B-cell-specific signatures reveal novel immunophenotyping and therapeutic targets for hepatocellular carcinoma.

BACKGROUND: Immunotherapy presents both promises and challenges in treating hepatocellular carcinoma (HCC) due to its complex immunological microenvironment. The role of B cells, a key part of the immune system, remains uncertain in HCC. AIM: To identify B-cell-specific signatures and reveal novel immunophenotyping and therapeutic targets for HCC. METHODS: Using the Tumor Immune Single-cell Hub 2 database, we identified B-cell-related genes (BRGs) in HCC. Gene enrichment analysis was performed to explore the possible collaboration between B cells and T cells in HCC. We conducted univariate Cox regression analysis using The Cancer Genome Atlas liver HCC collection dataset to find BRGs linked to HCC prognosis. Subsequently, least absolute shrinkage and selection operator regression was utilized to develop a prognostic model with 11 BRGs. The model was validated using the International Cancer Genome Consortium dataset and GSE76427. RESULTS: The risk score derived from the prognostic model emerged as an independent prognostic factor for HCC. Analysis of the immune microenvironment and cell infiltration revealed the immune status of various risk groups, supporting the cooperation of B and T cells in suppressing HCC. The BRGs model identified new molecular subtypes of HCC, each with distinct immune characteristics. Drug sensitivity analysis identified targeted drugs effective for each HCC subtype, enabling precision therapy and guiding clinical decisions. CONCLUSION: We clarified the role of B cells in HCC and propose that the BRGs model offers promising targets for personalized immunotherapy.

Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer.

Purpose: Accurate preoperative identification of Human epidermal growth factor receptor 2 (HER2) low expression breast cancer (BC) is critical for clinical decision-making. Our aim was to use machine learning methods to develop and validate an ultrasound-based radiomics nomogram for predicting HER2-low expression in BC. Methods: In this retrospective study, 222 patients (108 HER2-0 expression and 114 HER2-low expression) with BC were included. The enrolled patients were randomly divided into a training cohort and a test cohort with a ratio of 8:2. The tumor region of interest was manually delineated from ultrasound image, and radiomics features were subsequently extracted. The features underwent dimension reduction using the least absolute shrinkage and selection operator (LASSO) algorithm, and rad-score were calculated. Five machine learning algorithms were applied for training, and the algorithm demonstrating the best performance was selected to construct a radiomics (USR) model. Clinical risk factors were integrated with rad-score to construct the prediction model, and a nomogram was plotted. The performance of the nomogram was assessed using receiver operating characteristic curve and decision curve analysis. Results: A total of 480 radiomics features were extracted, out of which 11 were screened out. The majority of the extracted features were wavelet features. Subsequently, the USR model was established, and rad-scores were computed. The nomogram, incorporating rad-score, tumor shape, border, and microcalcification, achieved the best performance in both the training cohort (AUC 0.89; 95%CI 0.836-0.936) and the test cohort (AUC 0.84; 95%CI 0.722-0.958), outperforming both the USR model and clinical model. The calibration curves showed satisfactory consistency, and DCA confirmed the clinical utility of the nomogram. Conclusion: The nomogram model based on ultrasound radiomics exhibited high prediction value for HER2-low BC.

Breast cancer statistics 2024.

This is the American Cancer Society's biennial update of statistics on breast cancer among women based on high-quality incidence and mortality data from the National Cancer Institute and the Centers for Disease Control and Prevention. Breast cancer incidence continued an upward trend, rising by 1% annually during 2012-2021, largely confined to localized-stage and hormone receptor-positive disease. A steeper increase in women younger than 50 years (1.4% annually) versus 50 years and older (0.7%) overall was only significant among White women. Asian American/Pacific Islander women had the fastest increase in both age groups (2.7% and 2.5% per year, respectively); consequently, young Asian American/Pacific Islander women had the second lowest rate in 2000 (57.4 per 100,000) but the highest rate in 2021 (86.3 per 100,000) alongside White women (86.4 per 100,000), surpassing Black women (81.5 per 100,000). In contrast, the overall breast cancer death rate continuously declined during 1989-2022 by 44% overall, translating to 517,900 fewer breast cancer deaths during this time. However, not all women have experienced this progress; mortality remained unchanged since 1990 in American Indian/Alaska Native women, and Black women have 38% higher mortality than White women despite 5% lower incidence. Although the Black-White disparity partly reflects more triple-negative cancers, Black women have the lowest survival for every breast cancer subtype and stage except localized disease, with which they are 10% less likely to be diagnosed than White women (58% vs. 68%), highlighting disadvantages in social determinants of health. Progress against breast cancer could be accelerated by mitigating racial, ethnic, and social disparities through improved clinical trial representation and access to high-quality screening and treatment.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Breast cancer survival analysis in the Republic of Mauritius by age, stage at diagnosis and molecular subtype: A retrospective cohort study.

Breast cancer is by far the leading cancer both in terms of incidence and mortality in the Republic of Mauritius, a Small Island Developing State (SIDS). However, few studies assessed its survival by age, stage at diagnosis and molecular subtype. We identified 1399 breast cancer cases newly diagnosed between 2017 and 2020 at the Central Health Laboratory, Victoria Hospital. Cancers were categorized into five molecular subtypes: (1) luminal A, (2) luminal B Her2 negative, (3) luminal B Her2 positive, (4) Her2 enriched and (5) Triple negative. The net 1 and 3-year survival were estimated for different age groups, staging at time of diagnosis and molecular subtype. We also estimated the excess hazards using a multivariate Cox proportional hazards model. While early stage at diagnosis (stage 1 [44.4%] and stage 2 [20.1%]) were most common compared to late presentation (Stage 3 [25.4%] and stage 4 [10.1%]), luminal B Her2 negative (36.7%) was the most frequent molecular subtype. The net 1- and 3-year breast cancer survival rates were 93.9% (92.3-95.4) and 83.4% (80.4-86.4), respectively. Breast cancer three-year survival rates were poorest among the youngest patients (<50 years), 77.1% (70.7-83.5), those diagnosed with stage 4 (28.5% [17.1-39.9]) and cancer with a triple negative molecular subtype (71.3% [63.3-79.3]). Emphasis on a national breast cancer screening programme, down staging breast cancer at diagnosis and systematic molecular subtyping of all breast tissues could be pivotal in improving breast cancer survival outcomes in the Republic of Mauritius.

Multi-omics and Single Cell Sequencing Analyses Reveal Associations of Mitophagy-Related Genes Predicting Clinical Prognosis and Immune Infiltration Characteristics in Osteosarcoma.

Despite recent advances in clinical treatments, identifying high-risk osteosarcoma (OS) patients remains an unresolved clinical challenge. Mitophagy, a specialized form of cellular autophagy, selectively reduces the number of mitochondria or repairs their abnormal functions in response to external stress, thereby ensuring mitochondrial quality and maintaining mitochondrial function. Mitophagy plays a crucial role in cancer development, including processes such as mitochondrial repair, homeostasis maintenance, and tumor metabolism. However, its impact on OS has not yet been reported. In this study, we collected 58 mitophagy-related genes (MPRGs) from the TARGET and GEO databases and bioinformatically screened for those associated with OS prognosis. By LASSO-multivariable Cox regression algorithm, we subsequently developed a novel scoring system, the MPRG score, and validated its significance in predicting OS prognosis. Immune landscape analysis showed patients in the low MPRG group had a higher immune infiltration level than those in the high MPRG group. Drug sensitivity differences highlighted the potential need for alternative therapeutic strategies based on MPRG scoring system. The distribution characteristics of the MPRG signature in different cell subtypes of OS were explored by single-cell sequencing analyses. In vitro experiments further confirmed the abnormal expression of screened targets in OS. Our findings highlight the role of mitophagy in OS and its potential as a therapeutic target.

The prognostic role of circulating tumor DNA across breast cancer molecular subtypes: A systematic review and meta-analysis.

Objective: Circulating tumor DNA (ctDNA) is increasingly being used as a potential prognostic biomarker in cancer patients. We aimed to assess the prognostic value of ctDNA in different subtypes of breast cancer patients throughout the whole treatment cycle. Materials and methods: PubMed, Web of Science, Embase, Cochrane Library, Scopus, and clinical trials.gov databases were searched from January 2016 to May 2022. The following search terms were used: ctDNA OR circulating tumor DNA AND breast cancer OR breast carcinoma. Only studies written in English were included. The following pre-specified criteria should be met for inclusion: (i) original articles, conference abstracts, etc.; (ii) patients with breast cancer; (iii) ctDNA measurement; and (iv) clinical outcome data such as recurrence-free survival (RFS) and overall survival (OS). The random-effects model was preferred considering the potential heterogeneity across studies. The main outcomes are ctDNA detection rate and postoperative long-term outcomes (RFS and OS). Results: A total of 24 studies were screened. At every measurement time, the ctDNA detection rate of the HR+ subgroup was similar to that of the HR- subgroup (P = 0.075; P = 0.458; P = 0.744; and P = 0.578), and the ctDNA detection rate of the HER2+ subgroup was similar to that of the HER2- subgroup (P = 0.805; P = 0.271; P = 0.807; and P = 0.703). In the HR+ subgroup, RFS and OS of ctDNA positive patients were similar to those of ctDNA negative patients (P = 0.589 and P = 0.110), while RFS and OS of the ctDNA positive group was significantly shorter than those of the ctDNA negative patients in the HR- subgroup (HR = 4.03, P < 0.001; HR = 3.21, P < 0.001). According to HER grouping, the results were the same as above. In the triple negative breast cancer (TNBC) subgroup, the RFS and OS of ctDNA-positive patients was significantly shorter than of the ctDNA negative patients before and after surgery. Conclusions: ctDNA was more predictive of recurrence-free survival and overall survival in the HR- subgroup than in the HR+ subgroup, and the same result was showed in the HER2- subgroup vs. HER2+ subgroup. The prognosis of the TNBC subtype is closely related to ctDNA before and after surgery.

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis.

Background: High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment. Methods: Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments. Results: We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells. Conclusion: The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

The Landscape of Breast Cancer Molecular and Histologic Subtypes in Canada.

Purpose: To characterize the histologic and molecular subtype distribution of, and survival from, breast cancer (BC) among Canadian women overall, and by stage and age at diagnosis. Methods: Invasive BC cases from the Canadian Cancer Registry for women aged 15-99 years between 2012 and 2017 in Canada, excluding Quebec, were examined using pre-existing mortality linkages. Stage at diagnosis, molecular, and histologic subtypes, and 5-year net survival (NS) by age, subtype, and stage were determined. Results: 107,271 women with BC were included. Luminal A was the most common subtype, present in increasing proportions as women aged, up to a maximum of 55% of cases in 70-74. Ductal and luminal A were most likely to be diagnosed at stage I, while HER2+ had the highest proportion of diagnosis at stage III; triple negative (TN) and unknown had the highest proportion of stage IV. For all stages combined, luminal A had a five-year NS of 98%, while TN was 74%. NS for stage I BC was 99-100% for all subtypes, excepting TN, which was 96%. Survival decreased with advancing stage, most markedly for TN, for which stage III was 47% and stage IV 7%. Survival by equivalent stage and subtype was comparable across age groups but declined in older age categories. Conclusions: The varying natural histories of BC subtypes and histologies can inform prognoses, health system economics, and screening practices. The NS of 96% or greater for stage I, regardless of subtype, highlights the importance of early detection for all subtypes of BC, especially in aggressive subtypes.

Clinical characteristics and genomic profiling of outpatients with endometrial cancer at a Chinese tertiary cancer center.

OBJECTIVE: Endometrial cancer is stepping into the era of precision therapy. Genomic test is recommended for newly diagnostic patients. However, outpatients displayed more complex characteristics. Here, we elucidated the clinical characteristics and genomic profiling of outpatients with endometrial cancer at our institution. METHODS: Between 2018 and 2023, 68 endometrial cancer received genomic tests at outpatient department of Fudan University Shanghai Cancer Center. Data, including age, pathological histology, FIGO stage and treatment strategy were collected. Germline mutations, molecular subtypes and other somatic mutations were also summarized. RESULTS: Overall, 72.1% (49/68) of patients receive genomic tests at primary diagnosis, while 27.9% (19/68) of patients received tests at recurrence. Nine patients had deleterious germline mutations, including BRCA1(2), MLH1(1), MSH2(2, including one with co-mutation of RAD50), MSH6(2), FANCA(1), MUTYH(1). Molecular subtypes were recognized among 62 patients, as POLE super-mutation(4, 6.5%), MSI-H(7, 11.3%), CN-Low(36, 58.1%) and CN-High(15, 24.2%). Ten patients received anti-PD1 monotherapy or in combination with chemotherapy or anti-angiogenic therapy, with the duration of disease control of 1 to 35 months. The ORR rate was 30%, and six patients had stable disease. The median (range) follow-up time was 18(2-160) months. 23(33.8%) relapses were recorded, and CN-High subtype displayed worst PFS compared with other subtypes (P < 0.01). 6 deaths were reported including 2(5.6%) of CN-Low and 4(26.7%) of CN-High. CONCLUSION: Outpatients department gathered a considerable proportion of recurrent patients with complex genomic features. Patients with worse prognosis could be well studied, and anti-PD1 therapy was a promising salvage therapy in the real world.

Combination of bulk RNA and single-cell sequencing unveils PANoptosis-related immunological ecology hallmarks and classification for clinical decision-making in hepatocellular carcinoma.

PANoptosis is engaged in the program of immune response and carcinogenicity. Nonetheless, the actual impacts of PANoptosis on clinical management and oncology immunity in hepatocellular carcinoma (HCC) are not fully grasped. RNA-seq-derived computations were conducted to sort out the molecular subtypes and elucidate the disparities based on PANoptosis molecules. Single-cell sequencing (scRNA-seq) tools including Cytotrace and Addmodulescore were extracted to characterize diversification potency and quantify the PANoptosis motion. Transcriptional factors were inferred by the pySCENIC package and Cellchat program scrutinized the intercellular exchange across cell compartments. The PANoptosis score system originated by incorporating 10 machine learning algorithms and 101 compositions to project clinical results and deteriorate tendencies. Circulatory PANoptosis-associated protein HSP90AA1 was determined by enzyme-linked immunosorbent assay (ELISA). HCC individuals could be categorized into low- and high-PANoptosis groups with diverse biogenic and pharmacotherapy heterogeneity. Individuals in the elevated PANoptosis subtype were characterized as "hot tumor" conveying the increased presence of immunogenicity while reiterating an explicit negative connection with tumor stemness. Compared to immune and stromal cells, cancerous cells showcased decreased PANoptosis and heightened PANoptosis malignant cell subgroups might be tied to a substantial level of genomic expression of SREBF2, JUND, GATAD1, ZBTB20, SMAD5 and implied a more aggressive potential. The PANoptosis index, derived from machine learning, has been established to provide succinct frameworks for predicting outcomes and clarified the noteworthy utility of conventional regimens, as the differentiated power of HCC occurred together with vascular invasion and hepatocellular adenoma (HCA). The experiment confirmed that the circulating HSP90AA1 was aberrantly augmented in HCC patients, thus demonstrating its potential as a discriminatory biomarker. We systematically deciphered the molecular and immune ecosystem traits of PANoptosis in bulk and scRNA-seq degrees, which may deliver advantageous insights for customized treatment, awareness of the pathological process and prognosis scrutiny for HCC patients.

Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis.

Background: Postmenopausal osteoporosis (PMOP) represents as a significant health concern, particularly as the population ages. Currently, there is a paucity of comprehensive descriptions regarding the immunoregulatory mechanisms and early diagnostic biomarkers associated with PMOP. This study aims to examine immune-related differentially expressed genes (IR-DEGs) in the peripheral blood mononuclear cells of PMOP patients to identify immunological patterns and diagnostic biomarkers. Methods: The GSE56815 dataset from the Gene Expression Omnibus (GEO) database was used as the training group, while the GSE2208 dataset served as the validation group. Initially, differential expression analysis was conducted after data integration to identify IR-DEGs in the peripheral blood mononuclear cells of PMOP. Subsequently, feature selection of these IR-DEGs was performed using RF, SVM-RFE, and LASSO regression models. Additionally, the expression of IR-DEGs in distinct bone marrow cell subtypes was analyzed using single-cell RNA sequencing (scRNA-seq) datasets, allowing the identification of cellular communication patterns within various cell subgroups. Finally, molecular subtypes and diagnostic models for PMOP were constructed based on these selected IR-DEGs. Furthermore, the expression levels of characteristic IR-DEGs were examined in rat osteoporosis (OP) models. Results: Using machine learning, six IR-DEGs (JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5) were identified. Subsequently, two molecular subtypes of PMOP (subtype 1 and subtype 2) were established, with subtype 1 exhibiting a higher proportion of M1 macrophage infiltration. Analysis of the scRNA-seq dataset revealed 11 distinct cell clusters. It was noted that JUN was significantly overexpressed in M1 macrophages, while HMOX1 showed a marked elevation in endothelial cells and M2 macrophages. Cell communication results suggested that the PMOP microenvironment features increased interactions among M2 macrophages, CD8+ T cells, Tregs, and fibroblasts. The diagnostic model based on these six IR-DEGs demonstrated excellent diagnostic performance (AUC = 0.927). In the OP rat model, the expression of IL1R2 and TNFSF8 were significantly elevated. Conclusion: JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5 may serve as promising molecular targets for diagnosing and subtyping patients with PMOP. These results offer novel perspectives on the early diagnosis of PMOP and the advancement of personalized immune-based therapies.

Risk of locoregional recurrence after breast cancer surgery by molecular subtype-a systematic review and network meta-analysis.

BACKGROUND: The prevention of locoregional recurrence (LRR) is crucial in breast cancer, as it translates directly into reduced breast cancer-related death. Breast cancer is subclassified into distinct intrinsic biological subtypes with varying clinical outcomes. AIMS: To perform a systematic review and network meta-analysis (NMA) to determine the rate of LRR by breast cancer molecular subtype. METHODS: A NMA was performed as per PRISMA-NMA guidelines. Molecular subtypes were classified by St Gallen expert consensus statement (2013). Analysis was performed using R and Shiny. RESULTS: Five studies were included including 6731 patients whose molecular subtypes were available. Overall, 47.3% (3182/6731) were Luminal A (LABC: estrogen receptor (ER) + /human epidermal growth factor receptor-2 (HER2) - /progesterone receptor (PR) + or Ki-67 < 20%), 25.5% (1719/6731) were Luminal B (LBBC: ER + /HER2 - /PR - or Ki-67 ≥ 20%), 11.2% (753/6731) were Luminal B-HER2 + (LBBC-HER2: ER + /HER2 +), 6.9% (466/6731) were HER2 + (HER2 ER - /HER2 +), and finally 9.1% (611/6731) were triple-negative breast cancer (TNBC: ER - /HER2 -). The median follow-up was 74.0 months and the overall LRR rate was 4.0% (271/6731). The LRR was 1.7% for LABC (55/3182), 5.1% for LBBC (88/1719), 6.0% for LBBC-HER2 (45/753), 6.0% for HER2 (28/466), and 7.9% for TNBC (48/611). At NMA, patients with TNBC (odds ratio (OR) 3.73, 95% confidence interval (CI) 1.80-7.74), HER2 (OR 3.24, 95% CI 1.50-6.99), LBBC-HER2 (OR 2.38, 95% CI 1.09-5.20), and LBBC (OR 2.20, 95% CI 1.07-4.50) were significantly more likely to develop LRR compared to LABC. CONCLUSION: TNBC and HER2 subtypes are associated with the highest risk of LRR. Multidisciplinary team discussions should consider these findings to optimize locoregional control following breast cancer surgery.

[Molecular Subtype of Small Cell Lung Cancer: 
Challenge for Transforming into Clinical Practice].

Small cell lung cancer (SCLC), one of the histological subtypes of lung cancer, is characterized by high proliferation, early metastasis, susceptibility to drug resistance and recurrence. For several years, SCLC has always been regarded as a homogeneous disease, treated with a unified radiotherapy and chemotherapy strategy. Despite significant early therapeutic effects, drug resistance and recurrence occur quickly, and there is a lack of satisfactory treatment results, which may be due to insufficient understanding of the tumor heterogeneity of SCLC at present. Recently, the concept of SCLC molecular subtype based on the definition of relatively high expression of lineage transcription factors has been proposed in preclinical studies. This article mainly elaborates on the current status and latest findings of SCLC molecular subtype, emphasizing the potential problems that molecular typing may encounter in clinical practice, aiming to promote understanding of the research progress of molecular subtype in SCLC.
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[Advances of Treatment of Pulmonary Large Cell Neuroendocrine Carcinoma].

Large cell neuroendocrine carcinoma (LCNEC) of lung is a rare neuroendocrine carcinoma subtype with difficulty in early diagnosis and poor prognosis which is treated with standard strategies of small cell lung cancer and non-small cell lung cancer. In recent years, the precise types of LCNEC and its response to therapy have been identified by next-generation sequencing. Some researches have also found the correlation between different subtypes of LCNEC and the efficacy of chemotherapy regimens. However, there is no consensual agreement of its therapy. Recently, immune checkpoint inhibitors (ICIs) has provided a new option for LCNEC patients based on some retrospective research data and case reports. In this review, we aimed to summarize the epidemiological characteristics, standard therapy, the advances of molecular subtypes and clinical applications of ICIs of LCNEC, so as to provide optimal systemic clinical decision-making for LCNEC patients.
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Basal/squamous and Mixed subtype bladder cancers present poor outcomes after neoadjuvant chemotherapy in the VESPER trial.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is the standard treatment for muscle-invasive bladder cancer (MIBC), yet 40% of patients progress, emphasizing the need for biomarkers predictive for response or chemoresistance. Gene expression-based subtypes may serve as biomarkers, though which subtypes will respond, notably when it comes to the basal subtype, remains contentious. PATIENTS AND METHODS: This post-hoc study analyzed 300 NAC-treated patients enrolled in the GETUG/AFU VESPER trial, with transurethral diagnostic FFPE which underwent pathological review prior to being sequenced. "Mixed" subtype was defined for tumors displaying at least two different Consensus molecular subtypes in separate regions. We evaluated the association between molecular subtypes and outcome after NAC. Tumours with remaining tissue at cystectomy (n=83) were compared with pre-treatment tumours. RESULTS: Cases were classified Basal/squamous (Ba/Sq) (n=84), Luminal Unstable (n=57), Stroma-rich (n=53), Mixed (n=48), Luminal Papillary (n=39), Luminal Non-Specific (n=18) and Neuroendocrine-like (n=1), with 30/48 Mixed cases including a Ba/Sq component. Compared with other molecular subtypes in a multivariate Cox model, Ba/Sq (Pure or Mixed) patients had an increased hazard ratio of progression free survival (HR:2.0, 95% CI 1.36-3.0). Mixed tumors were associated with decreased metabolic activity that could account for chemoresistance. Ba/Sq and Mixed non-responders mostly maintained their subtype at cystectomy and have fewer myeloid dendritic cells after NAC. Tumors classified luminal papillary at TURBT exhibited an increase of T CD4+ and macrophage signatures after NAC. Other subtypes did not show significant immune changes after NAC. Our study design relied on detailed pathological review, which precluded evaluating the Mixed subtype in published datasets. Furthermore, the sample size for post-NAC analyses constrained the statistical power of these findings. CONCLUSIONS: Our findings underscore the importance of recognizing intra-tumor heterogeneity in MIBC, its role in chemoresistance associated with Ba/Sq subtype and provide valuable insights that could help future treatment development and improve patient outcomes.

Molecular profiling of risk factors for relapse in Japanese patients with stage II colorectal cancer: a retrospective cohort study.

BACKGROUND: The association between the molecular profiles and prognosis of Stage II colorectal cancer remains unclear. This study aimed to examine the risk factors for relapse of Stage II colorectal cancer using molecular profiling. METHODS: We retrospectively enrolled patients with pStage II colorectal cancer who did not receive perioperative adjuvant therapy and whose surgically resected specimens were evaluated using gene expression and whole-exome analyses between January 2014 and December 2018. We evaluated the long-term outcomes and examined the risk factors for relapse-free survival. RESULTS: We evaluated 322 patients with pStage II colorectal cancer, including 126 (39.1%) with right colon cancer. Eighty-seven patients (27.0%) had pT4 tumor, 175 (54.3%) had positive venous invasion, 120 (37.3%) had positive lymphatic invasion, and 68 (21.1%) had perineural invasion. The presence of mutations in key genes for colorectal cancer development based on whole-exome analyses was as follows: APC, 245 (76.1%); TP53, 208 (64.6%); and KRAS, 134 (41.6%). According to the consensus molecular subtype classification based on gene expression, 76 patients (23.6%) had consensus molecular subtype 4 and a significantly lower relapse-free survival than the other patients (5-year relapse-free survival: 83.8% vs. 92.9%, p = 0.017). Perineural invasion (hazard ratio: 5.316, p < 0.001) and consensus molecular subtype 4 (hazard ratio: 2.399, p = 0.020) were identified as independent risk factors for relapse-free survival. CONCLUSIONS: Molecular profiling of Stage II colorectal cancer to assess the risk factors for relapse may contribute to the indication and drug selection for adjuvant chemotherapy.

Diabetes mellitus complications associated with recurrence of stage I endometrioid endometrial cancer: A single-center retrospective study.

OBJECTIVE: Identifying clinical features that are associated with recurrence of endometrioid endometrial carcinoma (EEC) in patients with diabetes mellitus (DM). METHODS: A single-center retrospective cohort study was performed on patients with a diagnosis of both DM and Stage I EEC. Clinical and pathologic features were analyzed in relation to 5-year progression free survival (PFS). Kaplan-Meier Curves and Cox proportional hazard ratios were utilized to assess effect on 5-year PFS. RESULTS: A total of 539 patients were included, with biopsy proven recurrence in 86 (18 %), and 456 (82 %) with no evidence of recurrence. Age, BMI, HgbA1c, metformin use, number of antihyperglycemic medications, use of adjuvant radiation, and surgical approach were not associated with differences in PFS. Presence of end-organ complications associated with diabetes was correlated with worse PFS (HR 1.78, 95 % CI 1.1-2.9, P = 0.02), and specifically diabetic neuropathy was associated with higher rates of recurrence (HR 3.6, 95 % CI 2.1-6.2, P < 0.01). In this cohort, PFS was independently associated with extent of myoinvasion (HR 2.33, 95 % CI 1.4-3.7, P < 0.01) as well as both microsatellite instability (HR 3.43, 95 % CI 1.8-6.6, P < 0.01), and no specific molecular profile (HR 0.3, 95 % CI 0.2-0.6, P < 0.01) molecular subtypes. CONCLUSIONS: In patients with DM and EEC, extent of myoinvasion and TCGA molecular subtype correlated with worse PFS. Control of DM as evidenced by HgbA1c, BMI, and use of antihyperglycemic medications did not correlate with PFS in our cohort of patients with Stage I EEC, while the presence of diabetic neuropathy was associated with a higher risk of recurrence. These results highlight importance of evaluating diabetes severity and molecular subtype in endometrial cancer patients.

Glutamine metabolism-related genes predict the prognostic risk of acute myeloid leukemia and stratify patients by subtype analysis.

BACKGROUND: Acute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes. METHODS: Gln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis. RESULTS: Through a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes. CONCLUSION: Eight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification.

Characteristics of molecular subtypes and cinical outcomes in the immunotherapy Queue of extensive-stage small cell lung cancer patients.

BACKGROUND: With a series of clinical trials confirming the sensitivity of small cell lung cancer (SCLC) to immunotherapy, research on personalized treatment for SCLC has gained increasing attention. Currently, the most widely accepted subtype of SCLC is based on the expression levels of Achaete-Scute Family BHLH Transcription Factor 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), and POU class 2 homeobox 3 (POU2F3). However, real-world studies on this classification remain limited. METHODS: We retrospectively collected biopsy specimens from patients who received immunotherapy at Shandong Provincial Hospital between January 2019 and July 2021. After determining the patient subtypes using immunohistochemistry, we analyzed the relationships between each subtype and survival as well as some clinical characteristics. RESULTS: In our study, we found that the subtype I achieved a significant survival advantage compared to the other groups. Additionally, the subtype A demonstrated a significant survival disadvantage. Among patients in the subtype I, there was a higher proportion of early brain metastasis and patients with a family history of tumors, while the subtype A had a lower proportion. Furthermore, the subtype A exhibited relatively poor immune infiltration. CONCLUSION: In a diverse cohort of SCLC patients receiving immunotherapy, the subtype-I showed significant survival advantages while the subtype-A experienced a worse survival.