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Results for “prognostic biomarker”

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At least 19 recordsLinked to original sources

PubMed2026

Multi-omics and experimental validation identify RAPGEF2 as a protective prognostic biomarker in clear cell renal cell carcinoma.

Kidney Renal Clear Cell Carcinoma (KIRC) is characterized by marked molecular heterogeneity and metabolic reprogramming, underscoring the need for reliable biomarkers for prognostic assessment and individualized treatment. RAPGEF2, a guanine nucleotide exchange factor has been implicated in cell adhesion and differentiation, but its role in KIRC remains unclear. In this study, we systematically evaluated the expression pattern, prognostic significance, genomic associations, biological function, and therapeutic relevance of RAPGEF2 in KIRC through integrated multi-omics analyses and experimental validation. Pan-cancer single-cell and Spatial transcriptomic analysis revealed heterogeneous RAPGEF2 expression across tumor types, with a relatively prominent signal in KIRC, where RAPGEF2 was mainly enriched in endothelial cells. Survival analyses in the TCGA-KIRC showed that high RAPGEF2 expression was significantly associated with favorable overall survival, disease-specific survival, and progression-free interval, and these findings were validated in independent ICGC_RECA-EU and E-MTAB-1980 cohorts. Multivariate Cox regression further confirmed RAPGEF2 as an independent protective prognostic factor. Immunohistochemistry in a tissue microarray cohort demonstrated that higher RAPGEF2 protein expression was associated with improved overall survival. Genomic analyses showed that low RAPGEF2 expression was related to higher mutational burden. Functional assays demonstrated that RAPGEF2 knockdown promoted KIRC progression. Enrichment analyses indicated that RAPGEF2 may be associated with metabolic pathway remodeling, while immunotherapy cohort analyses suggested its potential association with therapeutic benefit. Collectively, RAPGEF2 is identified as a protective prognostic biomarker and potential functional regulator in KIRC.

Biomarker↗

PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗

PubMedDate not supplied

LEA.135 expression: its comparison with other prognostic biomarkers for patients with primary breast carcinoma.

The purpose of this retrospective study was to examine the prognostic value of expression of luminal epithelial antigen (LEA.135) for recurrence and overall survival of patients with primary invasive breast carcinoma by both univariate and multivariate analyses. The possible prognostic value of LEA.135 was also compared with some widely utilized prognostic biomarkers such as c-erbB 2, topoisomerase II.alpha (TPII.alpha), MIB 1, estrogen receptor (ER) and progesterone receptor (PR), as well as age of the patients and clinicopathologic parameters. The study was carried out by immunohistochemical methods on formalin-fixed/paraffin-embedded tissue sections in a series of 225 patients with median follow-up of 8.5 years. Prognostic significance of the biomarkers was determined by two-sided p value. In this series of patients, among the age and clinicopathologic parameters, only age, was significantly associated with a decreased overall survival (logrank p = 0.027). Among the prognostic biomarkers, TPII a expression at high (> 50% positive cells) or moderate (6-50% positive cells) level was associated with an increased rate of recurrence (logrank p < 0.001). However, the association of TPII.alpha expression with a decreased overall survival failed to reach a statistically significance. Expression of c-erbB 2 showed a trend of being associated with an increased probability of recurrence, but the association did not reach statistical significance. The remaining biomarkers were not associated with either the probability of recurrence or overall survival. LEA.135 expression was observed in 163 (72.4%) of the 225 patients. The patients with high (> 50% positive cells) or moderate (6-50% positive cells) level of LEA.135-positive cancer cells showed a significantly decreased probability of recurrence (logrank p < 0.001) and an increased overall survival (logrank p < 0.001) compared with those with LEA.135-negative cancer cells. The association remained significant by multivariate analysis for recurrence (likelihood ratio test p < 0.001) and overall survival (likelihood ratio test p < 0.001) when assessed with other prognostic parameters. Furthermore, the combination of LEA.135 with other prognostic biomarkers stratified four subgroups of patients with distinct clinical outcome. The subgroup of patients who were LEA.135+/TPII.alpha- showed the lowest probability of recurrence and the longest overall survival compared with those who were LEA.135-/TPII.alpha+ (logrank p < 0.001). Interestingly, the patients whose cancer cells were LEA.135+/TPII.alpha+, LEA.135+ MIB.1+ or LEA.135+/c-erbB 2+ experienced a decreased probability of recurrence and an increased overall survival compared with those with LEA.135-/TPII.alpha+, LEA.135- MIB.1+ or LEA.135-/c-erbB 2+ (logrank p < 0.001). The results demonstrated that LEA.135 is an independent and favorable prognostic biomarker for patients with primary invasive breast carcinoma, that the loss of LEA.135 expression is associated with aggressive phenotype of cancer cells during the breast cancer progression, and that its continued expression seems to override the adverse effects of expression of an oncogene or cell proliferation-associated molecules.

Age Factors↗

PubMed2026

Unveiling novel transcriptomic prognostic biomarkers for specific breast cancer subtypes and treatment regimens.

BACKGROUND: Breast cancer (BRCA) is the most common cancer in women worldwide, yet current gene expression panels offer limited insight into treatment responses across different subtypes and therapies. This study aimed to identify reliable biomarkers for predicting treatment outcomes in specific BRCA subtypes and treatment regimens. METHODS: This study analyzed transcriptomic data from The Cancer Genome Atlas to identify differentially expressed genes (DEGs) in patient groups treated with different combinations of hormone therapy (H), chemotherapy (C), radiotherapy (R), and targeted therapy (T). Non-negative matrix factorization clustering was performed to stratify patients into clusters representing different BRCA subtypes. Functional enrichment analysis was performed, and survival assessments were conducted using the METABRIC dataset. RESULTS: A total of 1,148 DEGs were identified across treatment regimens, with 75 common DEGs shared across multiple regimens. Among these, 12 candidate biomarkers were associated with luminal subtypes treated with H, including LRP1B, of which high expression predicted cancer recurrence. In triple-negative breast cancer (TNBC) treated with C, 76 candidate biomarkers were identified, including TTYH1 for recurrence and ANXA8L1 and MPZ for non-recurrence. Functional analyses identified intermediate filament organization and keratinization as pathways associated with specific candidate biomarkers of TNBC following C. Survival analysis using METABRIC strengthened the prognostic ability of LRP1B and TTYH1 to predict worse survival and ANXA8L1 and MPZ to predict prolonged survival, with four additional prognostic biomarkers. CONCLUSION: This study identified gene expression prognostic biomarkers for luminal and TNBC subtypes, thereby supporting personalized therapies. Further experimental validation is required to confirm these findings for clinical application. CLINICAL TRIAL REGISTRY: No.

Breast cancer↗

PubMed2026

Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.

BACKGROUND: Risk stratification in colorectal cancer (CRC) plays an important role in treatment decision-making. As such, prognostic biomarkers that can augment risk stratification have clinical value. Quantitative histologic features from routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) provide a novel avenue for biomarker discovery. In this study, we explored the potential for a computational histology artificial intelligence (CHAI) platform to develop and validate a prognostic biomarker in CRC. METHODS: The Cancer Genome Atlas Colorectal Adenocarcinoma project was utilized for this study, with inclusion of all subjects (stage I-IV) with available digitized H&E specimens. The cohort was split into development and validation cohorts by a stratified random split. The previously developed CHAI platform was applied in the development cohort to construct a continuous risk score from histologic features associated with progression-free interval (PFI) that was dichotomized based on an optimized cutpoint for distinguishing PFI into a high risk CHAI (+) and lower risk CHAI (-). PFI was compared between CHAI (+) and CHAI (-) patients in the validation cohort in multivariable Cox proportional hazards models. Time-dependent area under the curve (tdAUC) and C-indices were also calculated for PFI. RESULTS: A total of 583 participants were included in the study, with 409 assigned to the validation cohort. The CHAI biomarker classified 229 participants (56%) as CHAI (+) and 180 (44%) as CHAI (-) in the validation set. CHAI (+) participants had worse PFI in a multivariable analysis adjusting for available clinicopathologic variables (hazard ratio (HR) = 2.65; 95% confidence interval (CI), 1.63-4.30). TdAUC for the CHAI biomarker was 0.60 (95% CI, 0.53-0.67) at 12 months, 0.62 (0.55-0.69) at 36 months, and 0.67 (0.55-0.79) at 60 months; the C-index was 0.62 (95% CI, 0.58-0.67). CONCLUSIONS: The CHAI platform was used to develop a prognostic digital pathology biomarker in CRC. This demonstrates the feasibility and potential to apply this artificial intelligence-based digital pathology biomarker platform for risk stratification in CRC and supports its further study.

Artificial intelligence↗

PubMed2026

Integrated multi-omics analysis and functional experiments reveals PPAP2C as a potential prognostic biomarker and therapeutic target in breast cancer.

BACKGROUND: This study aims to systematically elucidate the clinical significance and biological function of the phospholipid phosphatase (PLPP) family member (PPAP2C) phosphatidic acid phosphatase type 2C in breast cancer, and to evaluate its potential as a prognostic biomarker and therapeutic target. METHODS: Gene expression data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases were integrated to characterize the expression profile of PLPP family members, focusing on PPAP2C in breast cancer. The prognostic value of PPAP2C, initially identified at the mRNA level (TCGA, (METABRIC) Molecular Taxonomy of Breast Cancer International Consortium, Gene Expression Omnibus (GEO)), was confirmed at the protein level by immunohistochemistry (IHC) on tissue microarrays (TMA). The oncogenic functions of PPAP2C were investigated in triple-negative breast cancer (TNBC) cells through CRISPR-Cas9-mediated knockout and ectopic overexpression, with assessment of key phenotypes including proliferation, colony formation, migration, and invasion. In vivo validation was subsequently performed using an MDA-MB-231 xenograft model. RESULTS: PPAP2C exhibits the most significant overexpression pattern across 33 cancer types (upregulated in 16 cancers, downregulated in only 3). Compared with normal tissues, PPAP2C showed specific overexpression in breast cancer tissues and was significantly associated with advanced clinical stages and aggressive subtypes (HER2+ and TNBC). Survival analysis demonstrated that high PPAP2C expression correlated with significantly shorter overall survival and disease-free survival, which was further validated in METABRIC and GEO cohorts. Tissue microarray analysis confirmed higher PPAP2C protein positivity in tumor tissues (94.7%) than in adjacent normal tissues (59.7%), with worse OS and RFS in high-expression groups. Multivariate analysis identified PPAP2C as an independent prognostic factor for OS. Functional experiments revealed that PPAP2C knockout (via 5-bp/1-bp frameshift mutations) suppressed TNBC cell proliferation, colony formation, migration, and invasion, while overexpression enhanced these phenotypes. In vivo studies further demonstrated complete tumor regression in MDA-MB-231 xenografts upon PPAP2C knockout. CONCLUSION: This study identifies PPAP2C as a key oncogenic driver and a robust independent prognostic biomarker in breast cancer. The findings provide compelling evidence that PPAP2C represents a promising therapeutic target, offering a new strategic avenue for precision therapy, particularly for aggressive breast cancer subtypes.

PLPP2↗

PubMedDate not supplied

Race/ethnicity, social class, and prevalence of breast cancer prognostic biomarkers: a study of white, black, and Asian women in the San Francisco bay area.

We assessed distributions of breast cancer prognostic biomarkers by race/ethnicity and socioeconomic position among paraffin-embedded tumor biopsy specimens from 135 US women (48 white women, 44 black women, 43 Asian women) diagnosed with breast cancer between 1966 and 1990. No racial/ethnic or socioeconomic differences in distributions were observed for tumor stage, lymph node involvement, estrogen, progesterone, and epidermal growth factor receptors, oncogenes such as Her2/neu and p53, cytoplasmic proteins cathepsin-D and ps2, and two indices of cell growth, Ki67 and DNA ploidy, adjusting for age at diagnosis, menopausal status, place of birth and, for racial/ethnic comparisons, working class composition of census block-group at diagnosis. Black and Asian women, however, were 3.5 times (95% confidence interval [CI] = 1.2, 10.1) and 3.7 times (95% CI = 1.3, 10.6), respectively more likely than white women to have a tumor size of > or = 20 mm, and Asian women were 3.4 times (95% CI = 1.1, 10.4) more likely than black women to be positive for androgen receptor, adjusting for these same factors. No differences in distributions by socioeconomic position were observed for these latter two tumor characteristics. These data suggest that racial/ethnic and socioeconomic disparities in breast cancer survival are unlikely to be explained solely by differential distributions of molecular breast cancer prognostic biomarkers.

Asian↗

PubMed2026

PPRC1 is a prognostic biomarker and key regulator of mitochondrial oxidative phosphorylation in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) remains an incurable haematological malignancy, underscoring the need for novel prognostic biomarkers and therapeutic targets. This study aimed to investigate the clinical and biological significance of peroxisome proliferator-activated receptor gamma coactivator-related protein 1 (PPRC1) in MM. METHODS: Expression and clinical data were obtained from public databases and an independent local cohort. Kaplan-Meier and Cox regression analyses were performed to evaluate prognostic value. Differential expression analysis, pathway enrichment analysis and single-cell RNA-seq data analysis were used to explore biological functions. PPRC1 was silenced in MM cell lines using siRNA to assess its effects on cell survival and oxidative phosphorylation. RESULTS: PPRC1 was significantly upregulated in MM and was associated with advanced disease stage and poor overall survival. Multivariate Cox analysis identified PPRC1 as an independent prognostic factor. A nomogram incorporating PPRC1 and revised-ISS improved survival prediction. Functional analyses revealed that PPRC1 was positively correlated with oxidative phosphorylation and oncogenic signalling pathways. A potential connection between PPRC1 expression and immune cell infiltration was observed. PPRC1 knockdown inhibited cell proliferation, induced cell cycle arrest and apoptosis and impaired oxidative phosphorylation in MM. CONCLUSIONS: PPRC1 acts as a prognostic biomarker and metabolic regulator in MM by sustaining mitochondrial oxidative phosphorylation. These findings highlight PPRC1 as a potential therapeutic target in MM.

Humans↗

PubMed2006

Surface-enhanced laser desorption/ionization time of flight mass spectrometry protein profiling identifies ubiquitin and ferritin light chain as prognostic biomarkers in node-negative breast cancer tumors.

Novel prognostic biomarkers are imperatively needed to help direct treatment decisions by typing subgroups of node-negative breast cancer patients. The current study has used a proteomic approach of SELDI-TOF-MS screening to identify differentially cytosolic expressed proteins with a prognostic impact in 30 node-negative breast cancer patients with no relapse versus 30 patients with metastatic relapse. The data analysis took into account 73 peaks, among which 2 proved, by means of univariate Cox regression, to have a good cumulative prognostic-informative power. Repeated random sampling (n = 500) was performed to ensure the reliability of the peaks. Optimized thresholds were then computed to use both peaks as risk factors and, adding them to the St. Gallen ones, improve the prognostic classification of node-negative breast cancer patients. Identification of ubiquitin and ferritin light chain (FLC), corresponding to the two peaks of interest, was obtained using ProteinChip LDI-Qq-TOF-MS. Differential expression of the two proteins was further confirmed by Western blotting analyses and immunohistochemistry. SELDI-TOF-MS protein profiling clearly showed that a high level of cytosolic ubiquitin and/or a low level of FLC were associated with a good prognosis in breast cancer.

Apoferritins↗

PubMed2006

Prognostic biomarkers in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma. Although it represents a curable disease, less than half of the patients are cured with conventional chemotherapy. The highly variable outcome reflects a heterogeneous group of tumors, with different genetic abnormalities and response to therapy. The International Prognostic Index (IPI) is useful in predicting the outcome of DLBCL patients. However, patients with identical IPI still exhibit marked variability in survival, suggesting the presence of significant residual heterogeneity within each IPI category. The discovery of specific genetic alterations and the assessment of protein expression led to the identification of multiple novel single molecular markers capable of predicting the outcome of DLBCL patients independently of clinical variables. The recent application of DNA microarrays and tissue array technologies allowed a better understanding of the biology of lymphoma and the development of novel diagnostic tools capable of improving the current models for outcome prediction. However, much confusion exists in the literature regarding the importance of different prognostic biomarkers and their applicability in routine practice. This review summarizes the recent advances in our understanding of prognostic biomarkers in DLBCL and discusses whether this is the right time for biomarkers-guided risk-adjusted therapy.

Biomarkers, Tumor↗

PubMed2026

MS4A3 as a potential prognostic biomarker for colon cancer: integrated analysis of expression patterns and immune cell infiltration.

BACKGROUND: Membrane Spanning 4-Domains A3 (MS4A3) has been confirmed to possess significant tumor-suppressive potential in various malignancies. However, its expression characteristics and clinical prognostic value in colon cancer (CC) still lack systematic and in-depth investigation. This study aimed to systematically investigate the expression pattern, prognostic value, immune microenvironment association, and biological function of MS4A3 in CC through integrated bioinformatics analyses and experimental validation. METHODS: This study utilized The Cancer Genome Atlas-Colon Adenocarcinoma (TCGA-COAD) cohort to screen for genes significantly associated with CC and combined multiple independent Gene Expression Omnibus (GEO) datasets to validate the expression patterns and prognostic significance of MS4A3. Key biological pathways were identified through gene set enrichment analysis (GSEA), and tumor immune infiltration characteristics were evaluated using the CIBERSORT algorithm. Additionally, the expression of MS4A3 and its impacts on cellular functions were validated at the cellular level through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, Cell Counting Kit-8 (CCK-8), EdU, Transwell, and TUNEL assays. RESULTS: Analysis of public datasets revealed that MS4A3 is significantly downregulated in CC tissues, and its low expression is an independent risk factor for shortened overall survival (OS). GSEA indicated that MS4A3 downregulation is closely associated with the aberrant activation of the pentose phosphate pathway. Immune infiltration analysis showed that low MS4A3 expression is closely linked to the enrichment of M2 macrophages and neutrophils, as well as the upregulation of multiple immune checkpoint genes. In vitro experiments further confirmed that MS4A3 was lowly expressed in CC cell lines. Its overexpression significantly inhibited CC cell viability, proliferation, migration, and invasion, while simultaneously promoting cell apoptosis. CONCLUSIONS: MS4A3 expression is significantly decreased in CC tissues and is significantly correlated with poor prognosis, suggesting that this gene may serve as a potential prognostic biomarker.

MS4A3↗

PubMed2026

ARHGAP22 as a Potential Prognostic Biomarker in Clear Cell Renal Cell Carcinoma: Insights into Tumor Immunity and Co-Expression Networks.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer and is characterized by substantial clinical heterogeneity, highlighting the need for reliable prognostic biomarkers. This study evaluated the expression pattern, prognostic relevance, and immune-related associations of ARHGAP22 in ccRCC using transcriptomic and clinical data from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) cohort, together with external validation data and protein-expression information from the Human Protein Atlas (HPA). ARHGAP22 expression was compared between tumor and adjacent normal tissues, and its associations with overall survival, clinicopathological characteristics, tumor microenvironment scores, and estimated immune-cell fractions were assessed. Co-expression and functional-enrichment analyses were also performed to characterize potential biological associations. ARHGAP22 was significantly upregulated in ccRCC tissues at the transcriptomic level, with corresponding differences observed in immunohistochemical images. High ARHGAP22 expression was associated with shorter overall survival, advanced clinicopathological features, and higher ImmuneScore, StromalScore, and ESTIMATEScore values. CIBERSORT-based analysis showed that the high-expression group had higher estimated fractions of M2 macrophages and regulatory T cells and lower estimated fractions of naïve B cells, resting mast cells, and activated dendritic cells after false discovery rate correction. Functional-enrichment analyses linked ARHGAP22-associated genes to immune-related processes, cell migration, and chemokine- and cytokine-mediated signaling pathways. These findings suggest that ARHGAP22 may represent a potential prognostic and immune-related biomarker in ccRCC, although further independent clinical and experimental validation is required.

Humans↗

PubMed2025

Integrated multi-omics analysis reveals TMEM147 as an immunosuppressive prognostic biomarker in LUAD.

TMEM147, an endoplasmic reticulum (ER) membrane protein, is implicated in lung adenocarcinoma (LUAD) progression, although its precise role remains unclear. To elucidate its function, this study integrated bioinformatics analyses with experimental validation. First, TMEM147 expression was assessed using TCGA and GEO datasets, with validation performed in LUAD cell lines. Survival analysis evaluated its prognostic significance. Subsequently, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and single-sample gene set enrichment analysis (ssGSEA) were employed to identify associated functional pathways and interactions within the tumor immune microenvironment. Transcription factor binding predictions and in vitro functional assays (migration, invasion, proliferation) further characterized TMEM147's role. Results indicated that TMEM147 was significantly upregulated in LUAD and correlated with poor outcomes in patients. FLI1 was predicted as a key transcriptional regulator of TMEM147. Furthermore, TMEM147 expression influenced immune cell infiltration profiles and was associated with pathways involved in ribonucleoprotein biogenesis and oxidative phosphorylation (OXPHOS). Importantly, silencing TMEM147 significantly reduced cancer cell migration, invasion, and proliferation. These findings collectively suggest that TMEM147 promotes LUAD progression and holds potential as both a prognostic biomarker and a therapeutic target.

Bioinformatics analysis↗

PubMed2001

Prognostic biomarkers in resected colorectal cancer: implications for adjuvant chemotherapy.

Knowledge of the prognostic role of biomarkers in colorectal cancer is limited and the routine determination for clinical practice is not warranted. However, for some of these markers, data are promising enough for further evaluation. This review addresses a comprehensive analysis of prognostic biomarkers in colorectal cancer. Data from published studies were collected and analyzed. A sufficient level of evidence suggests that DNA indexes, angiogenesis indicators and some genetic/biochemical markers identity prognostic differences in patients with early-stage colorectal cancer. High-risk patients could be the target for future adjuvant chemotherapy trials and one or more of these markers may identify prognostic subgroups with the same TNM stage category.

Antineoplastic Agents↗

PubMed2004

Identification of genes differentially expressed in breast cancer cell line SKBR3: potential identification of new prognostic biomarkers.

The identification of differentially expressed genes in tumour cells should have important implications in understanding carcinogenesis and developing new therapeutic and prognostic biomarkers. We have combined PCR-based cDNA subtraction and Northern blotting to identify truly differentially expressed genes in breast cancer cell line SKBR3 as compared to normal human mammary epithelial cells (HMEC). Hybridizing probe molecules were rescued from the Hybond N+ membranes and then PCR reamplified. The PCR reamplification is possible due to the fact that all probe molecules contain the same pair of adapter sequences on both ends. After cloning and sequencing three known genes, ribosomal protein L19 (RPL19), ADP/ATP carrier protein and ErbB-2 with high-elevated mRNA levels in SKBR3 were identified. In addition, two overexpressed genes with unknown functions, CXYorf1-related protein and hypothetical protein PRO2605, were found. High-titer andibodies against the recombinant RPL19 were detected in 5 patients out of 50 patients investigated. Thus, the present novel strategy based on the combination of PCR-based cDNA subtraction and Northern blotting should facilitate the identification of truly differentially expressed biomarkers, which may offer the potential to determine the proper drug for an individual patient at a given stage of disease or treatment.

Biomarkers, Tumor↗

PubMed2026

miR-2116-5p functions as a tumor suppressor in lung adenocarcinoma by targeting ADAM12 and serves as a prognostic biomarker.

BACKGROUND: MicroRNAs play key roles in tumor progression. miR-2116-5p is downregulated in lung adenocarcinoma (LUAD), and this study investigated its prognostic value and functional role in the disease. MATERIALS AND METHODS: A total of 125 LUAD patients contributed tissue samples. miR-2116-5p and ADAM12 expression in tissues and cell lines were detected by RT‑qPCR. Clinicopathological correlations of miR-2116-5p were analyzed using the chi-square test. Kaplan‑Meier and Cox regression were employed to assess prognostic significance. CCK‑8, Transwell, and dual‑luciferase reporter assays were performed to investigate miR‑2116-5p function and its targeting of ADAM12. Rescue experiments validated the functional involvement of ADAM12. RESULTS: Significant downregulation of miR-2116-5p was observed in LUAD tissues and cell lines. Low expression was markedly linked to lymph node metastasis (P = 0.013) and advanced TNM stage (P = 0.002). Patients exhibiting reduced miR-2116-5p levels showed worse overall survival, and it was identified as an independent prognostic factor (HR = 2.521, 95% CI: 1.129-5.628, P = 0.020). Functional experiments showed that increasing miR-2116-5p expression suppressed LUAD cell proliferation, migration, and invasion, whereas its knockdown promoted these processes. ADAM12 was confirmed as a direct target, with expression inversely correlated in LUAD tissues (r = -0.749, P < 0.001). ADAM12 overexpression effectively counteracted the ability of miR-2116-5p to suppress proliferation, migration, and invasion. CONCLUSION: miR‑2116-5p suppresses LUAD progression by targeting ADAM12, suggesting it may serve as a prognostic biomarker and therapeutic target.

Humans↗

PubMed1996

Protein expression of p53, bcl-2, and KI-67 (MIB-1) as prognostic biomarkers in patients with surgically treated, clinically localized prostate cancer.

BACKGROUND: Protein expression in the primary tumor of the tumor suppressor gene p53 and the proto-oncogene bcl-2 have been shown to be prognostic biomarkers of cancer recurrence after radical prostatectomy in patients with clinically localized prostate cancer. Cancer cell proliferation as measured by immunohistochemical markers such as the MIB-1 antibody for Ki-67 has recently been suggested to be of prognostic value in prostate cancer. The goal of this study was to determine the clinical use of p53, Ki-67 (MIB-1), and bcl-2 immunohistochemical protein expression in the primary tumor as combined predictors of disease progression after radical prostatectomy (RP). METHODS: Protein expressions of p53, Ki-67, and bcl-2 were evaluated in archival paraffin-embedded RP specimens from 162 patients monitored from 1 to 10 years (mean, 4.5 years) and correlated to stage, grade, race, and serologic (prostate-specific antigen) recurrence after operation. RESULTS: Expression was detected in 112 (69.1%), 44 (27.2%), and 62 (38.3%) of 162 patients for p53 (1+ or greater), bcl-2 (1+ or greater), and Ki-67 (2+ or greater), respectively. Biomarker expressions were not correlated to age and race; however, all increased with increasing stage and grade. The degree of expression by percentage of malignant cells staining correlated to recurrence for p53 and Ki-67 but not for bcl-2. All three markers were correlated to raw and Kaplan-Meier recurrence by means of univariate analysis with recurrence estimates at 6 years of 60.7% versus 24.2%, 84.2% versus 38.6%, and 72.4% versus 30.6% comparing positive versus negative expression of p53, bcl-2, and Ki-67, respectively. p53 and bcl-2 remained as independent prognostic markers by Cox multivariate regression analysis. Although Ki-67 did not remain an independent marker, it added prognostic use in certain subsets of patients. CONCLUSIONS: p53, bcl-2, and Ki-67 (MIB-1) appear to be important biomarkers to predict recurrence in patients with clinically localized prostate cancer after RP, and all three biomarkers deserve further study.

Aged↗

PubMed2025

CS Ratio is an immune-related prognostic biomarker for cervical cancer.

BACKGROUND: The tumor microenvironment (TME) plays a crucial role in cancer progression but its complex structure significant variability among patients present considerable challenges for research. Recent studies have demonstrated that macrophage polarization states defined by the expression levels of CXCL9 SPP1 (CS Ratio) are more prognostically relevant than traditional M1/M2 markers. The CS polarization state reflects a highly coordinated network of pro-tumor anti-tumor variables offering a simplified yet effective immune response indicator for the complex TME. The CS Ratio has been shown to correlate with the abundance of anti-tumor immune cells the gene expression programs of tumor-infiltrating cells responses to immunotherapy. Cervical cancer, one of the most common gynecological malignancies, still faces limited therapeutic options. CXCL9, a member of the CXC chemokine family, plays a critical role in immune regulation, inflammation, tumor growth, angiogenesis, and metastasis. Similarly, SPP1, a cytokine, influences immune-related pathways by regulating molecules such as interferon-γ and interleukin-12. However, no studies have systematically investigated the role of the CS Ratio in cervical cancer or its relationship with immunotherapy characteristics. Research in this area could provide critical insights into the role and clinical potential of the CS Ratio in cervical cancer and related tumors. METHODS: The expression ratio of CXCL9 to SPP1 was analyzed in cervical cancer patients using data from the Gene Expression Omnibus (GEO) database, which revealed significant differences. Data for cervical cancer patients were obtained from The Cancer Genome Atlas (TCGA) database. The optimal cutoff value for the CS Ratio was determined using the maxstat package in R, and Kaplan-Meier (KM) survival curves were constructed. Patients were categorized into High and Low groups based on the median CS Ratio. Immune scores were analyzed, and immune cell infiltration was assessed using CIBERSORT. Differences in the CS Ratio were evaluated across patients with varying pathological T stages and FIGO stages. Additionally, receiver operating characteristic (ROC) analysis was performed using the pROC package in R to calculate the area under the curve (AUC). Univariate and multivariate Cox regression analyses were performed to evaluate the potential of the CS Ratio as an independent prognostic factor in cervical cancer. A Cox regression-based nomogram integrating four key features was subsequently developed for the TCGA-CESC cohort. Nomogram performance was assessed using calibration curves and ROC analysis. RESULTS: The CS Ratio was significantly lower in cervical cancer patients compared to normal controls (P < 0.05). KM survival curves indicated that patients in the CS High group exhibited better prognoses. Immune score analysis revealed significantly higher immune scores (P < 0.05) and lower tumor purity (P < 0.05)in the CS High group compared to the Low group. CIBERSORT analysis revealed significantly higher proportions of CD8+ T cells (P < 0.05) and M1 macrophages (P < 0.05), and a significantly lower proportion of M2 macrophages (P < 0.05), in the CS High group compared to the Low group. The CS Ratio significantly decreased with advancing FIGO stage (P < 0.05). Both univariate (P < 0.05) and multivariate Cox regression analyses (P < 0.05) confirmed the CS Ratio as an independent prognostic factor. ROC analysis demonstrated that the CS Ratio had higher AUC values for predicting 1-year (AUC=0.69), 3-year (AUC=0.66), and 5-year OS (AUC=0.68) than CXCL9 or SPP1 alone. The Cox regression-based nomogram integrating four key features demonstrated predictive capability for 1-, 3-, and 5-year OS in CESC patients (Concordance Index = 0.751; 95% CI: 0.678-0.824; p = 1.50Í10-11). Significant survival differences were observed between the high-risk and low-risk groups based on the nomogram score. ROC analysis yielded high AUC values for survival prediction: 0.85 (95% CI: 0.94-0.75) at 1-year, 0.74 (95% CI:0.84-0.64) at 3-year, and 0.72 (95% CI:0.84-0.61) at 5-year. CONCLUSION: The CS Ratio may serve as a more effective prognostic biomarker for cervical cancer patients.

CXCL9↗