The analysis of multiple omics and examination of pathological images revealed the prognostic and therapeutic significances of CD93 in lung squamous cell carcinoma.

As a potent pro-angiogenic factor, the role of CD93 in the prognosis and therapeutic outcomes of lung squamous cell carcinoma (LUSC) merits exploration. In this study, we systematically collected transcriptomic, genomic, and clinical data from various public databases, as well as pathological images from hospital-operated patients. Employing statistical analysis software like R (Version 4.2.2) and GraphPad (Version 8.0), we conducted comprehensive analyses of multi-omics data. The results revealed elevated CD93 expression in LUSC tissues, closely associated with various cancer-related pathways. High CD93 expression indicated advanced clinical stage and poorer prognosis. Furthermore, CD93 contributed to resistance against chemotherapy and immunotherapy by enhancing tumor cell stemness, reducing immune cell infiltration, and inducing T cell exhaustion. Patients with low CD93 expression exhibited higher response rates to both chemotherapy and immunotherapy. Immunohistochemistry validated the significance of CD93 in LUSC. CD93 emerges as a biomarker signaling unfavorable prognosis and influencing therapeutic outcomes, suggesting a potential LUSC treatment avenue.

Presence, Subtypes, and Prognostic Significance of Tertiary Lymphoid Structures in Urothelial Carcinoma of the Bladder.

OBJECTIVE: To evaluate the presence and subtypes of tertiary lymphatic structures (TLSs) in urothelial carcinoma of the bladder (UCB) and to analyze their associated clinicopathological characteristics and prognostic significance. METHODS: The study enrolled 580 patients with surgically treated UCB, including 313 non-muscle invasive bladder cancer (NMIBC) and 267 muscle-invasive bladder cancer (MIBC). The presence and subtypes of TLSs were identified by immunohistochemistry (CD20, CD3, Bcl-6, and CD21). TLSs were classified into non-GC (nGC) TLS and GC TLS subtypes based on germinal center (GC) formation. Disease-free survival (DFS) was used as an endpoint outcome to evaluate the prognostic significance of TLS and its subtypes in UCB. RESULTS: TLSs were more common in MIBC than in NMIBC (67.8% vs 48.2%, P < .001), and the tumor-infiltrating lymphocyte (TIL) mean density was significantly higher in MIBC than in NMIBC (24.0% vs 17.5%, P < .001). Moreover, a positive correlation was found between TLS presence and GC structure formation and TIL infiltration in UCB. Endpoint events occurred in 191 patients. Compared to patients with endpoint events, patients without disease progression exhibited higher TIL density and more TLSs (P < .05). Kaplan-Meier curves showed that TLS was associated with better DFS in NMIBC (P = .041) and MIBC (P = .049). However, the Cox multivariate analysis did not demonstrate the prognostic significance of TLS. CONCLUSIONS: TLS is heterogeneous in UCB, and that TLS and GC structures are related to TIL density and prognostic events. However, TLS as a prognostic indicator remains unclear, warranting further investigation.

Intratumoral and Peritumoral Radiomics Based on Preoperative MRI for Evaluation of Programmed Cell Death Ligand-1 Expression in Breast Cancer.

BACKGROUND: Programmed cell death ligand-1 (PD-L1) is a promising target for immune checkpoint blockade therapy in breast cancer. However, the preoperative evaluation of PD-L1 expression in breast cancer is rarely explored. PURPOSE: To determine the ability of radiomics signatures based on preoperative dynamic contrast-enhanced (DCE) MRI to evaluate PD-L1 expression in breast cancer. STUDY TYPE: Retrospective. POPULATION: 196 primary breast cancer patients with preoperative MRI and postoperative pathological evaluation of PD-L1 expression, divided into training (n = 137, 28 PD-L1-positive) and test cohorts (n = 59, 12 PD-L1-positive). FIELD STRENGTH/SEQUENCE: 3.0T; volume imaging for breast assessment DCE sequence. ASSESSMENT: Radiomics features were extracted from the first phase of DCE-MRI by using the minimum redundancy maximum relevance method and least absolute shrinkage and selection operator algorithm. Three radiomics signatures were constructed based on the intratumoral, peritumoral, and combined intra- and peritumoral regions. The performance of the signatures was assessed using area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and accuracy. STATISTICAL TESTS: Univariable and multivariable logistic regression analysis, t-tests, chi-square tests, Fisher exact test or Yates correction, ROC analysis, and one-way analysis of variance. P < 0.05 was considered significant. RESULTS: In the test cohort, the combined radiomics signature (AUC, 0.853) exhibited superior performance compared to the intratumoral (AUC, 0.816; P = 0.528) and peritumoral radiomics signatures (AUC, 0.846; P = 0.905) in PD-L1 status evaluation, although the differences did not reach statistical significance. DATA CONCLUSION: Intratumoral and peritumoral radiomics signatures based on preoperative breast MRI showed some potential accuracy for the non-invasive evaluation of PD-L1 status in breast cancer. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Is HER2 ultra-low breast cancer different from HER2 null or HER2 low breast cancer? A study of 1363 patients.

OBJECTIVE: This study aims to analyze whether there are any differences in clinicopathological features and prognosis between HER2 ultra-low, HER2-null, and HER2-low expression in Chinese breast cancer (BC) patients. METHODS: The clinicopathological data of 1363 HER2-negative BC patients were retrospectively collected (from January 2018 to December 2019). HER2 status was further classified into HER2-null, HER2 ultra-low, and HER2-low. HER2-null expression is defined as infiltrating cancer cells completely free of staining. HER2 ultra-low expression is defined as ≤10% of infiltrating cancer cells showing incomplete and faint/weak membrane staining. HER2-low expression is defined as HER2 immunohistochemistry (IHC) 1+ or 2+ with negative in situ hybridization (ISH) assay. RESULTS: Of 1363 patients, there were 86 (6.3%) HER2-null patients, 395 (29.0%) HER2 ultra-low patients, and 882 (64.7%) HER2-low patients. HER2 ultra-low patients were different from HER2-low patients in terms of N stage, hormone receptor (HR) status, Ki-67 expression, and type of surgery. There were also significant differences in histologic type and postoperative endocrine therapy between HER2 ultra-low and HER2-null patients. HR+ (81.0%) tumors was more common than HR- (19.0%) in HER2 ultra-low patients. In addition, there was a significant difference in HR status between HER2 ultra-low and HER2-low patients (P = 0.001). The survival analysis showed that HER2 status had no effect on disease-free survival (DFS) in HER2-negative patients (all P > 0.05). However, regardless of HER2 status, HR+ patients had better DFS than HR- patients (P = 0.003). Cox multivariate analysis revealed that age (HR [95% CI] = 0.950 [0.928, 0.972], P < 0.001), HR status (HR [95% CI] = 3.342 [1.658, 6.736], P = 0.001), and postoperative endocrine therapy (HR [95% CI] = 0.048 [0.048, 0.023], P < 0.001) were important influencing factors of DFS in HER2-negative BC patients. CONCLUSION: HER2 ultra-low BC patients demonstrated distinct clinicopathological features from HER2-null and HER2-low tumors; while, HER2 status (null, ultra-low, or low) had no prognostic value in these HER2-negative BC population. Consistent with the published literature, HR status was an independent prognostic factor for DFS in HER2-negative BC patients.

An MRI-Based Radiomics Nomogram to Distinguish Ductal Carcinoma In Situ with Microinvasion From Ductal Carcinoma In Situ of Breast Cancer.

RATIONALE AND OBJECTIVES: Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer. MATERIALS AND METHODS: MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves. RESULTS: Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703-0.926, 0.848-0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672-0.989, 0.764-0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544-0.801, 0.527-0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility. CONCLUSION: The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS.

Effects of MCU-mediated Ca2+ Homeostasis on Ovarian Cancer Cell SKOV3 Proliferation, Migration and Transformation.

BACKGROUND: Atlas human proteomics database showed MCU as highly expressed in various tumor tissues, especially in ovarian cancer. Rare studies on the role of MCU and its regulation in ovarian cancer have been reported. OBJECTIVE: The objective of this study was to determine role of MCU in ovarian cancer cell SKOV3 proliferation, migration, and transformation, and explore the possible mechanism. METHODS: MCU siRNA on lentiviral particles were stably transfected into SKOV3 cells. CCK-8 assay was performed to analyze cell proliferation. Soft-agar colony formation assay was employed to evaluate tumorigenesis. Western blot and immunohistochemistry analyses were performed to evaluate the expression of MCU, MICU1 and phosphorylate of Akt in the ovarian cancer cell and tissue specimens. Scratch assay was combined with trans-well plates assay to detect the migration ability of cancer cells. The ROS production and Ca2+ expression were also determined. RESULTS: MCU expression was significantly higher in ovarian cancer tissues than normal tissues. MCU silencing decreased SKOV3 cell proliferation, migration, and transformation. ROS production was decreased after MCU silencing, depending on disturbed Ca2+ homeostasis. MICU1 expression has been found to be decreased and phosphorylation of Akt increased when MCU was silenced. CONCLUSION: Down-regulation of MCU inhibited SKOV3 cell proliferation, migration, and transformation via disturbing Ca2+ homeostasis and decreasing ROS production. MICU1 and phosphorylation of Akt are associated with MCU-mediated ovarian cancer malignancy.

Evaluation of Lymphatic Vessel Invasion Determined by D2-40 Using Preoperative MRI-Based Radiomics for Invasive Breast Cancer.

RATIONALE AND OBJECTIVES: Preoperative prediction of LVI status can facilitate personalized therapeutic planning. This study aims to investigate the efficacy of preoperative MRI-based radiomics for predicting lymphatic vessel invasion (LVI) determined by D2-40 in patients with invasive breast cancer. MATERIALS AND METHODS: A total of 203 patients with pathologically confirmed invasive breast cancer, who underwent preoperative breast MRI, were retrospectively enrolled and randomly assigned to the following cohorts: training cohort (n=141) and test cohort (n=62). Then, univariate and multivariate logistic regression were performed to select independent risk factors and build a clinical model. Afterwards, least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select predictive features extracted from the early and delay enhancement dynamic contrast-enhanced (DCE)-MRI images, and a radiomics signature was established. Subsequently, a nomogram model was constructed by incorporating the radiomics score and risk factors. Receiver operating characteristic curves were performed to determine the performance of various models. The efficacy of the various models was evaluated using calibration and decision curves. RESULTS: Fourteen radiomics features were selected to construct the radiomics model. The size of the lymph node was identified as an independent risk factor of the clinical model. The nomogram model demonstrated the best calibration and discrimination performance in both the training and test cohorts, with an area under the curve of 0.873 (95% confidence interval [CI]: 0.807-0.923) and 0.902 (95% CI: 0.800-0.963), respectively. The decision curve illustrated that the nomogram model added more net benefits, when compared to the radiomics signature and clinical model. CONCLUSION: The nomogram model based on preoperative DCE-MRI images exhibits satisfactory efficacy for the noninvasive prediction of LVI determined by D2-40 in invasive breast cancer.

Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer Using Preoperative MRI-Based Radiomics.

BACKGROUND: Evaluating tumor-infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored. PURPOSE: To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level. STUDY TYPE: Retrospective. POPULATION: A total of 154 patients with breast cancer were divided into a training cohort (N = 87) and a test cohort (N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results. FIELD STRENGTH/SEQUENCE: 3.0 T; axial T2-weighted imaging (fast spin echo), diffusion-weighted imaging (spin echo-echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo). ASSESSMENT: A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated. STATISTICAL TESTS: The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t-tests and chi-squared tests or Fisher's exact test, Hosmer-Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant. RESULTS: The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9. DATA CONCLUSION: The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2.

A Retrospective Study on the Clinicopathologic Characteristics and Outcomes of 179 Cases of Synchronous and Metachronous Bilateral Breast Cancer in China.

OBJECTIVE: To investigate the clinicopathologic characteristics and outcome of bilateral breast cancer (BBC) in the Chinese population. METHODS: A retrospective study was conducted on 7797 cases with primary breast cancer, including 7618 cases of unilateral breast cancer (UBC) and 179 cases of BBC. Among the latter, there were 108 cases of synchronous BBC (SBBC) and 71 cases of metachronous BBC (MBBC). RESULTS: In the present study, the incidence of SBBC and MBBC are 1.39% and 0.91% among the general population, respectively. In comparison of UBC and BBC, SBBC and MBBC, there are significant differences in the common clinicopathological characteristics, such as pathologic stage, hormone receptor (HR) status and molecular type. In respect of the surgical treatment of BBC, 49.72% of the patients chose mastectomy. The 3-year disease free survival (DFS) for SBBC and MBBC are 94.4% and 96.9%, respectively. There is no difference in the overall survival (OS) and DFS between SBBC and MBBC. The histological grade and type of surgery on tumors of both sides are important influencing factors of DFS in the BBC patients. CONCLUSION: There are statistical differences in the clinicopathological characteristics and outcomes between SBBC and MBBC among the Chinese population. Therefore, the treatment of BBC patients should be individualized.

N6-methyladenosine methylation-related genes YTHDF2, METTL3, and ZC3H13 predict the prognosis of hepatocellular carcinoma patients.

Background: Hepatocellular carcinoma (HCC) is a common primary malignant tumor and cause of cancer-related death in humans. Increasing evidence indicates that an imbalance in N6-methyladenosine (m6A) methylation is strongly linked to the occurrence and development of cancer. We used comprehensive bioinformatics to establish a potential prognostic model of HCC based on m6A methylation-related genes. And case analyses were used to verify the results. Methods: The clinical data and gene expressions were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The prognostic value of m6A methylation-related genes in HCC patients and their relationship with the immune microenvironment were explored by comprehensive bioinformatics analyses. We also collected pathological specimens from 70 patients with HCC from the Department of Pathology, Affiliated Hospital of Qingdao University, and performed immunohistochemical staining on the specimens. We compared tumor specimens from 27 patients positive for METTL3, YTHDF2, and ZC3H13 staining with their adjacent normal tissues and against 27 patient specimens negative for METTL3, YTHDF2, and ZC3H13. The influence of METTL3, YTHDF2, and ZC3H13 on survival was analyzed, and the prognostic model for METTL3, YTHDF2, and ZC3H13 in HCC was verified by clinical data. Results: Most m6A methylation-related genes showed significantly different expressions between cancer and normal tissues. Three candidate m6A methylation-related genes (YTHDF2, METTL3, and ZC3H13) were significantly correlated with the overall survival (OS) of HCC patients. A Kaplan-Meier survival analysis indicated a worse prognosis of high-risk patients than that of low-risk patients. Immunological analysis showed that the high-risk group was more likely to have higher follicular helper T cell counts and lower resting memory CD4 T cell counts. The expression of YTHDF2, METTL3, and ZC3H13 was validated by other databases, including the Oncomine database, the Human Protein Atlas (HPA), and the Kaplan-Meier plotter. Conclusions: Our prognostic model based on m6A methylation-related genes effectively predicted the prognosis of HCC patients.

Survival-associated N6-adenosine methyltransferase signatures in lung squamous cell carcinoma and clinical verification.

BACKGROUND: N6-methyladenine (m6A) is the most common modification of mRNA and IncRNA in higher organisms. m6A has been confirmed to be related to the formation and progression of tumors and m6A-related genes can be used as prognostic biomarkers in a variety of tumors. However, there have been no similar studies on lung squamous cell carcinoma. The main purpose of this study was aimed to explore the differential expression of m6A-related genes in lung squamous cell carcinoma tissues and its relationship with patient clinical prognosis. METHODS: We integrated three m6A writers that catalyze the methylation of adenine on mRNA molecules. The training set including 501 patients with LUSC was collected from The Cancer Genome Atlas (TCGA) database and the test set including 181 patients with LUSC was collected from the Gene Expression Omnibus (GEO) database. Based on the expression level of the m6A methylase gene, we established a tumor subgroup and risk-prognosis model to quantify the risk index and long-term patient prognosis, which were confirmed by principal component analysis (PCA) and receiver operating characteristic (ROC) curve analysis. After lung squamous cell carcinoma tissue specimens were obtained during surgery, immunohistochemistry (IHC) was used to verify the results in vitro. RESULTS: The results of the study showed that the expression of the three m6A methylases in tumor tissues and normal tissues was significantly different (P < 0.05). The survival-prognostic model based on METTL3 gene expression showed better predictive performance (AUC: 0.706). Patients in the high-risk and low-risk groups exhibited significant differences in terms of survival time and 5-year and 10-year survival rates. Immunohistochemistry revealed that patients with high METTL3 expression exhibited a longer survival time than those with low METTL3 expression. CONCLUSIONS: Our study showed that the molecular phenotype based on the expression of METTL3 may be an independent risk factor affecting the prognosis of lung squamous cell carcinoma. These findings not only prove the important role of m6A methylase in lung squamous cell carcinoma, but are also expected to provide more accurate prognostic assessment and individualized treatment for patients with lung squamous cell carcinoma.

Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the ability of radiomic signatures based on MRI to evaluate the response and efficiency of neoadjuvant chemotherapy (NAC) for treating breast cancers. METHODS: 152 patients were included in this study at our institution between March 2017 and September 2019. All patients with breast cancer underwent a preoperative breast MRI and the Miller-Payne grading system was applied to evaluate response to NAC. Quantitative parameters were compared between patients with sensitive and insensitive responses to NAC and between those with pathological complete responses (pCR) and non-pCR. Four radiomic signatures were built based on T2W imaging, diffusion-weighted imaging, dynamic contrast-enhanced imaging and their combination, and radiomics scores (Rad-score) were calculated. The combination of the clinical factors and Rad-scores created a nomogram model. Multivariate logistic regression was performed to assess the association between MRI features and independent clinical risk factors. RESULTS: 20 features and 18 features were selected to build the radiomic signature for evaluating sensitivity and the possibility of pCR, respectively. The combined radiomic signature and nomogram model showed a similar discrimination in the training (AUC 0.91, 0.92, 95% confidence interval [CI], 0.85-0.96, 0.86-0.98) and validation (AUC 0.93, 0.91, 95% CI, 0.86-1.00, 0.82-1.00) sets. The clinical factor model exhibited reduced performance (AUC 0.74, 0.64, 95% CI, 0.64-0.84, 0.46-0.82) in terms of NAC sensitivity and pCR. CONCLUSIONS: The combined radiomic signature and nomogram model exhibited potential predictive power for predicting effective NAC treatment which can aid in the prognosis and guidance of treatment regimens. ADVANCES IN KNOWLEDGE: Identifying a means of assessing the efficacy of NAC before surgery can guide follow-up treatment and avoid chemotherapy-induced toxicity.

Layer I as a putative neurogenic niche in young adult guinea pig cerebrum.

A considerable number of cells expressing typical immature neuronal markers including doublecortin (DCX+) are present around layer II in the cerebral cortex of young and adult guinea pigs and other larger mammals, and their origin and biological implication await further characterization. We show here in young adult guinea pigs that these DCX+ cells are accompanied by in situ cell division around the superficial cortical layers mostly in layer I, but they co-express proliferating cell nuclear antigen (PCNA) and an early neuronal fate determining factor, PAX6. A small number of these DCX+ cells also colocalize with BrdU following administration of this mitotic indicator. Cranial X-ray irradiation causes a decline of DCX+ cells around layer II, and novel environmental exploration induces c-Fos expression among these cells in several neocortical areas. Together, these data are compatible with a notion that DCX+ cortical neurons around layer II might derive from proliferable neuronal precursors around layer I in young adult guinea pig cerebrum, and that these cells might be modulated by experience under physiological conditions.