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The processes of cancer initiation, progression, and response to therapy are affected by the sex of cancer patients. Immunotherapy responses largely depend on the tumor microenvironment (TME), but how sex may shape some TME features, remains unknown. Here, we analyzed immune infiltration signatures across 19 cancer types from 1771 male and 1137 female patients in The Cancer Genome Atlas to evaluate how sex may affect the tumor mutational burden (TMB), immune scores, stromal scores, tumor purity, immune cells, immune checkpoint genes, and functional pathways in the TME. Pan‐cancer analyses showed higher TMB and tumor purity scores, as well as lower immune and stromal scores in male patients as compared to female patients. Lung adenocarcinoma, lung squamous carcinoma, kidney papillary carcinoma, and head and neck squamous carcinoma showed the most significant sex biases in terms of infiltrating immune cells, immune checkpoint gene expression, and functional pathways. We further focused on lung adenocarcinoma samples in order to identify and validate sex‐specific immune cell biomarkers with prognostic potential. Overall, sex may affect the tumor microenvironment, and sex‐specific TME biomarkers may help tailor cancer immunotherapy in certain cancer types. This study evaluated how sex may affect some TME features, including tumor mutational burden (TMB), immune scores, stromal scores, tumor purity, immune cells, immune checkpoint genes, and functional pathways in pan‐cancer and individual cancer types. The results revealed that sex may be a key factor affecting TME features in several cancer types.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a high incidence and increased mortality. CC chemokine receptors were participating in the modulation of the tumor microenvironment and involved in carcinogenesis and tumor development. However, the potential mechanistic values of CC chemokine receptors as clinical biomarkers and therapeutic targets in LUAD have not been fully clarified. ONCOMINE, UALCAN, GEPIA, Kaplan-Meier Plotter, SurvExpress, MethSurv, SurvivalMeth, cBioPortal, String, GeneMANIA, DAVID, Metascape, TRRUST, LinkedOmics, and Timer were applied in this work. The transcriptional levels of CCR1/10 in LUAD tissues were significantly reduced while the transcriptional levels of CCR3/6/7/8 were significantly elevated, and the expression of CCR1 was the highest in LUAD among these CC chemokine receptors. A significant correlation was found between the expression of CCR2/4/6/7 and the pathological stage of LUAD patients. There were significant associations between CCR2/3/4/5/6/10 expression levels and OS in LUAD, and LUAD patients with high transcriptional levels of CCR3/4 had inferior first-progression survival. In addition, the prognostic values of CC chemokine receptors signature in LUAD were explored in three independent cohorts, the high-risk group displayed unfavorable OS compared with the low-risk group, and the LUAD cases in the high-risk group also suffered inferior RFS than that in the low-risk group. And for the prognostic value of the DNA methylation of CC chemokine receptors, we found 1 CpG of CCR2, 2 CpGs of CCR3, 1 CpG of CCR4, 3 CpGs of CCR6, 3 CpGs of CCR7, 1 CpG of CCR8, and 3 CpGs of CCR9 were significantly associated with prognosis in LUAD patients. However, the DNA methylation signature analysis showed there was no statistically significant association between the high- and low-risk group. For potential mechanism, the neighbor gene networks, interaction analyses, functional enrichment analyses of CC chemokine receptors in LUAD were performed, the transcription factor targets, kinase targets, and miRNA targets of CC chemokine receptors were also identified in LUAD. We also found significant correlations among CC chemokine receptors expression and the infiltration of immune cells, the tumor infiltration levels among LUAD with different somatic copy number alterations of these chemokine receptors were also assessed. Moreover, the Cox proportional hazard model showed that CCR1/2/10, B_cell, CD4_Tcell were significantly related to the clinical outcome of LUAD patients. CC chemokine receptors might serve as immunotherapeutic targets and prognostic biomarkers in LUAD.

Discovering a new fibroblast type that supports immune activity and improves surgical outcomes in relapsed ovarian cancer Ovarian cancer is one of the most deadly cancers in women, and many patients experience relapse after initial treatment. For some of these patients, a second surgery called secondary cytoreductive surgery (SCR) can help remove remaining cancer tissue and improve survival. However, not all patients benefit equally from this surgery, and predicting who will respond best remains a major challenge. In this study, we looked closely at the tumor microenvironment, the community of non-cancerous cells that surround and interact with cancer cells. Our focus was on cancer-associated fibroblasts (CAFs), a type of support cell that can either help or hinder tumor growth. By combining advanced laboratory techniques, including multiplex immunohistochemistry, single-cell RNA sequencing, and computer-based spatial analysis, we analyzed tumor samples from patients with relapsed ovarian cancer to understand how different CAF types behave. We discovered a specific group of CAFs that express a protein called S100A4, which are found mainly around the edges of tumors. These S100A4 positive CAFs were more common in patients who achieved complete tumor removal (R0 resection) during surgery. They also showed signs of helping regulate the immune system rather than promoting cancer growth. Importantly, we identified a smaller subgroup named S100A4 positive antigen-presenting CAFs (apCAFs) that also carry a molecule called CD74, enabling them to interact with immune cells known as CD4 positive T cells. Patients with higher levels of these cells tended to have better overall survival. Our findings suggest that S100A4 and CD74 double positive fibroblasts may play a beneficial role in controlling cancer progression and could serve as promising biomarkers for predicting surgical outcomes and developing personalized therapies for relapsed ovarian cancer.

Background/Aims: The expression of PRAME and its role in hepatocellular carcinoma (HCC) remain unknown. The aim of this study was to examine the functional role of PRAME in HCC development and exploring the molecular mechanism. Methods: We first detected PRAME expression in 96 human HCC tissue samples and correlated with clinicopathological characteristics and prognosis of the patients. We then established stable HCC cell lines with PRAME overexpression and knockdown followed by functional analysis in vitro. Further, we examined the relationship between PRAME and p53 pathway in vitro by using Western blotting. Finally, PRAME expression was detected to evaluate its correlation with p-p53 and p53 pathway related apoptotic proteins in xenograft tumor mouse model using immunohistochemistry. Results: PRAME expression was significantly higher in HCC tissues than in adjacent non-tumor tissues and their expression was positively correlated with alpha fetoprotein levels and tumor size. In addition, PRAME expression was associated with AJCC stage and is a potential biomarker of poor prognosis regarding 5-year overall survival in HCC. In vitro studies, we found that PRAME expression was higher in HCC cell lines than in normal hepatic cell line. Inhibited cell proliferation and increased cell apoptosis was observed in PRAME knockdown HCC cells. Futher, increased cell apoptosis was correlated with the proportion of cells in G0/G1 stage, activated p53 mediated apoptosis, and increased cyclin p21 expression. Xenograft analysis in nude mice also found that PRAME knockdown inhibited tumorigenesis while PRAME overexpression had opposite effect. Conclusions: In HCC, PRAME serves as a potential biomarker for poor prognosis and novel therapeutic target in treating this cancer. PRAME is a potential biomarker of poor prognosis in HCC. PRAME surpresses HCC cell death in vitro and in vivo by regulating p53 apoptotic signaling and may serve as a potential therapeutic target in HCC.

MicroRNAs are a family of small, genome-encoded endogenous RNAs that are transcribed but are not translated into proteins. They serve essential roles in virtually every aspect of brain function, including neurogenesis, neural development, and cellular responses leading to changes in synaptic plasticity. They are also implicated in neurodegeneration and neurological disorders, in responses to hypoxia and ischemia, and in ischemic tolerance induced by ischemic preconditioning. In recent developments, miRNA expres- sion profiling has been examined in stroke, and these studies indicate that miRNAs have emerged as key mediators in ischemic stroke biology. Both increased and decreased miRNA levels may be needed either as prevention or treatment of stroke. Novel approaches are being developed to get miRNA related therapeu- tics into the brain across an intact blood-brain barrier, including chemical modification, use of targeting molecules and methods to disrupt the blood-brain barrier.

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Colorectal cancer (CRC) is the leading cause of cancer deaths around the world. It is necessary to identify patients with poor prognosis or with high risk for recurrence so that we can selectively perform intensive treatments such as preoperative and/or postoperative chemotherapy and extended surgery. The clinical usefulness of inflammation-related prognostic biomarkers available from routine blood examination has been reported in many types of cancer, e.g., neutrophil–lymphocyte ratio (NLR), lymphocyte–C-reactive protein ratio (LCR), platelet–lymphocyte ratio (PLR), lymphocyte–monocyte ratio (LMR), and so on. Moreover, some scoring systems based on circulating blood cell counts and albumin concentration have been also reported to predict cancer patients’ prognosis, such as the Glasgow prognostic score (GPS), systemic inflammation score (SIS), and prognostic nutritional index (PNI). The optimal biomarker and optimal cutoff value of the markers can be different depending on the cancer type. In this review, we summarize the prognostic impact of each inflammation-related marker in CRC.

In patients with endometrial cancer, the expression and prognostic significance of calreticulin (CRT) remains to be fully elucidated. To investigate the role of CRT in endometrial cancer, the present study compared its expression status with clinicopathological characteristics and evaluated its prognostic significance. The expression of CRT, PKR-like endoplasmic reticulum kinase (PERK), phosphorylated eukaryotic initiation factor 2α (p-eIF2α), and Ki67 were assessed by immunohistochemistry and/or western blotting in endometrial cancer patients. The association of the expression of CRT, p-eIF2α and Ki67 with patient survival rate was assessed by Kaplan-Meier and Cox regression analyses. Low levels of CRT and an overexpression of Ki67 were significantly associated with the stage, histology, and differentiation of the primary surgery without doxorubicin (DOX) neoadjuvant chemotherapy (NAC) patient group and were significantly correlated with a short progression-free survival and the overall survival. A multivariate analysis revealed that CRT and Ki67 expression were independent prognostic indicators for endometrioid endometrial cancer. Low CRT expression and an overexpression of Ki67 were significantly associated with DOX-NAC and the histology (P<0.05) pre-NAC and post-NAC in the DOX-NAC patient group. Upon treatment of DOX-NAC, CRT, PERK and p-eIF2α protein content were overexpressed in DOX-sensitive endometrial cancer (P<0.05), whereas there was no significant difference in the DOX-resistant group. Low CRT expression in endometrial cancer is significantly associated with aggressive progression and poor prognosis. CRT may therefore serve as a molecular marker for predicting the progression and prognosis in DOX-resistant endometrial cancer patients.

Accumulating research showed that ENC1 plays a critical role in maintaining the physiological functions. However, little is known about its role in predicting prognosis and immunotherapy response across cancers. In our results, compared to normal tissues, most cancer tissues exhibit increased ENC1 expression. We found that the most common type of genetic variation was gene mutation. In addition, a positive correlation was found between CNV and ENC1 expression. Moreover, the overexpression of ENC1 was positively correlated with poor clinical outcomes. The GSEA results showed that ENC1 is closely correlated with tumor-promoting biological functions in most cancers. ENC1 is also closely negatively associated with the infiltration levels of T cells, activated NK cells, and B cells. Most immunomodulators are positively associated with ENC1. Further, we verified that inhibition of ENC1 expression suppressed the proliferation and migration of breast cancer, pancreatic cancer and glioma cells. In conclusion, our study demonstrated that ENC1 plays a protumorigenic role in most cancers. Additionally, ENC1 is closely correlated with tumor microenvironment features and immune checkpoint inhibitors expression. Overall, ENC1 could serve as a promising potential prognostic biomarker in various tumors.

Background Liver cancer is the sixth most commonly diagnosed cancer and the fourth most common cause of cancer death. The purpose of this work is to find new diagnostic biomarkers or prognostic biomarkers and explore the biological functions related to the prognosis of liver cancer. Methods GSE25097 datasets were firstly obtained and compared with TCGA LICA datasets and an analysis of the overlapping differentially expressed genes (DEGs) was conducted. Cytoscape was used to screen out the Hub Genes among the DEGs. ROC curve analysis was used to screen the Hub Genes to determine the genes that could be used as diagnostic biomarkers. Kaplan-Meier analysis and Cox proportional hazards model screened genes associated with prognosis biomarkers, and further Gene Set Enrichment Analysis was performed on the prognosis genes to explore the mechanism affecting the survival and prognosis of liver cancer patients. Results 790 DEGs and 2162 DEGs were obtained respectively from the GSE25097 and TCGA LIHC data sets, and 102 Common DEGs were identified by overlapping the two DEGs. Further screening identified 22 Hub Genes from 102 Common DEGs. ROC and survival curves were used to analyze these 22 Hub Genes and it was found that there were 16 genes with a value of AUC > 90%. Among these, the expression levels of ESR1,SPP1 and FOSB genes were closely related to the survival time of liver cancer patients. Three common pathways of ESR1, FOBS and SPP1 genes were identified along with seven common pathways of ESR1 and SPP1 genes and four common pathways of ESR1 and FOSB genes. Conclusions SPP1, AURKA, NUSAP1, TOP2A, UBE2C, AFP, GMNN, PTTG1, RRM2, SPARCL1, CXCL12, FOS, DCN, SOCS3, FOSB and PCK1 can be used as diagnostic biomarkers for liver cancer, among which FOBS and SPP1 genes can also be used as prognostic biomarkers. Activation of the cell cycle-related pathway, pancreas beta cells pathway, and the estrogen signaling pathway, while on the other hand inhibition of the hallmark heme metabolism pathway, hallmark coagulation pathway, and the fat metabolism pathway may promote prognosis in liver cancer patients.