To evaluate the effectiveness of a novel computerized quantitative analysis based on histopathological and computed tomography (CT) images for predicting the postoperative prognosis of esophageal squamous cell carcinoma (ESCC) patients.

Currently, the clinicopathological and prognostic characteristics of dedifferentiated chordoma (DC) and poorly differentiated chordoma (PDC) remain poorly understood. In this study, we sought to characterize clinicopathological parameters in a large PDC/DC cohort and determine their correlations with progression-free survival (PFS) and overall survival (OS) of patients. We also attempted to compare clinical features between PDC/DC and conventional chordoma (CC).

To explore the potential of synchrotron radiation (SR) phase contrast imaging (PCI) for real-time microbubble formation monitoring during radiofrequency ablation (RFA).

Endometrial cancer (EC) is one of the most frequent gynecological tumors, and chemoresistance is a major obstacle to improving the prognosis of EC patients. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have recently emerged as crucial chemoresistance regulators that alter the levels of downstream target genes. Multidrug Resistance Protein 7 (MRP-7/ABCC10) is an ATP-binding cassette transporter that causes the resistance to anti-cancer drugs. The purpose of this research is to determine whether MRP-7 has a role in mediating the sensitivity of EC cells to paclitaxel and whether the expression of MRP-7 is regulated by miR-98 and lncRNA NEAT1. We reported that the levels of MRP-7 were significantly increased in EC tissues and associated with an unfavorable prognosis. Downregulation of MRP-7 in EC cells sensitized these cells to paclitaxel and reduced cell invasion. PLAUR serves as a downstream molecule of MRP-7 and facilitates paclitaxel resistance and EC cell invasiveness. Moreover, miR-98 serves as a tumor suppressor to inhibit MRP-7 expression, leading to the repression of paclitaxel resistance. Furthermore, a novel lncRNA, NEAT1, was identified as a suppressor of miR-98, and NEAT1 could upregulate MRP-7 levels by reducing the expression of miR-98. Taken together, these findings demonstrate that upregulation of MRP-7 and NEAT1, and downregulation of miR-98 have important roles in conferring paclitaxel resistance to EC cells. The modulation of these molecules may help overcome the chemoresistance against paclitaxel in EC cells.

A heterozygous point mutation of p53 gene at codon 280 from AGA to ACA (R280T) frequently occurs in nasopharyngeal carcinoma (NPC) cell lines, and about 10% NPC tissues. However, the role of this mutation in the pathogenesis of NPC remains unclear. In this study, we generated p53 knockout (KO) NPC cell lines from CNE2 cells carrying heterozygous p53 R280T (p53-R280T) mutation and C666-1 cells carrying wild-type p53 by CRISPR-Cas9 gene editing system, and found that KO of heterozygous p53-R280T significantly decreased NPC cell proliferation and increased NPC cell apoptosis, whereas KO of wild-type p53 had opposite effects on NPC cell proliferation and apoptosis. Moreover, KO of heterozygous p53-R280T inhibited the anchorage-independent growth and in vivo tumorigenicity of NPC cells. mRNA sequencing of heterozygous p53-R280T KO and control CNE2 cells revealed that heterozygous p53-R280T mutation activated PI3K-Akt signaling pathway. Moreover, blocking of PI3K-Akt signaling pathway abolished heterozygous p53-R280T mutation-promoting NPC cell proliferation and survival. Our data indicate that p53 with heterozygous R280T mutation functions as an oncogene, and promotes the oncogenicity of NPC cells by activating PI3K-Akt signaling pathway.

The study aims to analyze the expression of N6-methyladenosine (m6A)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ.

Lung cancer is one of the most common cancers and a significant cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer cases. Therefore, it is crucial to identify effective diagnostic and therapeutic methods. In addition, transcription factors are essential for eukaryotic cells to regulate their gene expression, and aberrant expression transcription factors are an important step in the process of oncogenesis in NSCLC.

Breast cancer has a high tumor-specific death rate and poor prognosis. In this study, we aimed to provide a basis for the prognostic risk in patients with breast cancer using significant gene sets selected by analyzing tumor mutational burden (TMB) and DNA damage repair (DDR).

Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.

Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

Published data have suggested a critical role for microRNA (miRNA) expression in chordoma progression. However, most of these studies focus on single miRNA and no multi-miRNA prognostic signature has been currently established for chordoma. In this study, we sought to develop and validate a 6-miRNA risk score (miRscore) model for survival prediction.

Objective: To assess the postoperative prognosis of patients with stage IB non-small cell lung cancer (NSCLC), using a prognostic model (PM). Methods: Patients with stage IB of NSCLC from the two academic databases {the Surveillance, Epidemiology, and End Results [SEER-A, N = 1,746 (training cohort)], Sun Yat-sen University Cancer Center [SYSUCC, N = 247 (validation cohort)], and SEER-B (N = 1,745)} who had undergone lung surgery from 2001 to 2015 were enrolled. The primary clinical endpoint was cancer-specific survival (CSS). Covariate inclusion of prognostic indicators was carried out using a multivariable two-sided P < 0.05. We identified and integrated significant prognostic factors for survival in the training cohort to build a model that could be validated in the validation cohort. We used univariate analysis to evaluate the utilized ability of PM in the different races/ethnicities. Results: CSS discrimination in the PM was comparable in both the training and validation cohorts [C index = 0.66(SEER-A), 0.67(SYSUCC), and 0.61(SEER-B), respectively]. Discretization with a fixed PM cutoff of 291.5 determined from the training dataset yielded low- and high-risk subgroups with disparate CSS in the validation cohort (training cohort: hazard ratio [HR] 2.724, 95% confidence intervals [CI], 2.074-3.577; validation cohort: SEER-B HR 1.679, 95% CI, 1.310-2.151, SYSUCC HR 3.649, 95% CI 2.203-6.043, all P < 0.05). Our five-factor PM was able to predict CSS; 48-month CSS was 87% in the low-risk subgroup vs. 69% in the high-risk subgroup for the training cohort, while in the validation cohort, they were 80 vs. 73%(SEER-B) and 84 vs. 60% (SYSUCC), respectively. In addition, the results showed that PM with all unadjusted HR > 1 was a significant risk prognostic indictor in white men (P < 0.001), Chinese people (P < 0.001), and other races (P = 0.012). Conclusion: We established and validated a PM that may predict CSS for patients with IB NSCLC in different races/ethnicities, and thus, help clinicians screen subgroups with poor prognosis. In addition, further prospective studies and more cases from different regions are necessary to confirm our findings.

Colon cancer undergoes a traditional pathway from colon polyps to colon cancer. It is of great significance to investigate the key molecules involved in carcinogenesis from polyps to malignancies. Circular RNAs (circRNAs) are stably expressed in human body fluids such as plasma. Here, we demonstrated a differential expression pattern of plasma circRNAs in healthy individuals, colon polyp patients and colon cancer patients using circRNA Arraystar microarray. We explored that circRNA HADHA (circHADHA) was upregulated in plasma from polyp patients, whereas it was downregulated in plasma from colon cancer patients. Overexpression of circHADHA promoted autophagy in colon epithelial cells. Moreover, in colon cancer cells, overexpression of circHADHA promoted autophagy, whereas it inhibited cell proliferation and colony formation. CircHADHA increased the expression of ATG13 via miR-361 in both colon epithelial and cancer cells. ATG13 knockdown reduced autophagy even in the presence of circHADHA in colon cancer cells. Furthermore, the growth of circHADHA-overexpressing colon cancer cell-derived xenograft tumors was significantly decreased compared with control tumors in nude mice. In conclusion, circHADHA was differentially expressed in the plasma of healthy individuals, colon polyp patients and colon cancer patients. CircHADHA promoted autophagy by regulating ATG13 via miR-361 in both colon epithelial and cancer cells. CircHADHA suppressed tumor growth by inducing cell autophagy in colon cancer cells. CircHADHA potentially serves as a biomarker for screening of precursor colon cancer and a therapeutic target for colon cancer treatment.