes that complement the TNM system in EC.

Integrative analyses of multi-omics data constructing tumor microenvironment and immune-related molecular prognosis model in human colorectal cancer.

The increasing prevalence and incidence of colorectal cancer (CRC), particularly in young adults, underscore the imperative to comprehend its fundamental mechanisms, discover novel diagnostic and prognostic markers, and enhance therapeutic strategies. Here, we integrated multi-omics data, including gene expression, somatic mutation data and DNA methylation data, to unravel the intricacies of tumor microenvironment (TME) in CRC and search for novel prognostic markers. By calculating the immune score for each patient from the expression profile, we delineated the differential immune cell fraction, constructed an immune-related multi-omics atlas, and identified molecular characteristics. The entire colorectal dataset (n = 343) was randomly divided into training (n = 249) and testing datasets (n = 94). We screened 144 immune-related genes, 6 mutant genes, and 38 methylation probes associated with overall survival (OS). These makers were then incorporated into a 10-gene prognostic model using Lasso and Cox regression in the training dataset, and the model's performance was evaluated in an independent validation dataset. The model exhibited satisfactory results (average concordance index [C-index] = 0.77), with the average 1-year, 3-year, and 5-year AUCs being 0.79, 0.76, and 0.76 in the training dataset and 0.74, 0.80, and 0.90 in the testing dataset. Furthermore, the prognostic model demonstrated applicability in guiding chemotherapy for CRC patients and exhibited a degree of pan-cancer utility in risk stratification. In conclusion, our integrated analysis of multi-omics data revealed immune-related genetic and epigenetic characteristics of the TME. We propose an integrative prognostic model that can stratify risk and guide chemotherapy for CRC patients. The generalizability of the model in risk stratification across different cancer types was validated in Pan-Cancer cohort.

Identification and Validation of a Novel Tertiary Lymphoid Structures-Related Prognostic Gene Signature in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors originating from the digestive system. Tertiary lymphoid structures (TLS), non-lymphoid tissues outside of the lymphoid organs, are closely connected to chronic inflammation and tumorigenesis. However, the detailed relationship between TLS and HCC prognosis remained unclear. In this study, we aimed to construct a TLS-related gene signature for predicting the prognosis of HCC patients. Methods: The Cancer Genome Atlas (TCGA) clinical data from 369 HCC tissues and 50 normal liver tissues were utilized to examine the differential expression of TLS-related genes. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, the prognostic model was constructed using the TCGA cohort and validated in the GSE14520 cohort and International Cancer Genome Consortium (ICGC) cohort. The Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were employed to validate the predictive ability of the prognostic model. Furthermore, Cox regression analysis was applied to identify whether the TLS score could be employed as an independent prognosis factor. A nomogram was developed to predict the survival probability of HCC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for TLS-related genes. Genetic mutation analysis, the CIBERSORT algorithm, and single-sample gene set enrichment analysis (ssGSEA) were used to assess the tumor mutation landscape and immune infiltration. Finally, the role of the TLS score in HCC therapy was investigated. Results: Six genes were included in the construction of our prognostic model (CETP, DNASE1L3, PLAC8, SKAP1, C7, and VNN2), and we validated its accuracy. Survival analysis showed that patients in the high-TLS score group had a significantly better overall survival than those in the low-TLS score group. Univariate, multivariate Cox regression analysis and the establishment of a nomogram indicated that the TLS score could independently function as a potential prognostic marker. A significant association between TLS score and immunity was revealed by an analysis of gene alterations and immune cell infiltration. In addition, two subtypes of the TLS score could accurately predict the effectiveness of sorafenib, transcatheter arterial chemoembolization (TACE), and immunotherapy in HCC patients. Conclusion: In this research, we conducted and validated a prognostic model associated with TLS that may be helpful for predicting clinical outcomes and treatment responsiveness for HCC patients.

A New Ferroptosis-Related Long Non-Coding RNA Risk Model Predicts the Prognosis of Patients With Papillary Thyroid Cancer.

Background: Ferroptosis is a novel form of regulated cell death that involves in cancer progression. However, the role of ferroptosis-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC) remains to be elucidated. The purpose of this paper was to clarify the prognostic value of ferroptosis-related lncRNAs in PTC. Methods: The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between ferroptosis-related genes (FRGs) and lncRNA was determined using Pearson correlation analysis. Multivariate Cox regression model (P < 0.01) was performed to establish a ferroptosis-related lncRNAs risk model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, risk curve and nomograms were then performed to assess the accuracy and clinical applicability of prognostic models. The correlations between the prognosis model and clinicopathological variables, immune and m6A were analyzed. Finally, in vitro assays were performed to verify the role of LINC00900, LINC01614 and PARAL1 on the proliferation, migration and invasion in TPC-1 and BCPAP cells, as well as the relationship between three lncRNAs and ferroptosis. Results: A five-ferroptosis-related lncRNAs (PARAL1, LINC00900, DPH6-DT, LINC01614, LPP-AS2) risk model was constructed. Based on the risk score, samples were divided into the high- and low-risk groups. Patients in the low-risk group had better prognosis than those in high-risk group. Compared to traditional clinicopathological features, risk score was more accurate in predicting prognosis in patients with PTC. Additionally, the difference of immune cell, function and checkpoints was observed between two groups. Moreover, experiments showed that LINC00900 promoted the proliferation, migration and invasion in TPC-1 and BCPAP cells, while LINC01614 and PARAL1 revealed opposite effects, all of which were related to ferroptosis. Conclusions: In summary, we identified a five-ferroptosis-related lncRNAs risk model to predict the prognosis of PTC. Furthermore, our study also revealed that LINC00900 functioned as a tumor suppressor lncRNA, LINC01614 and PARAL1 as an oncogenic lncRNA in PTC.

Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database.

BACKGROUND: Studies have found that first primary cancer (FPC) survivors are at high risk of developing second primary breast cancer (SPBC). However, there is a lack of prognostic studies specifically focusing on patients with SPBC. METHODS: This retrospective study used data from Surveillance, Epidemiology and End Results Program. We selected female FPC survivors diagnosed with SPBC from 12 registries (from January 1998 to December 2018) to construct prognostic models. Meanwhile, SPBC patients selected from another five registries (from January 2010 to December 2018) were used as the validation set to test the model's generalization ability. Four machine learning models and a Cox proportional hazards regression (CoxPH) were constructed to predict the overall survival of SPBC patients. Univariate and multivariate Cox regression analyses were used for feature selection. Model performance was assessed using time-dependent area under the ROC curve (t-AUC) and integrated Brier score (iBrier). RESULTS: A total of 10,321 female FPC survivors with SPBC (mean age [SD]: 66.03 [11.17]) were included for model construction. These patients were randomly split into a training set (mean age [SD]: 65.98 [11.15]) and a test set (mean age [SD]: 66.15 [11.23]) with a ratio of 7:3. In validation set, a total of 3,638 SPBC patients (mean age [SD]: 66.28 [10.68]) were finally enrolled. Sixteen features were selected for model construction through univariate and multivariable Cox regression analyses. Among five models, random survival forest model showed excellent performance with a t-AUC of 0.805 (95 %CI: 0.803 - 0.807) and an iBrier of 0.123 (95 %CI: 0.122 - 0.124) on testing set, as well as a t-AUC of 0.803 (95 %CI: 0.801 - 0.807) and an iBrier of 0.098 (95 %CI: 0.096 - 0.103) on validation set. Through feature importance ranking, the top one and other top five key predictive features of the random survival forest model were identified, namely age, stage, regional nodes positive, latency, radiotherapy, and surgery. CONCLUSIONS: The random survival forest model outperformed CoxPH and other machine learning models in predicting the overall survival of patients with SPBC, which was helpful for the monitoring of high-risk populations.

MICAL1 promotes the proliferation in acute myeloid leukemia and is associated with clinical prognosis and immune infiltration.

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has a poor prognosis and a high rate of relapse. Dysregulated metabolism plays an important role in AML progression. This study aimed to conduct a comprehensive analysis of MRGs using TCGA and GEO datasets and further explore the potential function of critical MRGs in AML progression. In this study, we identified 17 survival-related differentially expressed MRGs in AML using TCGA and GEO datasets. The 150 AML samples were divided into three molecular subtypes using 17 MRGs, and we found that three molecular subtypes exhibited a different association with ferroptosis, cuproptosis and m6A related genes. Moreover, a prognostic signature that comprised nine MRGs and had good predictive capacity was established by LASSO-Cox stepwise regression analysis. Among the 17 MRGs, our attention focused on MICAL1 which was highly expressed in many types of tumors, including AML and its overexpression was also confirmed in several AML cell lines. We also found that the expression of MICAL1 was associated with several immune cells. Moreover, functional experiments revealed that knockdown of MICAL1 distinctly suppressed the proliferation of AML cells. Overall, this study not only contributes to a deeper understanding of the molecular mechanisms underlying AML but also provides potential targets and prognostic markers for AML treatment. These findings offer robust support for further research into therapeutic strategies and mechanisms related to AML, with the potential to improve the prognosis and quality of life for AML patients. Nevertheless, further research is needed to validate these findings and explore more in-depth molecular mechanisms.

Lysine lactylation-based insight to understanding the characterization of cervical cancer.

Lysine lactylation (Kla), a recently discovered post-translational modification (PTM), is not only present in histone proteins but also widely distributed among non-histone proteins in tumor cells and immunocytes. However, the precise characterization and functional implications of these non-histone Kla proteins remain to be explored. Herein, a comprehensive proteomic analysis of Kla was conducted in HeLa cells. As a result, a total of 3633 Kla sites on 1637 proteins were identified. Subsequently, the stable Kla substrates were obtained and sorted to investigate the characterization and function of Kla proteins. Moreover, we characterized the Kla-related features of cervical cancers through integrative analyses of multiple datasets with proteomes, transcriptomes and single-cell transcriptome profiling. Kla-related genes (KRGs) were used to stratify cervical cancers into two clusters (C1 and C2). C2 cluster display inhibition in glycosylation and increased oxidative phosphorylation activity with high survival rate. In addition, we constructed a prognostic model based on two lactate signature genes, namely ISY1 and PPP1R14B. Interestingly, our findings revealed a negative correlation between PPP1R14B expression and the infiltration of CD8+ T cells, as well as a lower survival rate. This observation was further validated at the single-cell resolution. Simultaneously, we found that K140R mutant of PPP1R14B resulted in the decrease of Kla level and enhanced the proliferation and migration capabilities of cervical cancer cell lines, suggesting PPP1R14B-K140la has an effect on tumor behaviors. Collectively, we provides a Kla-based insight to understanding the characterization of cervical cancer, offering a potential avenue for therapeutic approaches.

Neutrophil extracellular traps-related lncRNAs prognostic signature for gastric cancer and immune infiltration: potential biomarkers for predicting overall survival and clinical therapy.

Gastric cancer (GC) is one of the most common digestive tract malignant tumors in the world. At the time of initial diagnosis, it frequently presents with local or distant metastasis, contributing to poor prognosis in patients. Neutrophil extracellular traps (NETs) constitute a mechanism employed by neutrophils that is intricately associated with tumor progression, prognosis, and response to immunotherapy and chemotherapy. Despite this, the specific involvement of NETs-related long non-coding RNAs (lncRNAs) in gastric cancer remains unclear. A prognostic model for NETs-related lncRNAs was constructed through correlation analysis, COX regression analysis, and least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the model was assessed using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, facilitating the exploration of the relationship between disease onset and prognosis in gastric cancer. Additionally, differences in the tumor microenvironment and response to immunotherapy among gastric cancer patients across high- and low-risk groups were analyzed. Furthermore, a prognostic nomogram integrating the risk score with relevant clinicopathological parameters was developed. The prognostic prediction model for gastric cancer, derived from NETs-related lncRNAs in this study, demonstrates robust prognostic capabilities, serving as a valuable adjunct to traditional tumor staging. This model holds promise in offering novel guidelines for the precise treatment of gastric cancer, thereby potentially improving patient outcomes.

Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records.

INTRODUCTION AND OBJECTIVES: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model. PATIENTS AND METHODS: n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n = 528 MASLD patients. RESULTS: In-sample model performance achieved AUROC curve 0.74-0.90 (95 % CI: 0.72-0.94), sensitivity 64 %-82 %, specificity 75 %-92 % and Positive Predictive Value (PPV) 94 %-98 %. Out-of-sample model validation had AUROC 0.70-0.86 (95 % CI: 0.67-0.90), sensitivity 69 %-70 %, specificity 96 %-97 % and PPV 75 %-77 %. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay >14 days. CONCLUSIONS: A TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions.

Consensus clustering and novel risk score model construction based on m6A methylation regulators to evaluate the prognosis and tumor immune microenvironment of early-stage lung adenocarcinoma.

BACKGROUND: The aim of this study was to investigate the correlation between m6A methylation regulators and cell infiltration characteristics in tumor immune microenvironment (TIME), so as to help understand the immune mechanism of early-stage lung adenocarcinoma (LUAD). METHODS: The expression and consensus cluster analyses of m6A methylation regulators in early-stage LUAD were performed. The clinicopathological features, immune cell infiltration, survival and functional enrichment in different subtypes were analyzed. We also constructed a prognostic model. Clinical tissue samples were used to validate the expression of model genes through real-time polymerase chain reaction (RT-PCR). In addition, cell scratch assay and Transwell assay were also performed. RESULTS: Expression of m6A methylation regulators was abnormal in early-stage LUAD. According to the consensus clustering of m6A methylation regulators, patients with early-stage LUAD were divided into two subtypes. Two subtypes showed different infiltration levels of immune cell and survival time. A prognostic model consisting of HNRNPC, IGF2BP1 and IGF2BP3 could be used to predict the survival of early-stage LUAD. RT-PCR results showed that HNRNPC, IGF2BP1 and IGF2BP3 were significantly up-regulated in early-stage LUAD tissues. The results of cell scratch assay and Transwell assay showed that overexpression of HNRNPC promotes the migration and invasion of NCI-H1299 cells, while knockdown HNRNPC inhibits the migration and invasion of NCI-H1299 cells. CONCLUSIONS: This work reveals that m6A methylation regulators may be potential biomarkers for prognosis in patients with early-stage LUAD. Our prognostic model may be of great value in predicting the prognosis of early-stage LUAD.