A prognostic model built on amino acid metabolism patterns in HPV-associated head and neck squamous cell carcinoma

OBJECTIVES

To compare amino acid metabolism patterns between HPV-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC) patients and identify key genes for a prognostic model.

DESIGN

Utilizing the Cancer Genome Atlas dataset, we analyzed amino acid metabolism genes, differentiated genes between HPV statuses, and selected key genes via LASSO regression for the prognostic model. The model's gene expression was verified through immunohistochemistry in clinical samples. Functional enrichment and CIBERSORTx analyses explored biological functions, molecular mechanisms, and immune cell correlations. The model's prognostic capability was assessed using nomograms, calibration, and decision curve analysis.

RESULTS

We identified 1157 key genes associated with amino acid metabolism in HNSCC and HPV status. The prognostic model, featuring genes like IQCN, SLC22A1, SYT12, and TLX3, highlighted functions in development, metabolism, and pathways related to receptors and enzymes. It significantly correlated with immune cell infiltration and outperformed traditional staging in prognosis prediction, despite immunohistochemistry results showing limited clinical identification of HPV-related HNSCC.

CONCLUSIONS

Distinct amino acid metabolism patterns differentiate HPV-positive from negative HNSCC patients, underscoring the prognostic model's utility in predicting outcomes and guiding therapeutic strategies.

Systematic analysis of PANoptosis-related genes identifies XIAP as a functional oncogene in breast cancer

BACKGROUND

Breast cancer (BC) is the most prevalent malignant disease affecting women globally. PANoptosis, a novel form of cell death combining features of pyroptosis, apoptosis, and necroptosis, has recently gained attention. However, its precise function in BC and the predictive values of PANoptosis-related genes remain unclear.

METHODS

We used the expression data and clinical information of BC tissues or normal breast tissues from public databases, and then successfully developed and verified a BC PANoptosis-related risk model through a combination of univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and Kaplan-Meier (KM) analysis. A nomogram was constructed to estimate survival probability, and its accuracy was assessed using calibration curves.

RESULTS

Among 37 PANoptosis-related genes, we identified 4 differentially expressed genes related to overall survival (OS). Next, a risk model incorporating these four PANoptosis-related genes was established. Patients were stratified into low/high-risk groups based on the median risk score, with the low-risk group showing better prognoses and higher levels of immune infiltration. Utilizing the risk score and clinical features, we developed a nomogram to predict 1-, 3- and 5-year survival probability. X-linked inhibitor of apoptosis protein (XIAP) emerged as a potentially risky factor with the highest hazard ratio. In vitro experiments demonstrated that XIAP inhibition enhances the antitumor effect of doxorubicin through the PANoptosis pathway.

CONCLUSION

PANoptosis holds an important role in BC prognosis and treatment.

Exploring the role of disulfidptosis-related signatures in immune microenvironment, prognosis and therapeutic strategies of cervical cancer

BACKGROUND

Cervical cancer is characterized by a complex immunosuppressive tumor microenvironment (TME). Disulfidptosis is a recently identified form of programmed cell death that has emerged as a crucial factor in tumorigenesis. However, the research on the specific involvement of disulfidptosis within the TME is still in its early stages.

METHODS

Under glucose starvation, SiHa and HeLa cells underwent experiments employing diverse cell death inhibitors and SLC7A11 knockdown to observe their impact on cell survival. TCGA-CESC cohort was subjected to consensus clustering for disulfidptosis-related clusters. Prognosis, function, immune infiltration, and differentially expressed genes (DEGs) evaluations among clusters were compared. A prognostic model based on DEGs and disulfidptosis regulator genes (DRGs) was constructed and internally and externally validated. The correlation between YWHAG and clinicopathological characteristics in cervical cancer patients was investigated at both the mRNA and protein levels. Proliferation and migration assays were performed to uncover the roles of YWHAG in cervical cancer.

RESULTS

Experimental validation confirmed disulfidptosis in cervical cancer cell lines. Cervical cancer patients were classified into three clusters based on DRGs, showing notably improved prognosis and increased immune infiltration in cluster B. The developed disulfidptosis-related prognostic model effectively stratified patients into high- and low-risk groups. Low-risk patients exhibited more favorable responses to immunotherapy and improved overall prognosis. Additionally, YWHAG, recognized as a tumor-promoting gene, demonstrated active roles in enhancing the growth, migration, and invasion of cervical cancer cells.

CONCLUSION

Our research proposed a prognostic model for cervical cancer, probably contributing to tumor microenvironment traits and more potent immunotherapy strategy exploration.

Utilizing a novel model of PANoptosis-related genes for enhanced prognosis and immune status prediction in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is the most common histopathologic type of renal cell carcinoma. PANoptosis, a cell death pathway that involves an interplay between pyroptosis, apoptosis and necroptosis, is associated with cancer immunity and development. However, the prognostic significance of PANoptosis in KIRC remains unclear. RNA-sequencing expression and mutational profiles from 532 KIRC samples and 72 normal samples with sufficient clinical data were retrieved from the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using differentially expressed genes (DEGs) related to PANoptosis in the TCGA cohort and was validated in a Gene Expression Omnibus (GEO) cohorts. Incorporating various clinical features, the risk model remained an independent prognostic factor in multivariate analysis, and it demonstrated superior performance compared to unsupervised clustering of the 21 PANoptosis-related genes alone. Further mutational analysis showed fewer VHL and more BAP1 alterations in the high-risk group, with alterations in both genes also associated with patient prognosis. The high-risk group was characterized by an unfavorable immune microenvironment, marked by reduced levels of CD4 + T cells and natural killer cells, but increased M2 macrophages and regulatory T cells. Finally, the risk model was predictive of response to immune checkpoint blockade, as well as sensitivity to sunitinib and paclitaxel. The PANoptosis-related risk model developed in this study enables accurate prognostic prediction in KIRC patients. Its associations with the tumor immune microenvironment and drug efficacy may offer potential therapeutic targets and inform clinical decisions.

Artificial intelligence-based prognostic model accurately predicts the survival of patients with diffuse large B-cell lymphomas: analysis of a large cohort in China

BACKGROUND

Diffuse large B-cell lymphomas (DLBCLs) display high molecular heterogeneity, but the International Prognostic Index (IPI) considers only clinical indicators and has not been updated to include molecular data. Therefore, we developed a widely applicable novel scoring system with molecular indicators screened by artificial intelligence (AI) that achieves accurate prognostic stratification and promotes individualized treatments.

METHODS

We retrospectively enrolled a cohort of 401 patients with DLBCL from our hospital, covering the period from January 2015 to January 2019. We included 22 variables in our analysis and assigned them weights using the random survival forest method to establish a new predictive model combining bidirectional long-short term memory (Bi-LSTM) and logistic hazard techniques. We compared the predictive performance of our "molecular-contained prognostic model" (McPM) and the IPI. In addition, we developed a simplified version of the McPM (sMcPM) to enhance its practical applicability in clinical settings. We also demonstrated the improved risk stratification capabilities of the sMcPM.

RESULTS

Our McPM showed superior predictive accuracy, as indicated by its high C-index and low integrated Brier score (IBS), for both overall survival (OS) and progression-free survival (PFS). The overall performance of the McPM was also better than that of the IPI based on receiver operating characteristic (ROC) curve fitting. We selected five key indicators, including extranodal involvement sites, lactate dehydrogenase (LDH), MYC gene status, absolute monocyte count (AMC), and platelet count (PLT) to establish the sMcPM, which is more suitable for clinical applications. The sMcPM showed similar OS results (P < 0.0001 for both) to the IPI and significantly better PFS stratification results (P < 0.0001 for sMcPM vs. P = 0.44 for IPI).

CONCLUSIONS

Our new McPM, including both clinical and molecular variables, showed superior overall stratification performance to the IPI, rendering it more suitable for the molecular era. Moreover, our sMcPM may become a widely used and effective stratification tool to guide individual precision treatments and drive new drug development.

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

Construction and significance of a breast cancer prognostic model based on cuproptosis-related genotyping and lncRNAs

BACKGROUND

/Purpose: Cuproptosis may play a significant role in breast cancer (BC). We aimed to investigate the prognostic impact of cuproptosis-related lncRNAs in BC.

METHODS

Consensus clustering analysis categorized TCGA-BRCA samples into 3 clusters, followed by survival and immune analyses of the 3 clusters. LASSO-COX analysis was performed on cuproptosis-related lncRNAs differentially expressed in BC to construct a BC prognostic model. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment, immune, and drug prediction analyses were performed on the high-risk and low-risk groups. Cell experiments were conducted to analyze the results of drug prediction and two cuproptosis-related lncRNAs (AC104211.1 and LINC01863).

RESULTS

Significant differences were observed in survival outcomes and immune infiltration levels among the three clusters (p < 0.05). The validation of the model showed significant differences in survival outcomes between the high-risk and low-risk groups in both the training and validation sets (p < 0.05). Differential mRNAs between the two groups were significantly enriched in the Neuroactive ligand-receptor interaction and cAMP signaling pathway. Additionally, significant differences were found in immune infiltration levels, human leukocyte antigen (HLA) expression, Immunophenoscore (IPS) scores, and Tumor Immune Dysfunction and Exclusion (TIDE) scores between the two groups (p < 0.05). Drug prediction and corresponding cell experimental results showed that Trametinib, 5-fluorouracil, and AICAR significantly inhibited the viability of MCF-7 cells (p < 0.05). AC104211.1 and LINC01863 were found to impact the proliferation of BC cells.

CONCLUSION

The risk-scoring model obtained in this study may serve as a robust prognostic biomarker, potentially aiding in clinical decision-making for BC patients.

An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma

Recent research highlights the significance of exosomes and long noncoding RNAs (lncRNAs) in cancer progression and drug resistance, but their role in lung adenocarcinoma (LUAD) is not fully understood. We analyzed 121 exosome-related (ER) mRNAs from the ExoBCD database, along with mRNA and lncRNA expression profiles of TCGA-LUAD using "DESeq2", "survival," "ConsensusClusterPlus," "GSVA," "estimate," "glmnet," "clusterProfiler," "rms," and "pRRophetic" R packages. This comprehensive approach included univariate cox regression, unsupervised consensus clustering, GSEA, functional enrichment analysis, and prognostic model construction. Our study identified 134 differentially expressed ER-lncRNAs, with 19 linked to LUAD prognosis. These ER-lncRNAs delineated two patient subtypes, one with poorer outcomes. Additionally, 286 differentially expressed genes were related to these ER-lncRNAs, 261 of which also correlated with LUAD prognosis. We constructed an ER-lncRNA-related prognostic model and calculated an ER-lncRNA-related risk score (ERS), revealing that a higher ERS correlates with poor overall survival in both the Meta cohort and two validation cohorts. The ERS potentially serves as an independent prognostic factor, and the prognostic model demonstrates superior predictive power. Notably, significant differences in the immune landscape were observed between the high- and low-ERS groups. Drug sensitivity analysis indicated varying responses to common chemotherapy drugs based on ERS stratification, with the high-ERS group showing greater sensitivity, except to rapamycin and erlotinib. Experimental validation confirmed that thymidine kinase 1 enhances lung cancer invasion, metastasis, and cell cycle progression. Our study pioneers an ER-lncRNA-related prognostic model for LUAD, proposing that ERS-based risk stratification could inform personalized treatment strategies to improve patient outcomes.

A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients

BACKGROUND

Vessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC.

METHODS

A total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell's concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis.

RESULTS

A prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time.

CONCLUSIONS

The VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.

Construction and validation of a novel immunological model to predict prognosis in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with limited treatment options. In this study, we investigated the role of immune cell infiltration in PDAC progression and constructed an immune-related predictive model for patients with PDAC based on the International Cancer Genome Consortium (ICGC) cohort. Related algorithms have been used to assess the immune microenvironment. Least Absolute Shrinkage and Selection Operator (LASSO) Cox analysis was used to construct the model, and receiver operating characteristic and decision curve analysis analyses were conducted to evaluate its diagnostic and prognostic efficacy. The results demonstrated a correlation between high immune infiltration and better prognosis in PDAC. The immune-related prognostic model (IPM) identified four genes through LASSO Cox analysis, with the high IPM group being associated with a worse prognosis. Cox regression analysis confirmed that IPM is an independent risk factor for PDAC. Validation through analysis of The Cancer Genome Atlas cohort and our own individual tumor samples revealed a similar trend to that observed in the ICGC cohort. Finally, a nomogram incorporating age and IPM demonstrated efficacy in the prognostic evaluation of patients with PDAC. In conclusion, we developed a novel immune-related prognosis prediction model for PDAC that offers new possibilities for the measurement of immunotherapy and prognostic assessment of patients.

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND

Gastric cancer (GC) is a common malignancy of the digestive system. According to global 2018 cancer data, GC has the fifth-highest incidence and the third-highest fatality rate among malignant tumors. More than 60% of GC are linked to infection with Helicobacter pylori (H. pylori), a gram-negative, active, microaerophilic, and helical bacterium. This parasite induces GC by producing toxic factors, such as cytotoxin-related gene A, vacuolar cytotoxin A, and outer membrane proteins. Ferroptosis, or iron-dependent programmed cell death, has been linked to GC, although there has been little research on the link between H. pylori infection-related GC and ferroptosis.

AIM

To identify coregulated differentially expressed genes among ferroptosis-related genes (FRGs) in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.

METHODS

Gene expression profiles of GC patients and those with H. pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. The FRGs were acquired from the FerrDb database. A ferroptosis-related gene prognostic index (FRGPI) was created using least absolute shrinkage and selection operator-Cox regression. The predictive ability of the FRGPI was validated in the GEO cohort. Finally, we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.

RESULTS

Four hub genes were identified (NOX4, MTCH1, GABARAPL2, and SLC2A3) and shown to accurately predict GC and H. pylori-associated GC. The FRGPI based on the hub genes could independently predict GC patient survival; GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group. The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression. Moreover, the gene expression levels of common immune checkpoint proteins dramatically increased in the high-risk subgroup of the FRGPI cohort. The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane. The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.

CONCLUSION

In this study, we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Construction of a prognostic model for extensive-stage small cell lung cancer patients undergoing immune therapy in northernmost China and prediction of treatment efficacy based on response status at different time points

BACKGROUND AND PURPOSE

Recently, the emergence of immune checkpoint inhibitors has significantly improved the survival of patients with extensive-stage small cell lung cancer. However, not all patients can benefit from immunotherapy; therefore, there is an urgent need for precise predictive markers to screen the population for the benefit of immunotherapy. However, single markers have limited predictive accuracy, so a comprehensive predictive model is needed to better enable precision immunotherapy. The aim of this study was to establish a prognostic model for immunotherapy in ES-SCLC patients using basic clinical characteristics and peripheral hematological indices of the patients, which would provide a strategy for the clinical realization of precision immunotherapy and improve the prognosis of small cell lung cancer patients.

METHODS

This research retrospectively collected data from ES-SCLC patients treated with PD-1/PD-L1 inhibitors between March 1, 2019, and October 31, 2022, at Harbin Medical University Cancer Hospital. The study data was randomly split into training and validation sets in a 7:3 ratio. Variables associated with patients' overall survival were screened and modeled by univariate and multivariate Cox regression analyses. Models were presented visually via Nomogram plots. Model discrimination was evaluated by Harrell's C index, tROC, and tAUC. The calibration of the model was assessed by calibration curves. In addition, the clinical utility of the model was assessed using a DCA curve. After calculating the total risk score of patients in the training set, patients were stratified by risk using percentile partitioning. The Kaplan-Meier method was used to plot OS and PFS survival curves for different risk groups and response statuses at different milestone time points. Differences in survival time groups were compared using the chi-square test. Statistical analysis software included R 4.1.2 and SPSS 26.

RESULTS

This study included a total of 113 ES-SCLC patients who received immunotherapy, including 79 in the training set and 34 in the validation set. Six variables associated with poorer OS in patients were screened by Cox regression analysis: liver metastasis (P = 0.001), bone metastasis (P = 0.013), NLR < 2.14 (P = 0.005), LIPI assessed as poor (P < 0.001), PNI < 51.03 (P = 0.002), and LDH ≥ 146.5 (P = 0.037). A prognostic model for immunotherapy in ES-SCLC patients was constructed based on the above variables. The Harrell's C-index in the training and validation sets of the model was 0.85 (95% CI 0.76-0.93) and 0.88 (95% CI 0.76-0.99), respectively; the AUC values corresponding to 12, 18, and 24 months in the tROC curves of the training set were 0.745, 0.848, and 0.819 in the training set and 0.858, 0.904 and 0.828 in the validation set; the tAUC curves show that the overall tAUC is > 0.7 and does not fluctuate much over time in both the training and validation sets. The calibration plot demonstrated the good calibration of the model, and the DCA curve indicated that the model had practical clinical applications. Patients in the training set were categorized into low, intermediate, and high risk groups based on their predicted risk scores in the Nomogram graphs. In the training set, 52 patients (66%) died with a median OS of 15.0 months and a median PFS of 7.8 months. Compared with the high-risk group (median OS: 12.3 months), the median OS was significantly longer in the intermediate-risk group (median OS: 24.5 months, HR = 0.47, P = 0.038) and the low-risk group (median OS not reached, HR = 0.14, P = 0.007). And, the median PFS was also significantly prolonged in the intermediate-risk group (median PFS: 12.7 months, HR = 0.45, P = 0.026) and low-risk group (median PFS not reached, HR = 0.12, P = 0.004) compared with the high-risk group (median PFS: 6.2 months). Similar results were obtained in the validation set. In addition, we observed that in real-world ES-SCLC patients, at 6 weeks after immunotherapy, the median OS was significantly longer in responders than in non-responders (median OS: 19.5 months vs. 11.9 months, P = 0.033). Similar results were obtained at 12 weeks (median OS: 20.7 months vs 11.9 months, P = 0.044) and 20 weeks (median OS: 20.7 months vs 11.7 months, P = 0.015). Finally, we found that in the real world, ES-SCLC patients without liver metastasis (P = 0.002), bone metastasis (P = 0.001) and a total number of metastatic organs < 2 (P = 0.002) are more likely to become long-term survivors after receiving immunotherapy.

CONCLUSION

This study constructed a new prognostic model based on basic patient clinical characteristics and peripheral blood indices, which can be a good predictor of the prognosis of immunotherapy in ES-SCLC patients; in the real world, the response status at milestone time points (6, 12, and 20 weeks) can be a good indicator of long-term survival in ES-SCLC patients receiving immunotherapy.

Elucidating the impact of parthanatos-related microRNAs on the tumoral immune microenvironment and clinical outcome in low-grade gliomas

Parthanatos, a cell death mechanism triggered by PARP-1 activation, is implicated in oncogenic processes, yet their role in low-grade gliomas (LGG) remains poorly understood. This research investigates Parthanatos-related miRNAs' prognostic and immunomodulatory potential, alongside their influence on therapeutic outcomes in LGGs. Comprehensive miRNA and mRNA profiles of LGG patients were extracted from TCGA and CGGA databases, integrating clinical parameters to identify Parthanatos-associated miRNAs. IHC data validated the expression levels of Parthanatos-related genes in glioma versus normal brain tissues. Protein-protein interaction networks and Spearman correlation analysis facilitated the identification of key miRNAs. Parthanatos-related miRNA indices (PMI) were screened using Lasso and assessed for their accuracy in predicting prognosis, comparing their associated potential molecular functions and heterogeneity of the immune microenvironment. Drug sensitivity was assessed between different groups and optimal therapeutic agents were predicted. Validate the expression levels of key miRNAs by qPCR. Ninety-one miRNAs significantly associated with Parthanatos were screened, through which a PMI prognosis model of nine miRNAs was constructed. The PMI score was able to independently predict the prognosis of patients with LGG, and the nomogram constructed based on the PMI provided a practical tool for clinical prediction of patient prognosis. The proportion of immune response was lower in patients in the high-risk group, and there were significant differences in drug sensitivity between different risk classes, while drugs such as Fasudil were identified as the most promising therapeutic agents for patients in the high-risk group. Our findings highlight the critical role of Parthanatos-associated miRNAs in the progression and treatment of LGG, offering novel insights into their prognostic value and therapeutic potential.

Identification of TAP1 as a T-cell related therapeutic target in gastric cancer by mediating oxalipliatin-related synergistic enhancement of immunotherapy

BACKGROUND

Given the intricate molecular complexities and heterogeneity inherent in T-cell immunotherapy of gastric cancer (GC), elucidative T-cell-related biomarkers were imperative needed for facilitating the prediction of GC patient prognosis and identify potential synergistic therapeutic targets.

METHODS

We conducted COX regression analysis in TISIDB, TCGA-STAD, and GEO databases to identify 19 GC T-cell-mediated sensitivity tumor killing (TTK) genes (key GCTTKs). Based on key GCTTKs, we constructed two TTK patterns and analyzed their metabolic pathways, mutation features, clinical data distribution, immune cell infiltration, and prognosis. LASSO regression was performed to develop a T-cell-mediated GC Prognosis (TGCP) model. We validated the TGCP model in GC patients. TAP1 was further selected for investigation of its biological functions and molecular mechanisms. We assessed the potential of TAP1 as a promising therapeutic target for GC using Patient-derived organoids (PDOs)-derived xenografts (PDOXs) models of GC.

RESULTS

The TTK patterns display notable disparities. The TGCP model showcases its proficiency in predicting immune response efficacy, effectively distinguishes immunotherapy difference GC patients. Our findings find further confirmation in PDOX models, affirming TAP1 can enhance immunotherapy facilitated by PDL1 inhibitors. Furthermore, Oxaliplatin, by promoting TAP1 expression, augments PDL1 expression, thereby enhancing the efficacy of immunotherapy.

CONCLUSIONS

We constructed a TGCP model, which demonstrates satisfactory predictive accuracy. Out of 9 prognostic genes, TAP1 was validated as a synergistic target for Oxaliplatin and PDL1 inhibitors, offering a genetic-level explanation for the synergy observed in GC treatment involving Oxaliplatin in combination with PDL1 inhibitors.

Identification of immunogenic cell death-related damage-related molecular patterns (DAMPs) to predict outcomes in patients with head and neck squamous cell carcinoma

PURPOSE

Head and neck cancer is the sixth most common type of cancer worldwide, wherein the immune responses are closely associated with disease occurrence, development, and prognosis. Investigation of the role of immunogenic cell death-related genes (ICDGs) in adaptive immune response activation may provide cues into the mechanism underlying the outcome of HNSCC immunotherapy.

METHODS

ICDGs expression patterns in HNSCC were analyzed, after which consensus clustering in HNSCC cohort conducted. A 4-gene prognostic model was constructed through LASSO and Cox regression analyses to analyze the prognostic index using the TCGA dataset, followed by validation with two GEO datasets. The distribution of immune cells and the response to immunotherapy were compared between different risk subtypes through multiple algorithms. Moreover, immunohistochemical (IHC) analyses were conducted to validate the prognostic value of HSP90AA1 as a predictor of HNSCC patient prognosis. In vitro assays were performed to further detect the effect of HSP90AA1 in the development of HNSCC.

RESULTS

A novel prognostic index based on four ICDGs was constructed and proved to be useful as an independent factor of HNSCC prognosis. The risk score derived from this model grouped patients into high- and low-risk subtypes, wherein the high-risk subtype had worse survival outcomes and poorer immunotherapy response. IHC analysis validated the applicability of HSP90AA1 as a predictor of prognosis of HNSCC patients. HSP90AA1 expression in tumor cells promotes the progression of HNSCC.

CONCLUSIONS

Together, these results highlight a novel four-gene prognostic signature as a valuable tool to assess survival status and prognosis of HNSCC patients.

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases

BACKGROUND

Liver metastases (LM) is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer (GC). The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis, thereby enhancing the ability to evaluate patient outcomes.

AIM

To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis, thereby enhancing patient outcome assessment.

METHODS

Retrospective analysis was conducted on clinical data pertaining to GCLM (type III), admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018. The dataset was divided into a development cohort and validation cohort in a ratio of 2:1. In the development cohort, we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients. Subsequently, we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis, calibration curves, and clinical decision curves. A nomogram was created to visually represent the prediction model, which was then externally validated using the validation cohort.

RESULTS

A total of 372 patients were included in this study, comprising 248 individuals in the development cohort and 124 individuals in the validation cohort. Based on Cox analysis results, our final prediction model incorporated five independent risk factors including albumin levels, primary tumor size, presence of extrahepatic metastases, surgical treatment status, and chemotherapy administration. The 1-, 3-, and 5-years Area Under the Curve values in the development cohort are 0.753, 0.859, and 0.909, respectively; whereas in the validation cohort, they are observed to be 0.772, 0.848, and 0.923. Furthermore, the calibration curves demonstrated excellent consistency between observed values and actual values. Finally, the decision curve analysis curve indicated substantial net clinical benefit.

CONCLUSION

Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model, demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Novel genome-wide DNA methylation profiling reveals distinct epigenetic landscape, prognostic model and cellular composition of early-stage lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has been a leading cause of cancer-related mortality worldwide. Early intervention can significantly improve prognosis. DNA methylation could occur in the early stage of tumor. Comprehensive understanding the epigenetic landscape of early-stage LUAD is crucial in understanding tumorigenesis.

METHODS

Enzymatic methyl sequencing (EM-seq) was performed on 23 tumors and paired normal tissue to reveal distinct epigenetic landscape, for compared with The Cancer Genome Atlas (TCGA) 450K methylation microarray data. Then, an integrative analysis was performed combined with TCGA LUAD RNA-seq data to identify significant differential methylated and expressed genes. Subsequently, the prognostic risk model was constructed and cellular composition was analyzed.

RESULTS

Methylome analysis of EM-seq comparing tumor and normal tissues identified 25 million cytosine-phosphate-guanine (CpG) sites and 30,187 differentially methylated regions (DMR) with a greater number of untraditional types. EM-seq identified a significantly higher number of CpG sites and DMRs compared to the 450K microarray. By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset. Finally, cell deconvolution based on the in-house EM-seq methylation profile was used to estimate cellular composition of early-stage LUAD.

CONCLUSIONS

This study firstly delves into novel pattern of epigenomic DNA methylation and provides a multidimensional analysis of the role of DNA methylation revealed by EM-seq in early-stage LUAD, providing distinctive insights into its potential epigenetic mechanisms.

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

BACKGROUND

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.

METHODS

Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.

RESULTS

The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.

CONCLUSION

The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

Clinical value of molecular subtypes identification based on anoikis-related lncRNAs in castration-resistant prostate cancer

BACKGROUND

Anoikis is a distinctive type of apoptosis. It is involved in tumor progression and metastasis. But its function in castration-resistant prostate cancer (CRPC) remains veiled. We aimed to develop a prognostic indicator based on anoikis-related long non-coding RNAs (arlncRNAs) and to investigate their biological function in CRPC.

MATERIAL AND METHOD

Differentially expressed anoikis-related genes were extracted from two CRPC datasets, GSE51873, and GSE78201. Four lncRNAs associated with the anoikis-related genes were selected. A risk model based on these lncRNAs was developed and validated in The Cancer Genome Atlas (TCGA) and the Memorial Sloan-Kettering Cancer Center (MSKCC) prostate cancer cohorts. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, immune checkpoints expression, and drug susceptibility were performed based on the model. To identify the biofunction of anoikis-related lncRNA, CCK-8 assays, colony formation assays, and flow cytometry were used.

RESULT

Twenty-nine anoikis-related genes were differentially expressed in the CRPC datasets. And 36 prognostic arlncRNAs were selected for the LASSO Cox analysis. Patients were subsequently classified into two subtypes by constructing an anoikis-related lncRNA based prognostic index (ARPI). The accuracy of this index was validated. KEGG enrichment analysis revealed that the high-ARPI group was enriched in cancer-related and immune-related pathways. Immune infiltration analysis has indicated a positive association between high-ARPI groups and increased immune infiltration. Fulvestrant, OSI-027, Lapatinib, Dabrafenib, and Palbociclib were identified as potential sensitive drugs for high-ARPI patients. In vitro experiments exhibited that silencing LINC01138 dampened the proliferation, migration and enzalutamide resistance in CRPC. Furthermore, it stimulated apoptosis and inhibited the eithelial-mesenchymal transition process.

CONCLUSION

Four arlncRNAs were identified and a risk model was established to predict the prognosis of patients with prostate cancer. Immune infiltration and drug susceptibility analysis revealed a potential therapeutic strategy for patients with castration-resistant prostate cancer.

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach

Background

In the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination.

Methods

Download glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes.

Results

The expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model.

Conclusions

Our study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Prognostic models for immunotherapy in non-small cell lung cancer: A comprehensive review

The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of lung cancer. Given the limited clinical benefits of immunotherapy in patients with non-small cell lung cancer (NSCLC), various predictors have been shown to significantly influence prognosis. However, no single predictor is adequate to forecast patients' survival benefit. Therefore, it's imperative to develop a prognostic model that integrates multiple predictors. This model would be instrumental in identifying patients who might benefit from ICIs. Retrospective analysis and small case series have demonstrated the potential role of these models in prognostic prediction, though further prospective investigation is required to evaluate more rigorously their application in these contexts. This article presents and summarizes the latest research advancements on immunotherapy prognostic models for NSCLC from multiple omics perspectives and discuss emerging strategies being developed to enhance the domain.

Characterization of macrophages in head and neck squamous cell carcinoma and development of MRG-based risk signature

Macrophages are immune cells in the TME that can not only inhibit angiogenesis, extracellular matrix remodeling, cancer cell proliferation, and metastasis but also mediate the phagocytosis and killing of cancer cells after activation, making them key targets in anti-tumor immunotherapy. However, there is little research on macrophages and their relation to disease prognosis in HNSCC. Initially, we collected scRNA-seq, bulk RNA-seq, and clinical data. Subsequently, we identified macrophages and distinguished MRGs. Using the K-means algorithm, we performed consensus unsupervised clustering. Next, we used ssGSEA analysis to assess immune cell infiltration in MRG clusters. A risk model was established using multivariate Cox analysis. Then, Kaplan-Meier, ROC curves, univariate and multivariate COX analyses, and C-index was used to validate the predictive power of the signature. The TIDE method was applied to assess the response to immunotherapy in patients diagnosed with HNSCC. In addition, drug susceptibility predictions were made for the GDSC database using the calcPhenotype function. We found that 8 MRGs had prognostic potential. Patients in the MRG group A had a higher probability of survival, and MRG clusters A and B had different characteristics. Cluster A had a higher degree of expression and infiltration in MRG, indicating a closer relationship with MRG. The accuracy of the signature was validated using univariate and multivariate Cox analysis, C-index, and nomogram. Immune landscape analysis found that various immune functions were highly expressed in the low-risk group, indicating an improved response to immunotherapy. Finally, drugs with high sensitivity to HNSCC (such as 5-Fluorouracil, Temozolomide, Carmustine, and EPZ5676) were explored and analyze the malignant characteristics of HNSCC. We constructed a prognostic model using multivariate Cox analysis, consisting of 8 MRGs (TGM2, STC1, SH2D3C, PIK3R3, MAP3K8, ITGA5, ARHGAP4, and AQP1). Patients in the low-risk group may have a higher response to immunotherapy. The more prominent drugs for drug selection are 5-fluorouracil, temozolomide and so on. Malignant features associated with HNSCC include angiogenesis, EMT, and the cell cycle. This study has opened up new prospects for the prognosis, prediction, and clinical treatment strategy of HNSCC.

Identification of crucial ubiquitin-associated genes for predicting the effects of immunotherapy and therapeutic agents in colorectal cancer

BACKGROUND

A growing body of research indicates that colorectal cancer (CRC) is significantly influenced by the ubiquitin-proteasome system. Nevertheless, reliable immune landscapes and ubiquitin-associated prognostic markers are still scarce.

METHODS

We systematically analyzed the RNA-seq data of 2,830 ubiquitin-related genes from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A CRC prognostic risk model was developed based on ubiquitin-associated gene signatures. In-depth multi-dimensional analyses were performed on ubiquitin-related subgroups with high and low risk. Drug response sensitivity for high-risk CRC patients was also predicted.

RESULTS

A total of 131 ubiquitin-related differentially expressed genes were retrieved, of which 9 prognostic genes for CRC were ultimately identified and further validated by our clinical CRC tumor and adjacent normal samples. The expression pattern of these 9 ubiquitin-associated genes was found to be strongly related to overall survival, immune cell fractions, and immune-related genes of CRC patients. CRC patients stratified by the ubiquitin prognostic model exhibited distinct clinicopathological characteristics and immune landscapes. A comprehensive framework for personalized medicine prediction identified regorafenib and sorafenib as the most promising therapeutic agents for high ubiquitin-related risk CRC patients, which was confirmed in cell viability assays.

CONCLUSIONS

Ubiquitin characteristics can reflect CRC prognosis and help develop innovative biomarkers for precision treatment.

Identification and Validation of Basement Membrane Related LncRNA Signatures as a Novel Prognostic Model for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a significant cancer with limited treatments and a poor prognosis, with the basement membrane (BM) playing a crucial role in its initiation and growth. This study utilized data from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO) databases to identify basement membrane-related genes differentially expressed in HCC. Through gene co-expression analysis, BM-associated long non-coding RNAs (lncRNAs) were discovered. LncRNAs related to HCC survival were selected via univariate analysis, and a prognostic model was constructed using LASSO regression and multivariate analysis. This model effectively classified HCC patients into high and low-risk groups, uncovering significant differences in prognosis, immune response, mutation, and drug sensitivity. Six BM-related lncRNAs (GSEC, MIR4435-2HG, AC092614.1, AC127521.1, LINC02580, and AC008050.1) were validated in normal and HCC cell lines, and the key role of AC092614.1 in regulating proliferation, migration, and invasion of HCC cells in vitro was explored. This research emphasizes the prognostic and therapeutic relevance of BM-related lncRNAs in HCC, highlighting AC092614.1's role in disease progression and as a potential target for targeted therapy.

PPP2R1A silencing suppresses LUAD progression by sensitizing cells to nelfinavir-induced apoptosis and pyroptosis

Lung adenocarcinoma is a major public health problem with the low 5-year survival rate (15%) among cancers. Aberrant alterations of meiotic genes, which have gained increased attention recently, might contribute to elevated tumor risks. However, systematic and comprehensive studies based on the relationship between meiotic genes and LUAD recurrence and treatment response are still lacking. In this manuscript, we first confirmed that the meiosis related prognostic model (MRPM) was strongly related to LUAD progression via LASSO-Cox regression analyses. Furthermore, we identified the role of PPP2R1A in LUAD, which showed more contributions to LUAD process compared with other meiotic genes in our prognostic model. Additionally, repression of PPP2R1A enhances cellular susceptibility to nelfinavir-induced apoptosis and pyroptosis. Collectively, our findings indicated that meiosis-related genes might be therapeutic targets in LUAD and provided crucial guidelines for LUAD clinical intervention.

Construction of immune-related gene pairs signature to predict the overall survival of multiple myeloma patients based on whole bone marrow gene expression profiling

Multiple myeloma (MM) is a plasma cell dyscrasia that is characterized by the uncontrolled proliferation of malignant PCs in the bone marrow. Due to immunotherapy, attention has returned to the immune system in MM, and it appears necessary to identify biomarkers in this area. In this study, we created a prognostic model for MM using immune-related gene pairs (IRGPs), with the advantage that it is not affected by technical bias. After retrieving microarray data of MM patients, bioinformatics analyses like COX regression and least absolute shrinkage and selection operator (LASSO) were used to construct the signature. Then its prognostic value is assessed via time-dependent receiver operating characteristic (ROC) and the Kaplan-Meier (KM) analysis. We also used XCELL to examine the status of immune cell infiltration among MM patients. 6-IRGP signatures were developed and proved to predict MM prognosis with a P-value of 0.001 in the KM analysis. Moreover, the risk score was significantly associated with clinicopathological characteristics and was an independent prognostic factor. Of note, the combination of age and β2-microglobulin with risk score could improve the accuracy of determining patients' prognosis with the values of the area under the curve (AUC) of 0.73 in 5 years ROC curves. Our model was also associated with the distribution of immune cells. This novel signature, either alone or in combination with age and β2-microglobulin, showed a good prognostic predictive value and might be used to guide the management of MM patients in clinical practice.

NET-related gene signature for predicting AML prognosis

Acute Myeloid Leukemia (AML) is a malignant blood cancer with a high mortality rate. Neutrophil extracellular traps (NETs) influence various tumor outcomes. However, NET-related genes (NRGs) in AML had not yet received much attention. This study focuses on the role of NRGs in AML and their interaction with the immunological microenvironment. The gene expression and clinical data of patients with AML were downloaded from the TCGA-LAML and GEO cohorts. We identified 148 NRGs through the published article. Univariate Cox regression was used to analyze the association of NRGs with overall survival (OS). The least absolute shrinkage and selection operator were utilized to assess the predictive efficacy of NRGs. Kaplan-Meier plots visualized survival estimates. ROC curves assessed the prognostic value of NRG-based features. A nomogram, integrating clinical information and prognostic scores of patients, was constructed using multivariate logistic regression and Cox proportional hazards regression models. Twenty-seven NRGs were found to significantly impact patient OS. Six NRGs-CFTR, ENO1, PARVB, DDIT4, MPO, LDLR-were notable for their strong predictive ability regarding patient survival. The ROC values for 1-, 3-, and 5-year survival rates were 0.794, 0.781, and 0.911, respectively. In the training set (TCGA-LAML), patients in the high NRG risk group showed a poorer prognosis (p < 0.001), which was validated in two external datasets (GSE71014 and GSE106291). The 6-NRG signature and corresponding nomograms exhibit superior predictive accuracy, offering insights for pre-immune response evaluation and guiding future immuno-oncology treatments and drug selection for AML patients.

Number of positive lymph nodes affects oncologic outcomes in cN0 mucoepidermoid carcinoma of the major salivary gland

The survival significance of the number of positive lymph nodes in salivary gland carcinoma remains unclear. Thus, the current study aimed to determine the effect of the number of positive lymph nodes on disease-specific survival (DSS) and overall survival (OS) in cN0 mucoepidermoid carcinoma (MEC) of the major salivary gland. Patients surgically treated for MEC of the major salivary gland between 1975 and 2019 were retrospectively enrolled from the surveillance, epidemiology, and end results database. The total population was randomly divided into training and test groups (1:1). Primary outcome variables were DSS and OS. Prognostic models were constructed based on the independent prognostic factors determined using univariate and multivariate Cox analyses in the training group and were validated in the test group using C-index. A total of 3317 patients (1624 men and 1693 women) with a mean age of 55 ± 20 years were included. The number of positive lymph nodes was an independent prognostic factor for both DSS and OS, but the effect began when at least two positive lymph nodes for DSS and three positive lymph nodes for OS were found. Predictive models for DSS and OS in the training group had C-indexes of 0.873 (95% confidence interval [CI] 0.853-0.893) and 0.835 (95% CI 0.817-0.853), respectively. The validation of the test group showed C-indexes of 0.877 (95% CI 0.851-0.902) for DSS and 0.820 (95% CI 0.798-0.842) for OS. The number of positive lymph nodes was statistically associated with survival in cN0 major salivary gland MEC. The current prognostic model could provide individualized follow-up strategies for patients with high reliability.

Novel targets and their functions in the prognosis of uterine corpus endometrial cancer patients

Aberrant mRNA expression is implicated in uterine corpus endometrial carcinoma (UCEC) oncogenesis and progression. However, effective prognostic biomarkers for UCEC remain limited. We aimed to construct a reliable multi-gene risk model using gene expression profiles. Utilizing TCGA data (543 UCEC samples, 35 controls), we identified 1517 differentially acting genes. Weighted gene co-expression complex analysis (WGCCA), hub gene screening, and risk regression analysis (RRA) were employed to determine prognosis-related genes and construct the risk model. Nomograms visualized risk scores and receiver operator characteristic (ROC) curves assessed model performance. Seven novel prognosis-related hub genes (ANGPT1, ASB2, GAL, GDF7, ONECUT2, SV2B, TRPC6) were identified. The model's concordance index (C index) by multivariate Cox regression analysis was 0.79. ROC curves yielded AUCs of 0.811 (3-year) and 0.79 (5-year), demonstrating the model's efficacy in predicting UCEC survival. Our study proposes a promising seven-biomarker risk model for predicting UCEC prognosis, offering potential clinical utility.

The prognostic value of sialylation-related long non-coding RNAs in lung adenocarcinoma

There has been increasing interest in the role of epigenetic modification in cancers recently. Among the various modifications, sialylation has emerged as a dominant subtype implicated in tumor progression, metastasis, immune evasion, and chemoresistance. The prognostic significance of sialylation-related molecules has been demonstrated in colorectal cancer. However, the potential roles and regulatory mechanisms of sialylation in lung adenocarcinoma (LUAD) have not been thoroughly investigated. Through Pearson correlation, univariate Cox hazards proportional regression, and random survival forest model analyses, we identified several prognostic long non-coding RNAs (lncRNAs) associated with aberrant sialylation and tumor progression, including LINC00857, LINC00968, LINC00663, and ITGA9-AS1. Based on the signatures of four lncRNAs, we classified patients into two clusters with different landscapes using a non-negative matrix factorization approach. Collectively, patients in Cluster 1 (C1) exhibited worse prognoses than those in Cluster 2 (C2), as well as heavier tumor mutation burden. Functional enrichment analysis showed the enrichment of several pro-tumor pathways in C1, differing from the upregulated Longevity and programmed cell death pathways in C2. Moreover, we profiled immune infiltration levels of important immune cell lineages in two subgroups using MCPcounter scores and single sample gene set enrichment analysis scores, revealing a relatively immunosuppressive microenvironment in C1. Risk analysis indicated that LINC00857 may serve as a pro-tumor regulator, while the other three lncRNAs may be protective contributors. Consistently, we observed upregulated LINC00857 in C1, whereas increased expressive levels of LINC00968, LINC00663, and ITGA9-AS1 were observed in C2. Finally, drug sensitivity analysis suggested that patients in the two groups may benefit from different therapeutic strategies, contributing to precise treatment in LUAD. By integrating multi-omics data, we identified four core sialylation-related lncRNAs and successfully established a prognostic model to distinguish patients with different characterizations. These findings may provide some insights into the underlying mechanism of sialylation, and offer a new stratification way as well as clinical guidance in LUAD.

Machine learning and optical coherence tomography-derived radiomics analysis to predict persistent diabetic macular edema in patients undergoing anti-VEGF intravitreal therapy

BACKGROUND

Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME.

METHODS

A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated.

RESULTS

The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson's R = 0.44, p < 0.001).

CONCLUSION

The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model's robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.

Identification and validation of a lactate metabolism-related six-gene prognostic signature in intrahepatic cholangiocarcinoma

PURPOSE

Intrahepatic cholangiocarcinoma (iCCA) is a highly malignant and fatal liver tumor with increasing incidence worldwide. Lactate metabolism has been recently reported as a crucial contributor to tumor progression and immune regulation in the tumor microenvironment. However, it remains poorly identified about the biological functions of lactate metabolism in iCCA, which hinders the development of prognostic tools and therapeutic interventions.

METHODS

The univariate Cox regression analysis and Boruta algorithm were utilized to identify key lactate metabolism-related genes (LMRGs), and a prognostic signature was constructed based on LMRG scores. Genomic variations and immune cell infiltration were evaluated in the high and low LMRG score groups. Finally, the biological functions of key LMRGs were verified with in vitro and in vivo experiments.

RESULTS

Patients in the high LMRG score group exhibit a poor prognosis compared to those in the low LMRG score group, with a high frequency of TP53 and KRAS mutations. Moreover, the infiltration and function of NK cells were compromised in the high LMRG score group, consistent with the results from two independent single-cell RNA sequencing datasets and immunohistochemistry of tissue microarrays. Experimental data revealed that lactate dehydrogenase A (LDHA) knockdown inhibited proliferation and migration in iCCA cell lines and tumor growth in immunocompetent mice.

CONCLUSION

Our study revealed the biological roles of LDHA in iCCA and developed a reliable lactate metabolism-related prognostic signature for iCCA, offering promising therapeutic targets for iCCA in the clinic.

Application of neutrophil-to-lymphocyte-to-monocyte ratio in predicting mortality risk in adult patients with septic shock: A retrospective cohort study conducted at a single center

Background

Sepsis is a life-threatening condition characterized by an aberrant host response to infection, resulting in multi-organ dysfunction. The application of currently available prognostic indicators for sepsis in primary hospitals is challenging. In this retrospective study, we established a novel index, the neutrophil-to-lymphocyte-to-monocyte ratio (NLMR), based on routine blood examination upon admission, and assessed its prognostic value for early mortality risk in adult patients with septic shock.

Methods

This study included clinical data from adult patients with septic shock who were admitted to the hospital between January 1, 2018, and December 31, 2022. Training and validation sets were constructed, and patients were categorized into "survival" and "death" groups based on their survival status within the 28-day hospitalization period. Baseline data, including demographic characteristics and comorbidities, and laboratory results, such as complete blood count parameters, were collected for analysis. The Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were documented.The NLMR was determined through the utilization of multivariate binary logistic regression analysis, leading to the development of a risk model aimed at predicting early mortality in adult patients suffering from septic shock.

Results

Overall, 112 adult patients with septic shock were enrolled in this study, with 84 and 28 patients in the training and validation sets, respectively. Multivariate binary logistic analysis revealed that the neutrophil, lymphocyte, and monocyte counts independently contributed to the mortality risk (odds ratios = 1.22, 0.08, and 0.16, respectively). The NLMR demonstrated an area under the receiver operating characteristic curve (ROC-AUC) of 0.83 for internal validation in the training set and 0.97 for external validation in the validation set. Both overall model quality values were significantly high at 0.74 and 0.91, respectively (P < 0.05). NLMR exhibited a higher ROC-AUC value of 0.88 than quick SOFA (ROC-AUC = 0.71), SOFA (ROC-AUC = 0.83), and APACHE II (ROC-AUC = 0.78).

Conclusion

NLMR may be a potential marker for predicting the risk of early death in adult patients with septic shock, warranting further exploration and verification.

A prognostic model for triple-negative breast cancer patients with liver metastasis: A population-based study

However, it is still difficult for clinicians to establish prognostic stratifications and therapeutic strategies because of the lack of tools for predicting the survival of triple-negative breast cancer patients with liver metastases (TNBC-LM). Based on clinical data from large populations, a sensitive and discriminative nomogram was developed and validated to predict the prognosis of TNBC patients with LM at initial diagnosis or at the later course.

Introduction/background

Liver metastasis (LM) in TNBC patients is associated with significant morbidity and mortality. The objective of this study was to construct a clinical model to predict the survival of TNBC-LM patients.

Materials and methods

Clinicopathologic data were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Affiliated Hospital of Sun Yat-Sen University (FAFSYU). Based on patients with newly diagnosed TNBC with LM (nTNBC-LM) from the SEER database, a predictive nomogram was established and validated. Its predictive effect on TNBC patients with LM at later disease course by enrolling TNBC patients from FAFSYU who developed LM later. The prognostic effect of different treatment for nTNBC-LM was further assessed.

Results

A prognostic model was developed and validated to predict the prognosis of TNBC-LM patients. For LM patients diagnosed at the initial or later treatment stage, the C-index (0.712, 0.803 and 0.699 in the training, validation and extended groups, respectively) and calibration plots showed the acceptable prognostic accuracy and clinical applicability of the nomogram. Surgical resection on the primary tumour and chemotherapy were found to be associated with significantly better overall survival (OS).

Conclusion

A sensitive and discriminative model was developed to predict OS in TNBC-LM patients both at and after initial diagnosis.

Identification of cellular senescence-associated genes as new biomarkers for predicting the prognosis and immunotherapy response of non-small cell lung cancer and construction of a prognostic model

Background

Globally, lung carcinoma remains the leading cause of death, with its associated morbidity and mortality rates remaining elevated. Despite the slow advancement of treatment, the outlook remains bleak. Cellular senescence represents a halt in the cell cycle, encompassing a range of physiological and pathological activities, along with diverse phenotypic alterations, including variations in secretory phenotype, macromolecular harm, and metabolic disturbances. Research has revealed its vital function in the formation and growth of tumors. This study aimed to examine cellular senescence-related mRNAs linked to the outlook of non-small cell lung cancer (NSCLC) and to formulate a predictive risk framework for NSCLC.

Methods

We acquired the NSCLC expression data from The Cancer Genome Atlas (TCGA) to examine mRNAs linked to cellular senescence. Both single-variable and multiple-variable cox proportion risk assessments were utilized to determine the traits of cellular senescence-related mRNAs linked to NSCLC prognosis. Subsequently, the prognostic model for cellular senescence-related mRNAs was integrated with clinical-pathological characteristics to create a prognostic nomogram. Furthermore, the study delved into the risk-oriented predictive model, examining immune infiltration and responses to immunotherapy among both high and low-risk categories.

Results

Utilizing both univariate and multivariate Cox proportion risk assessments, a risk model comprising 12 mRNAs associated with cellular aging was ultimately developed: IGFBP1, TLR3, WT1, ID1, PTTG1, ERRFI1, HEPACAM, MAP2K3, RAD21, NANOG, PRKCD, SOX5. Univariate analysis and multivariate analysis illustrated that the risk score served as a standalone indicator for prognosis, and the hazard ratio (HR) of the risk score were 1.182 (1.139-1.226) (p < 0.001) and 1.162 (1.119 - 1.206) (p < 0.001), respectively. Individual prognoses were forecasted using nomogram, c-index, and principal component analysis (PCA). Furthermore, the risk-oriented model revealed notable statistical variances in immune infiltration and response to immunotherapy among the high and low risk categories.

Conclusions

This study shows that mRNAs related to cell senescence associated with prognosis are reliable predictors of NSCLC immunotherapy reaction and prognosis.

Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data

BACKGROUND

Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments.

RESULTS

In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC.

CONCLUSION

In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

A brief step-by-step guide on conducting a systematic review and meta-analysis of prognostic model studies

Prognostic models provide an avenue to predict the risk of individual patients and support shared-decision making. Many prognostic models are published annually, and systematic reviews provide an avenue to collate the existing evidence behind prognostic models to determine whether a model demonstrates adequate predictive performance and is ready for real-world use. This article provides a brief step-by-step guide on how to conduct a systematic review and meta-analysis of prognostic model studies and how these reviews differ from systematic reviews of therapy and diagnosis.

A Study on Prognosis of Diffuse Glioma Based on Clinical Factors and Magnetic Resonance Imaging Radiomics

OBJECTIVE

To construct an optimal prognostic model to assess the prognosis of patients with diffuse glioma.

METHODS

Preoperative magnetic resonance imaging and clinical data were retrospectively collected from 266 patients (training cohort: validation cohort=7:3) with pathologically confirmed diffuse gliomas. A radiomics prognostic model (R-model) based on the radiomics features was constructed. A prognostic model based on clinical factors (C-model) and a fusion model (F-model) was also constructed. Based on the optimal model of three models, the nomogram was constructed. Finally, a "Prognosis Calculator for Diffuse Glioma" was constructed based on the nomogram.

RESULTS

The c-index of the R-, C-, and F-models in the validation cohort was 0.742, 0.796, and 0.814, respectively. In the validation cohort, the 1-year area under the curve of the R-, C-, and F-models was 0.749, 0.806, and 0.836, respectively; the 3-year area under the curve was 0.896, 0.966, and 0.963, respectively. In the training cohort, validation cohort, all cohorts, and different grades of glioma cohorts, F-model (optimal model) could identify low- and high-risk groups well. The "Prognosis Calculator for Diffuse Glioma" was available at https://github.com/HDCurry/prognosis.

CONCLUSIONS

Among the three models, the F-model (radiomics combined with clinical factors) had optimal predictive efficacy and could more accurately assess the prognosis of diffuse glioma. The "Prognosis Calculator for Diffuse Glioma" constructed based on this model could assist clinicians in more easily and accurately assessing the prognosis of patients with diffuse glioma, thus enabling them to make more reasonable treatment strategies.

The Predictive Value of Neutrophil Extracellular Trap-Related Risk Score in Prognosis and Immune Microenvironment of Colorectal Cancer Patients

Colorectal cancer (CRC) has brought great healthy burden for patients. Neutrophil extracellular traps (NETs) have been explored in several tumors, while it remains largely unclear in CRC. CRC-related data were downloaded from Cancer Genome Atlas and Gene Expression Omnibus databases. Then, a NET risk score was built after univariate Cox and LASSO Cox regression analysis. Prognostic value was evaluated via survival analysis, stratification analysis, and ROC analysis. The functional enrichment analysis was conducted basing on bulk and scRNA-seq data. The immune landscape difference was analyzed using CIBERSORT, XCell, and MCPcounter portals. NET risk score was built for CRC patients, basing on G0S2, HIST1H2BC, CRISPLD2, and IL17A. In TCGA-CRC and validation datasets, regardless of age or gender, high-risk CRC patients had significantly worse prognosis, besides higher NET risk score was mainly found in samples with MSI-H and advanced T, N, and M stages. Employing multiple databases, we noticed that M0 and M2 Macrophages infiltrated the most in high-risk CRC patients, besides M2 Macrophages and neutrophils showed positive correlation with NET risk score. A novel reliable prognostic NET risk score was developed for CRC patients, and high-risk patients had unfavorable prognosis with advanced disease status.

Exploration of vitamin D metabolic activity-related biological effects and corresponding therapeutic targets in prostate cancer

BACKGROUND

Previous studies have unequivocally demonstrated that the vitamin D (VD) metabolism pathway significantly influences prognosis and sensitivity to hormone therapy in prostate cancer (PCa). However, the precise underlying mechanism remains unclear.

METHODS

We performed molecular profiling of 1045 PCa patients, leveraging genes linked to VD synthesis and VD receptors. We then identified highly variable gene modules with substantial associations with patient stratification. Subsequently, we intersected these modules with differentially expressed genes between PCa and adjacent paracancerous tissues. Following a meticulous process involving single-factor regression and LASSO regression to eliminate extraneous variables and construct a prognostic model. Within the high-risk subgroup defined by the calculated risk score, we analyzed their differences in cell infiltration, immune status, mutation landscape, and drug sensitivity. Finally, we selected Apolipoprotein E (APOE), which featured prominently in this model for further experimental exploration to evaluate its potential as a therapeutic target.

RESULTS

The prognostic model established in this study had commendable predictive efficacy. We observed diminished infiltration of various T-cell subtypes and reduced expression of co-stimulatory signals from antigen-presenting cells. Mutation analysis revealed that the high-risk cohort harbored a higher frequency of mutations in the TP53 and FOXA genes. Notably, drug sensitivity analysis suggested the heightened responsiveness of high-risk patients to molecular inhibitors targeting the Bcl-2 and MAPK pathways. Finally, our investigation also confirmed that APOE upregulates the proliferative and invasive capacity of PCa cells and concurrently enhances resistance to androgen receptor antagonist therapy.

CONCLUSION

This comprehensive study elucidated the potential mechanisms through which this metabolic pathway orchestrates the biological behavior of PCa and findings hold promise in advancing the development of combination therapies in PCa.

Identification of Prognostic and Immune Characteristics of Two Lung Adenocarcinoma Subtypes Based on TRPV Channel Family Genes

Lung adenocarcinoma (LUAD) is one of the deadliest malignant tumors worldwide. Transient receptor potential vanilloid (TRPV) channels take pivotal parts in many cancers, but their impact on LUAD remains unexplored. In this study, LUAD samples were classified into two subtypes according to the expression characteristics of TRPV1-6 genes, with LUAD subtype cluster2 exhibiting significantly higher survival rates than cluster1. Subsequently, analysis of differentially expressed genes (DEGs) was performed between cluster1 and cluster2, revealing enrichment of DEGs in channel activity and Ca2+ signaling pathways. We established a protein-protein interaction network based on DEGs and constructed a LUAD prognostic model by using Cox regression analysis based on genes corresponding to 170 protein nodes. The prognostic model demonstrated good predictive ability for patient prognosis, with higher survival rates observed in the low-risk (LR) group. The risk score was validated as an independent prognostic indicator, according to Cox regression analysis. A clinically applicable nomogram was plotted. Immunological analysis indicated that the LR and high-risk (HR) groups had varied proportions of immune cell infiltration. The immunotherapy prediction indicated that LUAD patients in LR group had a greater likelihood to benefit from immune checkpoint blockade therapy. Furthermore, we hypothesized that the expression patterns of feature genes in the LUAD model were related to the sensitivity to lung cancer therapeutic drugs TAS-6417 and Erlotinib. To sum up, our LUAD prognostic model possessed clinical applicability for prognosis and immunotherapy response prediction.

Patients' selection and trial matching in early-phase oncology clinical trials

BACKGROUND

Early-phase clinical trials (EPCT) represent an important part of innovations in medical oncology and a valuable therapeutic option for patients with metastatic cancers, particularly in the era of precision medicine. Nevertheless, adult patients' participation in oncology clinical trials is low, ranging from 2% to 8% worldwide, with unequal access, and up to 40% risk of early discontinuation in EPCT, mostly due to cancer-related complications.

DESIGN

We review the tools and initiatives to increase patients' orientation and access to early phase cancer clinical trials, and to limit early discontinuation.

RESULTS

New approaches to optimize the early-phase clinical trial referring process in oncology include automatic trial matching, tools to facilitate the estimation of patients' prognostic and/or to better predict patients' eligibility to clinical trials. Classical and innovative approaches should be associated to double patient recruitment, improve clinical trial enrollment experience and reduce early discontinuation rates.

CONCLUSIONS

Whereas EPCT are essential for patients to access the latest medical innovations in oncology, offering the appropriate trial when it is relevant for patients should increase by organizational and technological innovations. The oncologic community will need to closely monitor their performance, portability and simplicity for implementation in daily clinical practice.

Risk factors for short-term prognosis of end-stage liver disease complicated by invasive pulmonary aspergillosis

BACKGROUND AND AIM

Patients with end-stage liver disease (ESLD) are susceptible to invasive pulmonary aspergillosis (IPA). This study aimed to investigate the risk factors affecting the occurrence and short-term prognosis of ESLD complicated by IPA.

METHODS

This retrospective case-control study included 110 patients with ESLD. Of them, 27 ESLD-IPA received antifungal therapy with amphotericin B (AmB); 27 AmB-free-treated ESLD-IPA patients were enrolled through 1:1 propensity score matching. Fifty-six ESLD patients with other comorbid pulmonary infections were enrolled as controls. The basic features of groups were compared, while the possible risk factors affecting the occurrence and short-term outcomes of IPA were analyzed.

RESULTS

Data analysis revealed invasive procedures, glucocorticoid exposure, and broad-spectrum antibiotic use were independent risk factors for IPA. The 54 patients with ESLD-IPA exhibited an overall treatment effectiveness and 28-d mortality rate of 50.00% and 20.37%, respectively, in whom patients treated with AmB-containing showed higher treatment efficacy than patients treated with AmB-free antifungal regimens (66.7% vs. 33.3%, respectively, χ2 = 6.000, P = 0.014). Multivariate logistic regression analysis revealed that the treatment regimen was the only predictor affecting patient outcomes, with AmB-containing regimens were 4.893 times more effective than AmB-free regimens (95% CI, 1.367-17.515; P = 0.015). The only independent predictors affecting the 28-d mortality rate were neutrophil-to-lymphocyte ratio and IPA diagnosis (OR = 1.140 and 10.037, P = 0.046 and 0.025, respectively).

CONCLUSIONS

Glucocorticoid exposure, invasive procedures, and broad-spectrum antibiotic exposure increased the risk of IPA in ESLD patients. AmB alone or combined with other antifungals may serve as an economical, safe, and effective treatment option for ESLD-IPA.

Toward a prognostic model for all-cause mortality among old people with disability in long-term care in China

BACKGROUND

Current prognostic model of all-cause mortality may not be applicable for old people with disability in long-term care due to the absence of injury- and care-related predictors. We aimed to develop a prognostic model specifically tailored to this population, based on comprehensive predictors.

METHOD

We conducted a prospective study involving 41,004 participants aged ≥60 with disability in long-term care across 16 study sites in Southwest China from 2017 to 2021. Participants' demographics, clinical characteristics, disability status, and injury- and care-related information at baseline were used as candidate predictors. We employed a LASSO Cox regression model to develop the prognostic model using the training set (70 % of participants), and the predictive performance was validated in the validation set (30 % of participants). The prognostic index (PI) scores of the prognostic model were used to quantify mortality risk.

RESULTS

At the end of the 4-year follow-up, 17,797 deaths (43.4 %) were observed. The prognostic model revealed several powerful and robust predictors of mortality across the total sample and subgroups, including higher age, living with comorbidities, physical and perceptual disability, and living with pressure sores. Non-professional care was an additional predictor in older participants. The risk of death for participants in the highest quartile of PI scores was approximately four-fold higher compared to those in the lowest quartile.

CONCLUSIONS

We developed and validated a prognostic model that can be practically utilized to identify individuals and populations at risk of death among old people with disability in long-term care.

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

The TAE score predicts prognosis of unresectable HCC patients treated with TACE plus lenvatinib with PD-1 inhibitors

BACKGROUND AND AIMS

Transcatheter arterial chemoembolization combined with lenvatinib and PD-1 inhibitors (triple therapy) exhibits promising efficacy for unresectable hepatocellular carcinoma (uHCC). We aimed to evaluate the prognosis of patients with uHCC who received triple therapy and develop a prognostic scoring model to identify patients who benefit the most from triple therapy.

METHODS

A total of 246 patients with uHCC who received triple therapy at eight centers were included and assigned to the training and validation cohorts. Prognosis was evaluated by the Kaplan-Meier curves. The prognostic model was developed by utilizing predictors of overall survival (OS), which were identified through the Cox proportional hazards model.

RESULTS

In the training cohort, the 3-year OS was 52.0%, with a corresponding progression-free survival (PFS) of 30.6%. The median PFS was 13.2 months [95% confidence interval, 9.7-16.7]. Three variables (total bilirubin ≥ 17 μmol/L, alpha-fetoprotein ≥ 400 ng/mL, and extrahepatic metastasis) were predictors of poor survival and were used for developing a prognostic model (TAE score). The 2-year OS rates in the favorable (0 points), intermediate (1 point), and dismal groups (2-3 points) were 96.9%, 61.4%, and 11.4%, respectively (p < 0.001). The PFS was also stratified according to the TAE score. These findings were confirmed in an external validation cohort.

CONCLUSIONS

Triple therapy showed encouraging clinical outcomes, and the TAE score aids in identifying patients who would benefit the most from triple therapy.

A prognostic model for overall survival in recurrent glioma patients treated with bevacizumab-containing therapy

Predictive markers and prognostic models are useful for the individualization of cancer treatment. In this study, we sought to identify clinical and molecular factors to predict overall survival in recurrent glioma patients receiving bevacizumab-containing regimens. A cohort of 102 patients was retrospectively collected from June 2011 to January 2022 at our institution. A nomogram was generated by Cox regression and feature selection algorithms based on 19 clinicopathological and 60 molecular variables. The model's performance was internally evaluated by bootstrapping in terms of discrimination and calibration. The median overall survival from the initiation of bevacizumab administration to death or last follow-up was 11.6 months (95% CI: 9.2-13.8 months) for all 102 patients, 10.2 months (95% CI: 6.4-13.3 months) for 66 patients with grade 4 tumors, and 13.8 months (lower limit of 95% CI: 11.5 months) for 36 patients with tumors of grade lower or not available. In the final model, a lower WHO 2021 grade (Grade lower or not available vs. Grade 4, HR: 0.398, 95% CI: 0.223-0.708, p = 0.00172), having received adjuvant radiochemotherapy (Yes vs. No, HR: 0.488, 95% CI: 0.268-0.888, p = 0.0189), and wildtype EGFR (Wildtype vs. Altered, HR: 0.193, 95% CI: 0.0506-0.733, p = 0.0157; Not available vs. Altered, HR: 0.386, 95% CI: 0.184-0.810, p = 0.0118) were significantly associated with longer overall survival in multivariate Cox regression. The overall concordance index was 0.652 (95% CI: 0.566-0.714), and the areas under the time-dependent curves for 6-, 12-, and 18-month overall survival were 0.677 (95% CI: 0.516-0.816), 0.654 (95% CI: 0.470-0.823), and 0.675 (95% CI: 0.491-0.860), respectively. A prognostic model for overall survival in recurrent glioma patients treated with bevacizumab-based therapy was established and internally validated. It could serve as a reference tool for clinicians to assess the extent the patients may benefit from bevacizumab and stratify their treatment response.

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as "hot" tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.