Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model.

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BCE-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.

BACKGROUND: Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment. METHODS: Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis. RESULTS: The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells. CONCLUSION: The CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.

TRIM8 as a predictor for prognosis in childhood acute lymphoblastic leukemia based on a signature of neutrophil extracellular traps.

Background: Neutrophil extracellular traps (NETs) can be attributed to the metastasis, occurrence, and immune evasion of cancer cells. We investigated the prognostic value of NET-related genes in childhood acute lymphoblastic leukemia (cALL) patients. Methods: Differential gene expression analysis was conducted on samples collected from public databases. Grouping them based on the expression level of NET-related genes, we assessed the correlation between immune cell types and the risk score for having a poor prognosis of cALL, with an evaluation of the sensitivity of drugs used in cALL. We further divided the groups, integrating survival data. Subsequently, methods including multivariable Cox algorithms, least absolute shrinkage and selection operator (LASSO), and univariable were utilized to create a risk model predicting prognosis. Experiments in cell lines and animals were performed to explore the functions of TRIM8, a gene selected by the model. To validate the role of TRIM8 in leukemia development, lentivirus-mediated overexpression or knockdown of TRIM8 was employed in mice with T-ALL and B-ALL. Results: Kaplan-Meier (KM) analysis underscored the importance of differentially expressed genes identified in the groups divided by genes participated in NETs, with enrichment analysis showing the mechanism. Correlation analysis revealed significant associations with B cells, NK cells, mast cells, T cells, plasma cells, dendritic cells, and monocytes. The IC50 values of drugs such as all-trans-retinoic acid (ATRA), axitinib, doxorubicin, methotrexate, sorafenib, and vinblastine were increased, while dasatinib exhibited a lower IC50. A total of 13 NET-related genes were selected in constructing the risk model. In the training, testing, and merged cohorts, KM analysis demonstrated significantly improved survival for low-risk cALL patients compared to high-risk cALL patients (p < 0.001). The area under the curve (AUC) indicated strong predictive performance. Experiments in Jurkat and SUP-B15 revealed that TRIM8 knockdown decreased the proliferation of leukemia cell lines. Further experiments demonstrated a more favorable prognosis in mice with TRIM8-knockdown leukemia cells. Results of cell lines and animals showed better outcomes in prognosis when TRIM8 was knocked down. Conclusion: We identified a novelty in a prognostic model that could aid in the development of personalized treatments for cALL patients. Furthermore, it revealed that the expression of TRIM8 is a contributing factor to the proliferation of leukemia cells and worsens the prognosis of cALL.

Can serum tumor marker densities according to tumor volume and testicle size be used to predict progression in patients with testicular cancer?

Background: The objective of this study is to determine the role of tumor marker density (TMD) values such as alpha-fetoprotein tumor volume ratio (ATVR), beta-human chorionic gonadotropin tumor volume ratio (ßTVR), alpha-fetoprotein testicle size ratio (ATSR), beta-human chorionic gonadotropin testicle size ratio (ßTSR), lactate dehydrogenase tumor volume ratio (LTVR), and lactate dehydrogenase testicle size ratio (LTSR) in the determination of progression-free survival (PFS) in patients with testicular cancer. Materials and methods: A retrospective study was conducted of 95 patients followed-up in our clinic with a diagnosis of testicular cancer between January 2015 and August 2022. Patients were grouped according to clinical stage, as either early stage (n = 50) or advanced stage (n = 45). Clinical and pathological data and TMD values for all patients were recorded. Results: The median age of patients was 35 years (21-63 years). All TMDs except LTVR in advanced stage patients were found to be significantly higher than those of early stage patients (p < 0.05). Median ATVR (2.58 vs. 0.0), ATSR (0.63 vs. 0.03), ßTVR (0.9 vs. 0.009), and ßTSR (0.18 vs. 0.007) of the nonseminoma patients were found to be significantly higher than those of the seminoma patients, respectively (p < 0.001). Progression-free survival (months) was decreased in seminoma patients with high values of ßTVR (11.3 ± 1.9 vs. 35.2 ± 0.7), ßTSR (16.2 ± 3.4 vs. 35.2 ± 0.75), LTVR (17.7 ± 3.4 vs. 35.2 ± 0.7), and LTSR (21.5 ± 3.13 vs. 35.09 ± 0.8) (p < 0.001). Decreased PFS (months) was associated with higher values of ATVR (5.37 ± 0.7 vs. 35.05 ± 0.93), ßTVR (7.4 ± 1.5 vs. 34.6 ± 1.3), ATSR (5.37 ± 0.75 vs. 35.05 ± 0.9), ßTSR (7 ± 1.5 vs. 34.6 ± 1.3), and LTSR (7.9 ± 1.2 vs. 34.3 ± 1.5) in nonseminoma patients (p < 0.001). Based on multivariate analysis, ßTVR-LTVR and ATVR-ATSR were determined to be independent risk factors for reduced PFS in seminoma and nonseminoma patients, respectively (p < 0.05). Conclusions: The results of this study suggest that the calculation of TMDs could be a promising and simple method for prediction of PFS among testicular cancer patients.

Clinical application value of long non-coding RNAs signatures of genomic instability in predicting prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) presents challenges due to its high recurrence and metastasis rates and poor prognosis. While current clinical diagnostic and prognostic indicators exist, their accuracy remains imperfect due to their biological complexity. Therefore, there is a quest to identify improved biomarkers for HCC diagnosis and prognosis. By combining long non-coding RNA (lncRNA) expression and somatic mutations, Duan et al identified five representative lncRNAs from 88 lncRNAs related to genomic instability (GI), forming a GI-derived lncRNA signature (LncSig). This signature outperforms previously reported LncSig and TP53 mutations in predicting HCC prognosis. In this editorial, we comprehensively evaluate the clinical application value of such prognostic evaluation model based on sequencing technology in terms of cost, time, and practicability. Additionally, we provide an overview of various prognostic models for HCC, aiding in a comprehensive understanding of research progress in prognostic evaluation methods.

Bioinformatics analysis of BTK expression in lung adenocarcinoma: implications for immune infiltration, prognostic biomarkers, and therapeutic targeting.

In recent years, as more and more lung-cancer patients have been treated with immunotherapeutic agents, their survival has been prolonged compared to before. It is well known that BTK (Bruton's tyrosine kinase) is predominantly found in cells of the hematopoietic system. However, there is a distinct lack of literature on BTK expression in lung adenocarcinoma (LUAD) patients and its effect on the immune microenvironment. Consequently, the main goal of this investigation was to analyze how BTK expression in lung adenocarcinoma affects its progression, along with its prognostic significance, through the utilization of bioinformatics online resources and publicly available databases. Data on the sequencing results and clinical records of lung adenocarcinoma patients were gathered from The Cancer Genome Atlas (TCGA) database. Based on the expression level of BKT, TCGA categorized lung adenocarcinoma patients into BTK high-expression and low-expression groups. We investigated the effects of BKT on clinicopathologic, genomic, and immunologic characteristics of lung adenocarcinoma patients. We analyzed BTK mRNA expression in tumors and normal tissues using two key resources: Tumor Immuno Estimation Resource 2.0 (TIMER 2.0) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2). We analyzed the prognosis of the patients using GEPIA2 and validated the results using univariate and multivariate analyses. In addition, we assessed BTK protein expression by Human Protein Atlas (HPA). We sought to elucidate the clinical prognostic significance of BTK in The TCGA using the online tool GEPIA 2. Furthermore, to clarify the biologic roles and pathways linked to BTK, we conducted a genomic enrichment analysis of the information. To predict the proportion of various immune cell infiltrations in the immune microenvironment of lung adenocarcinoma patients diagnosed in the TCGA database, we performed an analysis using the TIMER online tool. Using TIMER and CIBERSORT, the correlation between genes co-expressed with BTK and the corresponding tumor-infiltrating immune cells was explored; finally, the relationship between BTK expression and immune infiltration and immune checkpoints in the TMB group and the high and low groups was analyzed by R language analysis using the TCGA database. The expression of BTK provides some hints about the prognosis of the patients. The high expression of BTK is involved in immune response regulation signaling pathways, leukocyte-mediated immunity, leukocyte intercellular adhesion, graft rejection, and complement. Analysis of the GEPIA 2 database showed that BTK was co-expressed with the genes FGD2, SASH3, NCKAP1L, CD53, ARHGAP30 and LPXN. Increased expression of the above-mentioned genes resulted in increased proportions of CD8 + T cells, memory CD4 + T cells, B cells, macrophages, and dendritic cells, and decreased proportions of Treg cells and TH2 cells. In addition, our study revealed a strong positive correlation between various key immune checkpoints (e.g., PDCD1, CD274, PDCD1LG2, CTLA4, HAVCR2, LAG3, TIGIT, and SIGLEC15) and BTK expression. In conclusion, increased BTK expression in lung adenocarcinoma is closely associated with prolonged survival of lung-cancer patients. Moreover, the genes classified under the BTK high-expression group exhibit significant enrichment in immune-related pathways, suggesting a potential impact on the tumor microenvironment. We investigated the potential of BTK as a tumor suppressor gene in predicting prolonged patient survival. In addition, we further investigated the possibility that BTK further affects the immunotherapeutic response of patients by influencing the microenvironment of tumor immune infiltration, but the relevant mechanisms remain to be further studied.

Predictive value of prognostic nutritional index as prognostic biomarkers in patients with lymphoma: a systematic review and meta-analysis.

BACKGROUND: Several research have indicated the significant potential of the Prognostic Nutritional Index (PNI) as a prognostic biomarker in lymphoma patients. However, there is some inconsistency in the findings of a few studies. Hence, to offer a thorough evaluation of the predictive significance of PNI in lymphoma patients, we performed a meta-analysis to examine the prognostic value of PNI for survival outcomes in lymphoma patients. METHODS: We conducted a comprehensive search for pertinent works published up until December 2023 in databases such as PubMed, EMBASE, Cochrane Library, and Web of Science. We obtained hazard ratio (HR) data related to survival outcomes and computed aggregated HRs with their corresponding 95% confidence intervals (CIs) to evaluate the correlation between PNI and both overall survival (OS) and progression-free survival (PFS) in lymphoma patients. RESULTS: By analyzing data from 1260 patients in 28 studies, we found that PNI levels were associated with prognosis in lymphoma patients. High PNI levels predicted that patients had longer OS (HR: 0.46, 95% CI 0.37-0.58, P < 0.05) and better PFS (HR: 0.56, 95% CI 0.45-0.70, P < 0.05). Subgroup analyses showed that the predictive ability of PNI for patient prognosis may differ depending on the type of lymphoma. In addition, we found that the critical PNI value had greater predictive potential at 40-45 and above 45. CONCLUSION: Our study suggests a strong association between PNI and prognostic outcomes in lymphoma patients, indicating that PNI holds substantial prognostic value in this population.

Metabolic tumor volume from baseline [18 F]FDG PET/CT at diagnosis improves the IPI stratification in patients with diffuse large B-cell lymphoma.

PURPOSE: Although several different parameters of PET/CT were reported to be predictive of survival in DLBCL, the best parameter remains to be elucidated and whether it could improve the risk stratification of IPI in patients with DLBCL. PROCEDURES: 262 DLBCL patients including in the training and validation cohort were retrospectively analyzed in this study. RESULTS: Among different parameters, MTV was identified as the optimal prognostic parameter with a maximum area under the curve (AUC) of 0.652 ± 0.112 than TLG and SDmax (0.645 ± 0.113 and 0.600 ± 0.117, respectively). Patients with high MTV were associated with inferior PFS (p < 0.001 and p = 0.021, respectively) and OS (p < 0.001 and p < 0.001, respectively) in both the training and validation cohort. The multivariate analysis revealed that high MTV was an unfavorable factor for PFS (relative ratio [RR], 2.295; 95% confidence interval [CI], 1.457-3.615; p < 0.01) and OS (RR, 2.929; 95% CI 1.679-5.109; p < 0.01) independent of IPI. CONCLUSIONS: Further analysis showed MTV could improve the risk stratification of IPI for both PFS and OS (p < 0.01 and p < 0.01, respectively). In conclusion, our study suggests that MTV was an optimal prognostic parameter of PET/CT for survival and it could improve the risk stratification of IPI in DLBCL, which may help to guide treatment in clinical trial.

Natural killer (NK) cells-related gene signature reveals the immune environment heterogeneity in hepatocellular carcinoma based on single cell analysis.

The early diagnosis of liver cancer is crucial for the treatment and depends on the coordinated use of several test procedures. Early diagnosis is crucial for precision therapy in the treatment of the hepatocellular carcinoma (HCC). Therefore, in this study, the NK cell-related gene prediction model was used to provide the basis for precision therapy at the gene level and a novel basis for the treatment of patients with liver cancer. Natural killer (NK) cells have innate abilities to recognize and destroy tumor cells and thus play a crucial function as the "innate counterpart" of cytotoxic T cells. The natural killer (NK) cells is well recognized as a prospective approach for tumor immunotherapy in treating patients with HCC. In this research, we used publicly available databases to collect bioinformatics data of scRNA-seq and RNA-seq from HCC patients. To determine the NK cell-related genes (NKRGs)-based risk profile for HCC, we isolated T and natural killer (NK) cells and subjected them to analysis. Uniform Manifold Approximation and Projection plots were created to show the degree of expression of each marker gene and the distribution of distinct clusters. The connection between the immunotherapy response and the NKRGs-based signature was further analyzed, and the NKRGs-based signature was established. Eventually, a nomogram was developed using the model and clinical features to precisely predict the likelihood of survival. The prognosis of HCC can be accurately predicted using the NKRGs-based prognostic signature, and thorough characterization of the NKRGs signature of HCC may help to interpret the response of HCC to immunotherapy and propose a novel tumor treatment perspective.

Major pathologic response and long-term clinical benefit in hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer after neoadjuvant chemotherapy.

BACKGROUND: The majority of HR+/HER2-breast cancer patients can also achieve long-term survival despite not attaining pCR, indicating limited prognostic value of pCR in this population. This study aimed to identify novel pathologic end points for predicting long-term outcomes in HR+/HER2-breast cancer after neoadjuvant chemotherapy. METHODS: We analyzed HR+/HER2-breast cancer patients with stage II-III tumors who underwent curative surgery after neoadjuvant chemotherapy from three hospitals. Major pathologic response (MPR), defined as the presence of Miller-Payne grades 3-5 and positive lymph node ratio of ≤10 %, was used as a pathological evaluation indicator. We assessed the association between MPR and event-free survival (EFS) and performed Multivariable Cox regression to identify independent factors associated with EFS. RESULTS: From January 2010 to December 2020, 386 patients were included in the final analysis. 28 patients (7.3 %) achieved pCR and 118 patients (30.6 %) achieved MPR. The median duration of follow-up was 54.4 months,5-year EFS was 87 % in the MPR group vs. 68 % in the non-MPR group. Multivariate analysis showed that low PR expression, high clinical stage, lower Miller-Payne grades and Positive lymph node ratio were independent poor prognostic factors for EFS (all P values < 0.05). The prognostic effect of MPR remained in multivariable models (hazard ratio (HR), 0.45; 95 % confidence interval (CI), 0.26-0.76; P = 0.008), In non-pCR patients, those who achieved MPR exhibited a similar EFS compared with pCR patients (HR, 2.25; 95 % CI, 0.51-9.84; P = 0.28). CONCLUSION: MPR may be a novel pathologic end point in HR+/HER2-breast cancer after neoadjuvant chemotherapy, holding greater applicability in the prognosis evaluation than pCR.

Identification of PI3K-AKT Pathway-Related Genes and Construction of Prognostic Prediction Model for ccRCC.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC), the predominate histological type of renal cell carcinoma (RCC), has been extensively studied, with poor prognosis as the stage increases. Research findings consistently indicated that the PI3K-Akt pathway is commonly dysregulated across various cancer types, including ccRCC. Targeting the PI3K-Akt pathway held promise as a potential therapeutic approach for treating ccRCC. Development and validation of PI3K-Akt pathway-related genes related biomarkers can enhance healthcare management of patients with ccRCC. PURPOSE: This study aimed to identify the key genes in the PI3K-Akt pathway associated with the diagnosis and prognosis of CCRCC using data mining from the Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO) datasets. METHODS: The purpose of this study is to use bioinformatics methods to screen data sets and clinicopathological characteristics associated with ccRCC patients. The exhibited significantly differential expressed genes (DEGs) associated with the PI3K-Akt pathway were examined by KEGG. In addition, Kaplan-Meier (KM) analysis used to estimate the survival function of the differential genes by using the UALCAN database and graphPad Prism 9.0. And exploring the association between the expression levels of the selected genes and the survival status and time of patients with ccRCC based on SPSS22.0. Finally, a multigene prognostic model was constructed to assess the prognostic risk of ccRCC patients. RESULTS: A total of 911 genes with common highly expressed were selected based on the GEO and TCGA databases. According to the KEGG pathway analysis, there were 42 genes enriched in PI3K-Akt signalling pathway. And seven of highly expressed genes were linked to a poor prognosis in ccRCC. And a multigene prognostic model was established based on IL2RG, EFNA3, and MTCP1 synergistic expression might be utilized to predict the survival of ccRCC patients. CONCLUSIONS: Three PI3K-Akt pathway-related genes may be helpful to identify the prognosis and molecular characteristics of ccRCC patients and to improve therapeutic regimens, and these risk characteristics might be further applied in the clinic.

Diagnostic Challenges and Treatment Options for Mucocle of the Appendix: A Comprehensive Review.

Mucocles of the appendix, encompassing mucinous cystadenomas and mucinous cystadenocarcinomas, represent rare but clinically significant appendiceal lesions characterized by the accumulation of mucin within the appendix lumen. This review explores the diagnostic complexities and treatment strategies associated with mucocles, emphasizing the importance of its accurate recognition and management. Diagnostic challenges arise due to overlapping symptoms with acute appendicitis and other appendiceal pathologies, necessitating a multidimensional approach that includes imaging, histopathological analysis, and clinical correlation. Treatment options range from appendectomy for benign lesions to more extensive surgical procedures, such as right hemicolectomy for malignant forms. Prognostic factors, including histological subtype and tumor size, influence treatment decisions and long-term outcomes. By synthesizing current evidence and clinical insights, this review aims to provide a comprehensive framework for clinicians to navigate the complexities of mucocles of the appendix, offering perspectives that can guide effective management and future research endeavors.

Prognostic nomogram for patients with advanced unresectable hepatocellular carcinoma treated with TAE combined with HAIC.

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and often arises in the context of chronic liver disease, such as hepatitis B or C infection, and cirrhosis. Advanced unresectable HCC (uHCC) presents significant treatment challenges due to its advanced stage and inoperability. One efficient treatment method for advanced uHCC is the use of hepatic arterial infusion chemotherapy (HAIC) combined with transcatheter arterial embolization (TAE). Patients and Methods: In this study, we conducted a retrospective collection of clinical data, including basic information, radiological data, and blood test parameters, for patients with advanced uHCC who underwent TAE + HAIC treatment from August 2020 to February 2023. A total of 743 cases involving 262 patients were included. Ultimately, the covariates included in the analysis were the Child-Pugh score, extrahepatic metastasis, tumor number, tumor size, and treatment method. Results: In the study, we performed univariable and multivariable analysis on 23 clinical factors that were screened by LASSO regression, indicating that the five variables aforementionedly were identified as independent factors influencing patient prognosis. Then we developed a nomogram of the sensitive model and calculated concordance indices of prognostic survival models. Conclusion: Based on the uHCC patient cohort, we have developed a prognostic model for OS in patients who received TAE + HAIC treatment. This model can accurately predict OS and has the potential to assist in personalized clinical decision-making.

An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies.

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

Epidermal growth factor receptor-mutated lung carcinomas with insufficient response to epidermal growth factor receptor inhibitors.

Administration of single-agent epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is a standard treatment option for metastatic non-small cell lung carcinomas with EGFR exon 19 deletions (ex19del) and L858R substitutions. However, there is a significant interpatient heterogeneity with regard to the degree of the response and its duration. Patients with EGFR ex19del mutation, TP53 wild-type, good performance status, low tumor burden and no circulating tumor DNA (ctDNA) at baseline have the best chances to derive pronounced benefit from TKI therapy. In contrast, subjects with EGFR L858R substitution, mutated TP53, poor overall condition, high tumor volume and detectable ctDNA are generally poor responders to EGFR inhibitors. ctDNA dynamics in the first days or weeks of treatment allows reliable identification of patients, who are very unlikely to derive clinically meaningful benefit from single-agent TKIs. These patients are candidates for clinical trials, which may involve the addition of chemotherapy and antiangiogenic drugs to patients, who failed to achieve immediate benefit from TKI monotherapy.

[Box: see text].

Predicting adjuvant radiation therapy benefit in cutaneous squamous cell carcinoma with the 40-gene expression profile.

Aim: To independently confirm that the 40-gene expression profile (40-GEP) test can identify patients with high-risk cutaneous squamous cell carcinoma who are more or less likely to benefit from adjuvant radiation therapy (ART).Materials & methods: Primary cutaneous squamous cell carcinoma tumors from two academic centers received retrospective 40-GEP testing and were analyzed for 5-year metastasis-free survival and projected time to event.Results: Random sampling of matched patient pairs (n = 52 ART-treated; 371 no ART) showed a median 50% decrease in 5-year progression rate for ART-treated patients (vs no ART) with 40-GEP Class 2B. Class 2A was associated with a modest ART benefit, but not Class 1.Conclusion: The 40-GEP identified patients most likely to benefit from ART (Class 2B) and those that can consider deferring treatment (Class 1).

Independent validation study: 40-GEP identifies patients with cutaneous squamous cell carcinoma who would be most likely to benefit from adjuvant radiation therapy.

Integrating Lysosomal Genes and Immune Infiltration for Multiple Myeloma Subtyping and Prognostic Stratification.

Lysosomes are crucial in the tumour immune microenvironment, which is essential for the survival and homeostasis in multiple myeloma (MM). Here, we aimed to identify lysosome-related genes for the prognosis of MM and predicted their regulatory mechanisms. Gene expression profiles of MM from the GSE2658 and GSE57317 datasets were analysed. Lysosome-related differentially expressed genes (DEGs) were identified and used for molecular subtyping of MM patients. A prognostic model was constructed using univariate Cox regression and LASSO regression analyses. The relationship between prognostic genes, immune cell types, and autophagy pathways was assessed through correlation analysis. RT-qPCR was performed to validate the expression of prognostic genes in MM cells. A total of 9,954 DEGs were identified between high and low immune score groups, with 213 intersecting with lysosomal genes. Molecular subtyping revealed two distinct MM subtypes with significant differences in immune cell types and autophagy pathway activities. Five lysosome-related DEGs (CORO1A, ELANE, PSAP, RNASE2, and SNAPIN) were identified as significant prognostic markers. The prognostic model showed moderate predictive accuracy with AUC values up to 0.723. Prognostic genes demonstrated significant correlations with various immune cell types and autophagy pathways. Additionally, CORO1A, PSAP and RNASE2 expression was up-regulated in MM cells, while ELANE and SNAPIN were down-regulated. Five lysosomal genes in MM were identified, and a new risk model for prognosis was developed using these genes. This research could lead to discovering important gene markers for the treatment and prognosis of MM.

Prognostic factors for low- and high grade squamous intraepithelial lesions in histological preparations following LLETZ procedure.

OBJECTIVE: Aim: To investigate the influence of the following prognostic factors: age, parity, hormonal status (premenopausal, postmenopausal), histological result from targeted biopsy (LSIL, HSIL), adequacy of colposcopic examination (satisfactory, unsatisfactory colposcopy), type of TZ (type 1, 2, 3), type of cervical lesions (type 1, 2, 3), the colposcopic impression (diagnosis) of the cervical lesion (LSIL, HSIL/Ca colli uteri in situ), lesion size (up to 1/3; up to 2/3; more than 2/3 of the cervical circumference) for the occurrence of LSIL and HSIL/Ca colli uteri in situ in the final histological result after LLETZ procedure. PATIENTS AND METHODS: Materials and Methods: This is a prospective study (01.01.2017 - 31.07. 2021) including 189 patients with cervical precancerous lesions received LLETZ treatment One gynaecologic oncologist performed video colposcopy, targeted biopsy, and LLETZ. One histopathologist diagnosed histological specimens from the biopsy and LLETZ procedure. RESULTS: Results: We found a statistically significant correlation between the histological result of the targeted biopsy factor and the colposcopic diagnosis factor concerning the final histological result of LLETZ. The cervical lesion size factor and cervical lesion type factor have prognostic significance for the histological outcome following LLETZ. CONCLUSION: Conclusions: The histological result of targeted biopsy and colposcopic diagnosis are significant factors for the final histological result after LLETZ. Cervical lesion invasion into the endocervical canal is a prognostic factor for HSIL, and its invisible borders - for carcinoma (in situ or microinvasive/invasive). Lesion size up to 1/3 of the cervix is a prognostic factor for LSIL and large lesions (2/3 of the cervix) - for HSIL and cervical cancer (in situ, microinvasive/invasive).

Claudin-1 in Head and Neck Squamous Cell Carcinoma.

INTRODUCTION: The objective response rate to immunotherapy is limited in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) patients, whose prognosis is still dismal. Few prognostic factors are clinically available, mostly related to patient or disease characteristics. Gene expression signatures offer better prognostic abilities but are mainly used in research. One such GE model classifies HNSCC into 6 clusters with different prognoses. Claudin-1 (CLDN1), which influences tumor microenvironment and immune cell infiltration, has emerged as a potential target, especially in cancers like HNSCC with high CLDN1 expression. METHODS: A single-center cohort of 100 loco-regionally advanced HNSCC patients from the BD2Decide observational study was analyzed. Patients were selected to balance long-term survivors and deceased patients, including HPV-negative and HPV-positive cases. Primary tumor specimens underwent GE analysis using Affymetrix ClariomD chips. Primary endpoint was overall survival (OS). RESULTS: The cohort comprised 100 HNSCC patients with a median age of 60 years, predominantly men (76%). Median OS and disease-free survival (DFS) were 94.24 and 42.79 months, respectively. CLDN1 expression varied significantly among primary sites, being highest in hypopharynx cancers. Differences in expression were not significant when stratified by HPV status or clinical stage. CLDN1 expression differed across the 6 transcriptomic clusters, with the highest levels in clusters associated with mesenchymal and hypoxic features. Higher CLDN1 expression correlated with shorter OS (hazard ratio [HR]: 3, p = 0.0023) and DFS (HR: 2.14, p = 0.02). CONCLUSION: CLDN1 expression is heterogeneous in HNSCC and carries prognostic significance. It is highest in tumors with HPV-like biology and hypoxic environments, and lowest in immune-sensitive clusters. High CLDN1 is a negative prognostic factor and a promising therapeutic target. Anti-CLDN1 treatments could improve outcomes of CLDN1+ HNSCC patients, and combination therapies with ICIs might overcome resistance in CLDN1- cases. These findings support the need for clinical studies on anti-CLDN1 therapies.

Machine-learning-based prediction by stacking ensemble strategy for surgical outcomes in patients with degenerative cervical myelopathy.

BACKGROUND: Machine learning (ML) is extensively employed for forecasting the outcome of various illnesses. The objective of the study was to develop ML based classifiers using a stacking ensemble strategy to predict the Japanese Orthopedic Association (JOA) recovery rate for patients with degenerative cervical myelopathy (DCM). METHODS: A total of 672 patients with DCM were included in the study and labeled with JOA recovery rate by 1-year follow-up. All data were collected during 2012-2023 and were randomly divided into training and testing (8:2) sub-datasets. A total of 91 initial ML classifiers were developed, and the top 3 initial classifiers with the best performance were further stacked into an ensemble classifier with a supported vector machine (SVM) classifier. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicted outcome was the JOA recovery rate. RESULTS: By applying an ensemble learning strategy (e.g., stacking), the accuracy of the ML classifier improved following combining three widely used ML models (e.g., RFE-SVM, EmbeddingLR-LR, and RFE-AdaBoost). Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top 3 initial classifiers varied a lot in predicting JOA recovery rate in DCM patients. CONCLUSIONS: The ensemble classifiers successfully predict the JOA recovery rate in DCM patients, which showed a high potential for assisting physicians in managing DCM patients and making full use of medical resources.