The impact of HPV/HIV co-infection on immunosuppression, HPV genotype, and cervical cancer biomarkers

Abstract

Background

Human papillomavirus (HPV) and human immunodeficiency virus (HIV) co-infection present a significant impact on women's health globally, especially in immunocompromised individuals. HIV-induced immunosuppression promotes the persistence of high-risk HPV infection and increased the progression to cervical cancer. The aim of this systematic review was to assessed the impact of HPV/HIV co-infection on the prevalence and distribution of HR-HPV genotypes, the level of immunosuppression and expression of cervical cancer biomarkers.

Method

The article selection method for this review was based on the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards. The total of eighty-four (84) articles from standard electronic databases mainly Web of Science, PubMed, and Scopus were extracted and reviewed. The articles were published in English between 2008 and 2024 and comprised a total of 80023 participants.

Results

The HR-HPV genotypes reported across various studies include HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 54, 56, 58, 59, 66, 68, 70, 73, and 82. Among HIV positive individuals, the most common circulating HR-HPV genotypes were HPV16, 18, 45, 35, and 58, accounted for 11%, 10%, 9%, 8%, and 8% of cases, respectively. Approximately 29.1% and 30.0% of patients had CD4 counts of 200–400 cells/L and 300–400 cells/L, respectively. The most commonly reported cervical cancer biomarkers were p16INK4a and Ki-67, according to the analysis.

Conclusion

The findings indicate high prevalence of multiple HR-HPV genotypes among HIV positive individuals, indicating the impact of HPV/HIV co-infection on immunosuppression and persistence of HPV infection. The expression of cervical cancer biomarker such as p16INK4a and Ki-67 emphasized target screening and early detection strategy in high-risk population. However, there was no direct impact of HPV/HIV co-infection reported on these biomarkers and required to be studied more especially in people living with HIV.

Highlights

◦ An analysis revealed a significant occurrence of high-risk HPV genotypes, namely HPV16, 18, and 45, among women who had HIV.

◦ In comparison to HIV-negative women, HIV-positive women had a higher probability of experiencing persistent HPV infections and more severe cervical lesions.

◦ Significant correlations were seen between CD4 levels and the occurrence of high-risk HPV infections, suggesting the involvement of immunosuppression in the persistence of HPV.

◦ Cervical cancer biomarkers reported across various studies were p16INK4a and Ki-67, despite these biomarkers reported there was no established direct impact of HPV/HIV co-infection to indicate aggressive disease development.

◦ The results suggest the need for implementation of focused screening and vaccination programs for cervical cancer in populations at high risk, particularly among women who are HIV-positive.

Introduction

Human papillomavirus (HPV) and HIV co-infection present a serious public health problem globally. HPV/HIV co-infection is especially prevalent among women in low-income countries, where healthcare access and preventive measures are frequently inadequate [48]. HPV, a double-stranded circular DNA virus is a well-known carcinogen factor associated with cervical cancer development [25]. High-risk HPV genotypes, particularly HPV 16 and 18, account for about 80% of cervical cancer cases globally [65]. Researchers have identified and grouped over 200 HPV genotypes based on their ability to induce cervical cancer [47]. High risk HPV genotypes such as HPV16, 18, 31,32, 33, 34, 35, 36, 39, 45, 51, 52, 53, 54, 55, 56, 57, 58, 59, 66, 68 and 82. They are linked with development of moderate to high-grade cervical lesions and cervical cancers eventually [6]. Cervical cancer is the fourth most common cancer among women worldwide, with approximately 75,000 new cases and 50,000 deaths reported, with the vast majority in Sub-Saharan Africa [68]. HIV infection significantly increases the risk of HPV infection, persistence, and rapid progression to cervical cancer, resulting in higher cervical cancer prevalence [32]. HPV/HIV co-infection is increasingly prevalent in high-HIV-burden regions like Sub-Saharan Africa, driven by shared transmission routes and the immune suppression caused by HIV [18]. HIV infection produces significant immunosuppression, as evidence by CD4 + T cells declination limiting the body's ability to generate an effective immunological response to HPV infection [27]. As a result, HIV-positive women are more susceptible to persistent HPV infections, increasing their risk of developing high-grade cervical intraepithelial neoplasia (CIN) and invasive cervical cancer [7].

CD4 + T cells are essential in coordinating adaptive immunity, mediate the HPV-related immune response to HPV infection [50]. However, HIV targets and depletes CD4 + T cells, reducing the body’s ability to clear HPV infections allowing HPV persistence and rapid progression of cervical dysplasia and cancer [28]. Research also suggests that HIV infection can cause alteration in the natural history of HPV infection and accelerates the development of cervical cancer [14]. Furthermore, HPV oncogenes, particularly E6 and E7, play an important role in the development of cervical dysplasia and cancer [57]. By targeting tumor suppressor proteins like p53 and pRB, the E6 and E7 proteins disrupt important tumor-suppressor pathways leading to uncontrolled cell growth and genomic instability [51]. HIV co-infection can increase viral oncogene expression dysregulation, which leads to more cells dividing and genetic instability, making cervical lesions more complicated [58]. Moreover, HIV-induced inflammation and alterations in the cervical microenvironment promote the development of cervical cancer [40]. Chronic inflammation enhanced the growth of new cells, the formation of new blood vessels, and the remodeling of tissues creating an environment that is favorable for HPV-associated cancer development [10]. Unbalanced DNA methylation and histone modifications increase the risk of cervix cancer by altering gene expression and promoting tumor growth [33]. Additionally, cervical cancer lesions in women with HPV/HIV co-infection are more severe and progress faster than in HPV mono-infection [65]. Cervical cancer lesions in women with HPV/HIV co-infection are more severe, progress faster, and are associated with a higher likelihood of cytological abnormalities, such as atypical squamous cells and high-grade squamous intraepithelial lesions, indicating disease progression [16].

Biomarkers such as p16INK4a and Ki-67 are useful predictors of aggressive cervical cancer progression in HPV/HIV co-infections [33]. In co-infected patients, higher expression of these biomarkers is associated with higher tumor aggressiveness, metastatic potential, and poorer clinical outcomes [76]. Finally, understanding the immunological, molecular, and clinical interaction between HPV and HIV is key in developing new ways to treat cervical cancer in co-infected patients [53].

Method

Search strategy

The research team conducted a comprehensive literature search and critically analyzed studies that examined the impact of HPV/HIV co-infection, focusing on key aspects such as immunosuppression, the distribution and prevalence of high-risk HPV genotypes, and the expression of cervical cancer biomarkers. The team reviewed both contemporary and older papers on immunosuppression, HPV genotypes, and cervical cancer biomarkers. This comprehensive search was conducted between 1st of March 2024 to 15th of August, 2024, using numerous databases namely; Web of Science, Scopus, and PubMed. The search technique used for Web of Science and Scopus was: ("human papillomavirus" OR HPV) AND ("Human immunodeficiency virus" OR HIV) AND ("suppress OR "Immune suppress" OR genotype OR biomarker") AND ("cervical cancer"). For PubMed, the search string used was: ((human papillomavirus [Title/Abstract] OR HPV [Title/Abstract]) AND (human immunodeficiency virus [Title/Abstract] OR HIV [Title/Abstract]) AND (Immunosuppress [Title/Abstract] OR Immune suppress [Title/Abstract] OR genotype [Title/Abstract] OR biomarker [Title/Abstract]) AND (cervical cancer [Title/Abstract]) NOT (review [Publication Type])). The search only included peer-reviewed literature published in English. The article selection method was based on the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards refer to Fig. 1 for the PRISMA flow chart.

Fig. 1.

PRISMA eligibility flow chart

Inclusion and exclusion criteria

Original articles conducted among women with HPV/HIV co-infection, studies that reported CD4 counts and biomarkers such as Ki-67 and P16 were included in the study. Studies conducted on men or those that did not report HPV and HIV co-infection, with insufficient data on immunosuppression, HPV genotypes, or cervical cancer biomarkers were excluded. Review articles, editorials, letters to the editor, and case reports were also excluded.

Data extraction and synthesis

Data from each of the studies reviewed were collected using excel spread sheet. The data collected include, publication year, name of the author, location of the study, sample size, HR-HPV genotypes reported in HPV/HIV co-infection and HPV mono-infection, method of genotyping, CD4 counts reported and method used, finally cervical cancer biomarker and method used.

Quality assessment

The quality of the article collected for this review was assessed blindly using Rayyan platform by two independent reviewers with invitation of a third reviewer for conflict resolution to reduced personal bias. We first conducted the title and abstract screening, excluding 272 articles that did not meet the inclusion criteria. We detected 39 conflicts among the 98 articles that met the inclusion criteria. The third reviewer resolved these conflicts by screening 109 articles. Two reviewers again downloaded and uploaded 109 full-text PDF articles to Rayyan for comprehensive full-text screening. Following the full-text screening, two reviewers included 78 articles, excluded 10, and detected 20 conflicts. We resolved these conflicts by inviting the third reviewer, which ultimately resulted in 84 articles meeting the eligible criteria and quality assessment for this review.

Results

Search results

The research identified a total of 664 articles across three databases: 328 articles from the Web of Science, 230 articles from Scopus, and 106 articles from PubMed. We then uploaded and merged all the articles on the Rayan platform. We detected 336 duplicate articles during the initial merge. We resolved 181 of these duplicate articles, deleted 255, and left 409 for title and abstract screening on Rayan.

Study characteristic

This systematic review includes publications published from 2008 to 2024. The sample sizes of the included studies ranged from 50 to 5,392 people, with majority of them adopting cross-sectional study design. This systematic review included 84 research articles and 57 were studies conducted in Africa distributed as follows: Kenya (18 articles), South Africa (11 articles), Uganda (8 articles), Rwanda (8 articles), Tanzania (5 articles), Botswana (5 studies), Nigeria (3 studies), Zimbabwe (2 studies), Ghana (2 studies), and Congo, Senegal, Gambia, Morocco, Côte d'Ivoire, Burundi, and Zambia with one study each. In Asia, there were eight (8) studies; India (4 articles), China (2 articles), Korea (1 article), and Pakistan (1 article). Seven (7) articles were identified from Europe including three from Italy, one from Romania, and three from Denmark. Seven articles were also conducted in North America, including four in Brazil, two in Colombia, and one in Guyana. The United States contributed five studies. The studies were carried out in hospitals, clinics, and research laboratories, with individuals coming from both urban and rural locations.

Discussion

This systematic review highlights a significant burden of high-risk HPV genotypes among women living with HIV. Women co-infected with HPV/HIV exhibited a notably higher prevalence of HR-HPV genotypes and multiple HR-HPV infections compared to those with HPV mono-infections shown in Table 1 and Fig. 2. HIV-induce immunosuppressive effects, which reduce the immune system's ability to eliminate HPV infections allowing it to remain in the body for long periods of time as a result of decreased immune surveillance. The weakened immunological milieu enhances HR-HPV integration into the host genome, altering normal cellular function and increasing the risk of neoplastic transformation. The interaction of HIV-induced immunosuppression and HPV persistence highlights the increased risk of cervical cancer in HIV-positive women.

Table 1.

Prevalence and distribution of HR-HPV genotypes among women living with HPV/HIV co-infection and HPV mono-infections reported across various studies reviewed

HR-HPV genotypes Order of HR-HPV prevalence reported		Percentage %		Location	Year	Sample size	Study
HPV/HIV co-infection	HPV mono-infection	HPV/HIV	HPV
16,81,58,72 33,62,73	18,16,31,33,35,45,52,58,66	33.9	13.9	Italy	2008	227	[71]
16,18,31,51,52,58,68	16,52,18,31,51,58	45	32.2	Romania	2015	1032	[73]
16,18,45,33,58,68	16,18	36.9	4.4	Pakistan	2023	200	[5]
35,45,51,52,58,66,18	45,35,33,51,18	63	23	Tanzania	2021	2134	[32]
16,18,31,58,45	16,45,33,18	30	20	Columbia	2018	3399	[9]
16,18,35,51,58,33,45,52,59	16,18,39,45,	52	41	Tanzania	2021	2134	[34]
16,18,45,35,39,51,31,52,56,58,59,68	16,18,45,31,33,35,52,56,58,68	66.7	69.1	South Africa, Kenya	2015	274	[17]
16,18,31,33,35,39,45,52,56,58,59	16,18,31,35,58	65	49	South Africa	2021	193	[66]
16,66,53,58,45,52,61	16,66,53	23.0	4.1	Kenya	2021	317	[45]
16,18,56,45,33,58	16,18,33,35,56,45	87	79	Zimbabwe	2018	107	[37]
16,18,31,33,35,39,45,51,52, 53,56,58,66,70	16,18,31,33,35,39,45,51,52,53,56,58,59,66,68,70	30	4.9	Korea	2014	1938	[54]
16,18,33,52,51,56,66	66,18,16,67,45,33,39	13.4	2.7	Burundi	2019	3500	[44]

The percentage of HR-HPV detection in HPV/HIV co-infections was found to be higher compared to HPV mono-infections in most studies. Some regions report unique HR-HPV profiles. For example, Korea includes genotypes such as HPV-53 and HPV-70, while Africa and other regions consistently shows high prevalence of genotypes such as HPV-16, HPV-18, and HPV-45 followed by 31,33,35 and 58

Fig. 2.

Percentage distribution of HR-HPV genotypes among HIV positive individuals reported across various studies globally. The HR-HPV genotypes reported across various studies include HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 54, 56, 58, 59, 66, 68, 70, 73, and 82. The most common HR-HPV genotypes reported among HIV-positive individuals included HPV 16, 18, 45, 35, and 58, accounting for 11%, 10%, 9%, 8%, 8%, respectively as presented while 31and 33 accounted for 7% each

These findings align with another previews study conducted by Bogale et al. [7] a systematic review and meta-analysis. They found several HR-HPV genotypes, such as HPV16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 54, 56, 58, 59, 66, 68, 69, 70, 73, and 82. Among these genotypes reported, HPV16 (20%) was the most prevalent, followed by HPV45 (15%) and 52 at 13%. Additionally, our findings bear some slight similarities to another systematic review by [68] in sub-Saharan Africa, which also reported HPV16 (18%), 35 (10.12%), 52 (9.7%), and 45 (6.82%). The similarities in these findings suggest a consistent pattern of specific HR-HPV genotypes being the most prevalent in HIV-positive individuals on a global scale. Particularly, HPV16, 35, 45, and 52 stand out as the most commonly reported genotypes in both studies, highlighting their impact on HPV/HIV co-infection.

The emergence of HPV genotypes 31, 33, 35, and 58 in our study indicate their significant contribution to cervical cancer progression on a global scale. Although HPV16 and HPV18 have traditionally been regarded as the most oncogenic genotypes, recent studies have highlighted the increasing clinical relevance of other high-risk HPV genotypes, such as HPV31, 33, 35, and 58. These genotypes belong to the Alpha-9 species group, which is characterized by high prevalence and significant oncogenic potential [2]. Within this group, HPV16, 31, 33, 35, and 58 are particularly associated with cervical cancer, especially in individuals co-infected with HIV [62]. The findings from our current study, which report the presence of these genotypes, further emphasize their considerable oncogenic potential. Their high prevalence and strong association with cervical cancer suggest that these genotypes are playing an increasingly important role in disease progression, particularly in regions with a high burden of HIV, such as sub-Saharan Africa [2]. Notably, most of the studies reviewed in this context were conducted in African populations, as reflected in Table 2, highlighting the regional predominance and clinical significance of these genotypes.

Table 2.

Summary of the study findings

S/N	Author	Year	Simple Size	Study design	Median age	Study setting	Duration	ART	Mean CD4 counts cells/ μL	location
1	Dylla et al	2017	100	Cross sectional study	40	Two clinics	2012–2014	NA	NA	South Africa
2	J.D. Siqueira et al	2016	130	Retrospective study	28	Hospitals and research laboratory	2009–2011	NA	326 cells/ μL	Brazil
3	Ndiaye et al	2012	204	Multicentric study	51.9	Teaching hospitals, research laboratory	2006–2010	NA	NA	sub- saharan Africa
4	MADEDDU et al	2014	57	Cross sectional study	40	Hospital	2008–2009	NA	571 ± 227 cells/ μL	Italy
5	D.D Yar et al	2016	10,603	Case control	40.1	Regional hospital	2013–2014	Yes	NA	Ghana
6	Lina et al	2008	205	Cross sectional study	32.2	Hospital	2001–2004	Yes	200 ± 500 cells/ μL	Italy
7	Howard D,et al	2013	13,690	Cross sectional study		Teaching hospitals	1996–2010	yes	350 cells/ μL	USA
8	Camargo et at	2018	1399	Cross sectional observational study	37.5	Hospital	2007–2013	NA	100 cells/ μL	Colombia
9	Keller et al	2015	2091	Prospective study	35	Hospital	1994–2002	NA	350 cells/ μL	Colombia
10	Amos et at	2012	170	Case control study	40.5	Hospital	2005–2006	Yes	NA	Tanzania
11	Mckenzie et al	2014	510	Retrospective study	40	Hospital	2000–2008	NA	193 cells/ μL	USA
12	Aaron et al	2021	116	Cross sectional study	NA	Research laboratory	2010–2013	NA	NA	Kenya
13	Lynette Denny et al	2019	659	Cross sectional study	55	Hospital and research laboratory	2007–2010	NA	NA	sub- saharan Africa
14	Margot Boudes et al	2021	90	observational, monocentric and historical study	44.2	Laboratory setting	2014–2022	NA	350 cells/ μL	France
15	L. Stewart et al	2016	4068	Cohort study	39.5	Hospital	1994–2011	NA	NA	New York
16	Hugo De et al	2011	274	Case study	40.8	Teaching hospitals	2007–2009	Yes	334 cells/ μL	Kenya, south Africa
17	James Mburu et al	2023	647	Cross sectional study	42.8	Hospital	NA	Yes	200 cells/ μL	Kenya
18	Ramona et at	2015	1,032	Cross sectional study	36.5	Hospital	2013–2014	Yes	369 cells/ μL	Romania
19	McGrath et al	2017	500	Cross sectional study	38.0	Hospital	2019	Yes	371 cells/ μL	Kenya
20	de Vuyst, et al	2015	250	Cross sectional and perspective	37	Hospital	2015	NA	NA	Kenya
21	Nyabigambo et al	2022	750	Cross sectional study		Hospital	NA	Yes	< 500 cells/ μL	Uganda
22	Chung et al	2013	500	Cross sectional study	40	Hospital	2009	yes	350 cells/ μL	Kenya
23	Nantale et al	2024	300	Cross sectional study	33	ART clinic	2021–2022	YES	NA	Uganda
24	Paul Thistle et al	2020	400	Cross sectional study	42	Hospital	NA	NA	NA	Zimbabwe
25	Hafsa Aziz et al	2023	135	Cross sectional study	46	Hospital	2017–2019	NA	NA	Pakistan
26	Rowhani-Rahbar et al	2008	5392	Cross sectional study	31.4	Infectious disease clinic	1993–19,998	NA	< 500 cells/ μL	Senegal
27	SR Wall et al	2005	1348	NA	NA	Hospital	1999	NA	NA	Gambia
28	Gad Murenzi et al	2021	500	Case control	NA	Health center	1996–2002	NA	NA	Rwanda
c	Akakpo et al	2023	330	Cross sectional study	47.2	Teaching hospital	2020–2021	Yes	NA	Ghana
30	Wanja Karani et al	2021	1854	Case control study	39	Teaching hospital	NA	NA	NA	Kenya
31	Tsimba Lemba et al	2023	284	Population based cross sectional study	37.8	Hospital	2021–2022	NA	NA	Congo
32	Wenyan Huo et al	2020	226	Cross sectional study	43.7	Teaching hospital	2016–2017	NA	NA	China
33	Ruby Mcharo et al	2021	1,134	Perspective study	40	Referral Hospital	2013–2020	Yes	200 cells/ μL	Tanzania
34	Lall et al	2021	100	Cross sectional study	39.4	ART clinic	4 years	yes	363 cells/ μL	India,
35	Nakigozi et al	2024	5856	Descriptive cross-sectional study	40	Health centers	NA	Yes	NA	Uganda
36	Tawe et al	2020	126	Cross sectional study	45	Hospital/ research laboratory	2013–2016	Yes	487 cells/ μL	Botswana
37	Tawe et al	2022	98	Cross sectional study	50	Hospital	NA	NA	NA	Botswana &Kenya
38	Murenzi et al	2022	5000	Cross sectional study	NA	Military hospital	2017–2018	NA	NA	Rwanda
39	Ongeziwe et al	2020	193	Cross sectional study	40	Referral hospital	2017–2019	NA	NA	South Africa
40	Leitao et al	2008	545	Case control study	40	Hospital	1995–2006	Yes	208 cells/ μL	USA
41	Omire et al	2020	217	Cross sectional study	36	Referral hospital	NA	NA	NA	Kenya
42	Orang’o et al	2020	800	Cross sectional study	30	Hospital	2016–2017	NA	NA	Kenya
43	Maina et al	2023	200	Group randomized clinical trials	37	Hospital and Research laboratory	2010–2016	Yes	374 cells/ μL	Kenya
44	Uwamungu et al	2023	50	Cross sectional study	53.1	Teaching hospital	NA	NA	200 cells/ μL	Rwanda
45	Mbuya et al	2021	373	Longitudinal case control study	41	Referral hospital	NA	NA	NA	Tanzania
46	Njue et al	2021	317	Case control study	34	Hospital	2019	NA	NA	Kenya
47	Houlihan et al	2016	503	Cohort study	17.8	Research laboratory	2010	NA	NA	Tanzania
48	Mitchell et al	2017	87	Cross sectional study	38.9	HIV clinic	2013	Yes	350 cells/ μL	Uganda
49	Wentzensen et al	2014	363	Cross sectional study	53	Hospital	2009–2010	Yes	350 cells/ μL	USA
50	Ouladlahsen et al	2018	25	Longitudinal cohort study	39	Research laboratory	2013–2016	NA	523 cells/ μL	Morocco
51	Abel et al	2019	439	Cross sectional study	46	4 Hospitals	2011–2014	Yes	185 cells/ μL	Guiana
52	J. N. MBATHA ET AL	2017	1223	Cross sectional study	17.5	Govt. High school	2010–2013	NA	NA	South Africa
53	Ermel et al	2016	51	Longitudinal study	39	Cervical cancer Clinic	2015	Yes	350 cells/ μL	Kenya
54	MUDINI et al	2018	107	Cross sectional study	44	Hospital	2014–2015	NA	NA	ZIMBABWE
55	A Jaquet et al	2012	1940	Cross sectional study	36	Clinic	2010	yes	471 cells/ μL	Coˆ te d’Ivoire
56	McDonald et al	2012	1,050	Cross sectional study	37	Hospital	1998–1999	NA	NA	South Africa
57	McDonald et al	2014	9,421	Cross sectional study	NA	Clinical sites	2000–2002	NA	NA	South Africa
58	Badial et al	2018	80	Cross sectional study	39.3	Hospital	2010–2011	Yes	50 cells/ μL	Brazil
59	Park EK, et al	2014	1,938	Retrospective study	47	Hospital	2009–2012	NA	307 ± 351 cells/ μL	Korea
60	Thorsteinsson et al	2019	96	Prospective observational cohort study	42.5	Hospital	2011–2014	yes	350 cells/ μL	Denmark
61	Menon et al	2017	616	Observational cross-sectional study	28	Hospital	2009–2015	NA	NA	Kenya
62	Ndizeye et al	2019	3500	Cross sectional study	39.9	Health center	2013–2016	NA	NA	Burundi
63	Mujuni et al	2016	255	Cross sectional study	39	Hospital	2014	yes	200 cells/ μL	Tanzania
64	Ezechi et al	2014	536	Cross sectional study	37	Cervical cancer clinic	NA	yes	200 cells/ μL	Nigerian
65	Jaya et al	2016	216	Cross sectional study	35	Hospital	2010	NA	350 cells/ μL	India
66	Wu TJ, et al	2016	146	Cross sectional study	35.4	AIDS center	2010–2012	NA	506 cells/ μL	Kenya
67	Meiring et al	2012	109	Cross sectional study	31	ARV clinic	NA	Yes	NA	South Africa
68	Ermel et al	2019	285	Longitudinal study	37	Clinic	2015–2016	NA	NA	Kenya
69	Murenzi G, et al	2018	5000	Retrospective study	NA	Hospital	1996–2002	NA	NA	Rwanda
70	Odida et al	2011	316	Case control study	40	Reginal Hospital	2004–2006	NA	100 cells/ μL	Uganda
71	A.E. Luque et a	2010	225	Cross sectional study	38.3	AIDS clinical trial clinic	NA	NA	346 cells/ μL	Uganda
72	Ramogola-Masire et al	2011	100	Cross sectional study	36	Health clinic	2009–2010	NA	238 ± 458 cells/ μL	Botswana
73	Sarkar et al	2011	1106	Hospital based cross sectional study	30	Hospital	NA	yes	NA	India
74	Sahasrabuddhe et al	2007	150	Cross sectional study	36.2	Teaching hospital	NA	NA	208 cells/ μL	Zambia
75	Anastos et al	2010	710	Observational cohort study	40	Hospital	2005	NA	100 cells/ μL	Rwanda
76	Dartell et al	2012	3603	Cross sectional study	38	Cancer institution	2008–2009	NA	NA	Tanzania
77	Namujju et al	2011	1943	Cohort study	23	Hospital	2011	NA	NA	Uganda
78	Blossom et al	2007	135	Cross sectional study	25	Referral hospital	2002 6 weeks	NA	NA	Uganda
79	Bogale et al	2022	578	Comparative cross-sectional study	38.9	HIV clinic	2021	NA	NA	Ethiopia
80	Chakravarty et al	2016	216	Cross sectional study	35	Hospital	2010–2012	Yes	350 cells/ μL	India
81	Chambuso et al	2020	181	Cross sectional study	35	Hospital	2016–2017	NA	NA	Ghana
82	De lemos et al	2012	237	Cross sectional study	40	Hospital	2005–2006	NA	200 cells/ μL	Brazil
83	Di salvo et al	2023	10,241	implementation study	NA	Referral hospital	NA	NA	NA	Tanzania
84	Konopnicki et al	2013	825	Perspective observational study	38	Hospital	2002- 2011	Yes	426 cells/ μL	Belgium

The studies included in this review span across multiple continents, with significant representation from Africa, North America, South America, Asia, and Europe. All the studies utilized Virginia Swabs except a study conducted in Uganda by Nyabigambo et al. [46] who utilized urinary sample

Additionally, several factors, such as the number of included studies, the analysis method, study setting variations, and differences in the timeframe of these studies, likely contribute to the observed variation in percentage and order of prevalence. Additionally, the various diagnostic instruments, combination of different technology and genotyping methodologies used in different research projects may influence the differences in reported prevalence. These methods' sensitivity and specificity can influence the detection rates of different HPV genotypes. PCR based techniques was the most commonly used HPV genotyping method (38.8%), followed by Roche Linear Array (13.8%) and Gene Xpert (11.3%). These methods rely on PCR as the foundational step for amplification, either for direct analysis or to prepare DNA for further processes like hybridization or sequencing as summarized in Table 3.

Table 3.

Percentage molecular genotyping methods reported across various studies reviewed

Genotyping Method	Principle of operation	Studies	Percentage
Gene Xpert	Used Real-Time PCR technology to detect and quantify target nucleic acid sequence	[26, 32, 43]	11.3
Ampfire	Employ isothermal amplification for rapid and sensitive detection of target nucleic acid sequence without thermal cycling	[36, 40]	5.0
COBAS	Used Real-Time PCR technology for quantitative detection and differentiation of HPV genotypes	[64]	1.3
Hologic	Used target captured and transcription mediation amplification for detecting and genotyping HPV	[17, 49]	6.3
Hybrid assay 2	Combine hybrid captured and signal amplification to detect and quantify HPV genotypes	[14]	5.0
Hybrispot machine (PCR)	Employ PCR technology to detect and quantify HPV genotypes	[66]	1.3
Line probe assay	Used reverse hybridization to detect and quantify HPV genotypes often with colorimeter or chemiluminescent	[20, 73]	1.3
PapilloCheck	Used DNA chip technology to detect and differentiate HPV multiple genotypes simultaneously	[8]	1.3
PCR	Amplified HPV DNA using thermal cycler, consisting of denaturation, annealing and extension steps	[14, 44]	38.8
LiPA Extra assay	The test uses reverse hybridization, biotin-labeled PCR-amplified HPV DNA binds to specific oligonucleotide probes immobilized on a strip, and a colorimetric reaction identifies the genotypes based on probe binding	[14]	1.3
PCR & sequencing	Combines PCR amplification with sequencing to identify the exact nucleotide sequence of the generated DNA	[12]	2.5
PCR-microarray system	Combining PCR amplification with microarray technology allows for high-throughput detection and genotyping of multiple DNA sequences at once	[54]	1.3
Real-Time hr-HPV assay	Real-time PCR is used to detect high-risk HPV genotypes, utilizing fluorescent probes that allow for real-time amplification monitoring	[67]	1.3
Roche Linear Array	To detect multiple HPV genotypes, PCR is used first, followed by reverse hybridization on a linear array	[22, 32, 59]	13.8
RT-PCR	Reverse transcription polymerase chain reaction (PCR) is used to convert RNA into DNA, which is subsequently amplified to detect and quantify RNA targets	[35, 38]	8.8
Sanger sequencing MTd	To sequence DNA, chain-terminating nucleotides are used, which allows the exact nucleotide sequence to be determined by measuring the length of terminated fragments	[61]	1.3

The table presented molecular genotyping method used utilized in detecting HR-HPV genotypes across various studies reviewed. PCR-based methods dominate (e.g., standard PCR: 38.8%, Roche Linear Array: 13.8%), while isothermal amplification (Gene Xpert: 11.3%, Ampfire: 5.0%) and hybridization techniques (LiPA Extra assay, Line Probe) are less common. Advanced sequencing and microarray systems are used minimally, reflecting technological variation based on study

These variations could also be driven by regional differences in HPV and HIV co-infection epidemiology. For example, the burden of HPV-related cancer may differ between regions due to differences in sexual behavior, cultural practice, and access to preventive measures like the HPV vaccine and cervical cancer screening programs [38]. Percentage distribution of HR-HPV genotypes among HIV positive individuals reported across various studies globally are shown in Fig. 2. Variations in the demographic features of study populations, such as age, lifestyle, and immunological status, may also contribute to the discrepancy [68]. Different levels of immunosuppression among HIV-positive individuals in different locations could impact the prevalence and persistence of some specific HR-HPV genotypes [38]. For example, majority of the studies included in this review reported low CD4 count levels within the 200–500 cells/µL ranges [9, 24, 30, 31, 31] and [11] reported that CD4 counts between 200 and 400 ml/L and 300 and 400 ml/L accounted for 29.1% and 30.0%, respectively as summarized in Table 4.

Table 4.

Showing CD4 count reported across various studies

CD4 count cells/μL	Percentage	Frequency	References
200–300	29.1	32	[9, 9, 11, 24, 30, 31, 31]
300–400	30.0	33	[24, 72]
400–500	23.6	26	[13, 31]
500–600	7.3	8	[4, 69, 73]
600–700	1.8	2	[1, 29, 74]
700–800	0.9	1	[29],
800–900	1.8	2	[18]
900–1000	0.9	1	[15]
1000–2000	3.6	4	[9, 21]
2000–4000	0.9	1	[9]

Though these studies did not report immunosuppression, however, it is generally known that CD4 counts below 400 ml/μL have been categorized as immunosuppressed, ranging from severe (below 300 ml/μL CD4 counts) to moderate (300–499 ml/μL CD4 counts). From our review, we observed that 29.1% fall within 200 -300 ml/μL, 30% fall within 300 s-400 ml/μL, 23.6% fall within 400–500 ml/μL, and 17.2% were above 500 ml/μL which is regarded as normal as shown in Table 4. The percentage values in Table 4 represent the number of times authors reported the CD4s within the range

The percentages of different methods used to measure CD4 counts in the reviewed studies reflects their availability, accessibility, and operational advantages as summarized in Table 5. The FACS Count Analyzer, which was reported in 33.3% of the studies reported was the most commonly utilized method due to its dependability and adaptability for resource-constrained settings, particularly in high HIV incidence areas [20]. The CD4 Counter, which was been reported in 20.0% of studies, is popular since it is inexpensive, portable, and simple, making it excellent for rural or underserved areas. Less popular technologies, including as Cell Analyzers, FACSCount, and Flow Cytometry (all 4.4%), are typically reserved for advanced research or clinical settings. While flow cytometry provides high sensitivity and multi-parameter analysis, its cost and technical requirements limit its utilization, and other advanced systems are equally constrained by operational expenses and maintenance requirements [11].

Table 5.

CD4 counting Methods Reported across various studies

CD4. Method	Studies reported	Principle of operation	References
CD4 counter	9	CD4 cells are counted using a simplified flow cytometry approach, which frequently includes fluorescence-labeled antibodies specific to CD4 + T cells	[4, 48]
Cell Analyzer	7	Employs automated technologies that use laser-based flow cytometry to evaluate and count numerous cell types, including CD4 + T cells, using surface markers	[19, 38]
FACS count analyzer	15	Uses fluorescence-activated cell sorting (FACS) to count and evaluate CD4 + T lymphocytes by marking them with specific fluorescent antibodies and detecting them using flow cytometry	[11, 12, 49]
FACSCount	2	A flow cytometer that is specifically intended to count CD4 + T lymphocytes by identifying and quantifying them with fluorochrome-labeled antibodies	[20, 24]
Flow cytometer	2	Laser-based technology is used to evaluate the physical and chemical properties of cells, detecting and counting CD4 + T cells using specific fluorescence markers	[3, 12]

The FACS Count Analyzer was the most frequently method reported in 33.3% of the studies. The CD4 Counter is also quite commonly used method reported in 20.0% of the studies. Other methods such as Cell Analyzer, FACSCount, and Flow Cytometer are used less frequently, with FACSCount and Flow Cytometer each reported in 4.4%

This finding suggests significant immunosuppression, which is prevalent among people with advanced HIV infection. It weakens the body's ability to eliminate HPV infections, leading to persistent infections that increase the risk of developing cervical cancer. Interestingly, HPV-16 was prevalent in all the studies reporting HIV-related immunosuppression. Some of the studies reported immunosuppression even when 100% of the study participants were on antiretroviral therapy (ART) [17, 42, 54, 68]. This highlights the importance of monitoring and managing HPV infection in HIV-positive individuals, particularly those undergoing ART, and screening for cervical cancer regularly.

The types of samples utilized in laboratory analysis have a substantial impact on the identification and prevalence rates of various high-risk HPV (HR-HPV) genotypes. For instance, Nyabigambo et al. [46] conducted one of the reviewed studies in Uganda and reported varying prevalence rates for HPV genotypes among HIV-positive patients. In this study, HPV58 was the most prevalent (87.1%), followed by HPV16 (58.6%), 33 and 59 at 48.6%, 31 (41.4%), and 18 (31.7%) among HIV-positive women. Notably, this study used self-collected urine samples rather than vaginal swabs, which may affect the identification and prevalence rates of specific HPV genotypes. Using other types of samples, such as urine, may produce different results compared to established procedures like vaginal swabs, thus changing the observed distribution of HR-HPV.

Various studies [22, 42, 54] have reported an association between HPV infection and HIV infection, the number of sexual partners, early sexual debut, and smoking. Smoking suppresses the immune system, increasing vulnerability to HPV infections [21]. The cervix's immaturity during adolescence makes early sexual debut a risk factor, increasing susceptibility to HR-HPV infection. The transformation zone, where HPV-related cancer normally develops, is more exposed and susceptible to infection [39]. Additionally, having sex with multiple partners increases the likelihood of exposure to both HPV and HIV [56]. The reported risk factors vary from one region to another, which is the primary reason for the global variation in the distribution of HR-HPV genotypes.

We looked at studies that reported the impact of HPV/HIV co-infection on cervical cancer biomarkers. The cervical cancer biomarkers include were, P16INK4a, Ki-67, and p16INK4a/Ki-67 combined as reported in Table 6. P16INK4a, a cyclin-dependent kinase inhibitor that acts as a tumor suppressor by inhibiting CDK4 and CDK6, preventing phosphorylation of the retinoblastoma (Rb) protein, and causing cell cycle arrest in the G1 phase [70]. As a compensatory mechanism, the cell will upregulate p16INK4a overexpression, which is detectable in HPV-infected cells. p16INK4a was utilized in 50% of the studies of the studies reviewed reporting cervical cancer biomarkers [31, 41] Ki-67 is a cellular proliferation marker whose presence signals active cell division, a hallmark of malignant progression was utilized in 12% of the studies reporting cervical cancer biomarkers [31]. while the combination of p16INK4a and Ki-67 was utilized in 38% of the studies that reported these biomarkers [49, 63].

Table 6.

Percentage cervical cancer biomarkers reported across various studies reviewed

Method	Studies Reported	Percentage %	References
P16INK4a	4	50%	[23]
Ki-67	1	12%	[31],
p16 INK4a + / Ki-67 +	3	38%	[49, 63],

Key: P16INK4a = P16, the molecular weight of protein (16 kilodaltons), INK (inhibitor kinase), and a (CDKN2A). Ki (Kiel) and 67 (67 number of antibodies in the protein)

P16INK4a was the most commonly utilized biomarker reported in 50% of the studies reviewed. The Ki-67 biomarker was reported in 12% of the studies, while the combination of P16INK4a + /Ki-67 + was reported in 38% of the studies reviewed

However, these studies did not report the impact of HPV/HIV co-infection on cervical cancer biomarker, This limitation may stem from the relatively small number of studies meeting inclusion criteria and limited research conducted on these biomarkers in the context of co-infection [31, 41] One of the study reviewed utilized p16INK4a to detect CINI, CIN II and CIN III in the context of HPV infection alone and further concluded that p16INK4a can be used as a biomarker for early screening of cervical cancer [23]. Another study utilized the combination of p16INK4a and Ki-67 by immunohistochemistry to detect cervical cancer in HPV/HIV co-infection but could not established any significant impact of HIV co-infection on cervical cancer biomarkers [63]. These biomarkers were utilized using immunohistochemistry accounted for 87.3% of the studies followed by ELISA at 12.7% of the studies that reported cervical cancer biomarkers as presented in Table 7. It is worth to note that p16INK4a detection as cervical cancer biomarker did not do well using ELISA method [51]. These biomarkers can be expressed more in the present of HIV infection, suggesting chronic HPV infection notably in people with weak immune systems leading to more oncogenic activity and cell proliferation. This highlights the importance of targeted screening and early detection measures in this high-risk population for effectively managing and treating HPV-related cancers. When these markers are present together, they make it easier to detect both cervical intraepithelial neoplasia (CIN) and cervical cancer as suggested by [23].

Table 7.

Cervical cancer biomarkers determination methods reported

Method	Percentage	Principle of Operation	References
Immunohistochemistry	87 .3%	Detects and visualizes specific proteins in tissue slices via antibodies coupled with enzymes or fluorophores. The presence of Ki-67 is indicative of cell growth	[23, 31, [31, 49]
ELISA	12. 7%	The p16 protein, a tumor suppressor, is identified in tissue samples using p16-specific antibodies. High p16 levels are linked to HPV-related carcinogenesis and cervical cancer	[51]

The most common method used by to assess these biomarkers was Immunohistochemistry accounted for 87.3% of the studies reported while ELISA accounted for 12.7% of the studies reviewed

Key: ELISA Enzymes-linked immunosorbent assay

The majority of the studies included in this review were from sub-Saharan Africa, where the burden of HPV and HIV is high. Sub-Saharan Africa has higher rates of HPV-related malignancies, such as cervical cancer, owing to poor access to preventive interventions such as the HPV vaccine and regular cervical cancer screening programs [75]. Furthermore, the high prevalence of HIV in this region increases the incidence and persistence of HR-HPV infections due to the immunocompromised status of HIV-positive individuals [20]. However, other regions with high HIV incidence, such as parts of Asia, Eastern Europe, and Latin America, also face comparable issues [60]. In these areas, low healthcare infrastructure, insufficient access to preventative treatments, and high levels of immunosuppression among HIV-positive individuals all contribute to an increased burden of HPV and the risk of cervical cancer [73]. A study in Brazil [6] found that HIV-immunosuppressed patients with HPV16, 56, and 70 were found to have CIN I, CIN II, and CIN III. This finding underscores the global reach of the problem, given that other regions with high HIV prevalence have reported similar patterns of HPV-related cervical lesions. The fact that HPV types 16, 56, and 70 were found in the cohort shows a range of high-risk HPV genotypes that can cause cervical lesions, which is similar to the problem of HPV and HIV co-infection in the rest of the world. Another study in Denmark [69] reported HPV 58, 52, 51, and 35 as the most common HPV genotypes.

The findings indicate that in addition to the most prevalent high-risk HPV (HR-HPV) genotypes, such as HPV16, 18, and 45, other HR-HPV genotypes are emerging, particularly among HIV-positive individuals. These findings highlight the dynamic nature of HPV genotype distribution in the context of HPV/HIV co-infection. HIV-positive individuals are particularly susceptible to a broader range of HR-HPV infections due to their compromised immune systems. HIV-induced immunosuppression reduces the host’s ability to effectively clear HPV infections, creating an environment conducive to persistent and multiple HR-HPV infections. This leads to the coexistence and persistence of numerous HR-HPV genotypes, which increases the chance of developing cervical intraepithelial neoplasia (CIN) and cervical cancer. Emerging HR-HPV genotypes, including HPV58, 70, and 56, are of special concern due to their high carcinogenic potential. These genotypes have been demonstrated to have an important role in the evolution of cervical lesions, complicating disease management and preventative efforts. The interplay between HIV-induced immunosuppression and HR-HPV infections creates a synergistic effect that accelerates the oncogenic process. Persistent infections with multiple HR-HPV genotypes in HIV-positive individuals not only increase the risk of higher-grade CIN but also reduce the efficacy of natural immune responses to control HPV proliferation. This situation underscores the importance of tailored public health interventions, including regular screening for a broader range of HR-HPV genotypes, timely treatment of cervical lesions, and enhanced HPV vaccination coverage targeting HIV-positive populations.

Conclusion

This systematic review reveals high prevalence of multiple high-risk HPV genotypes among HIV-positive individuals, highlighting the impact of HPV/HIV co-infection on cervical cancer development. Low CD4 counts ranging from severe to moderate immunosuppression were found to associated with increased persistence and progression of HR-HPV infections, emphasizing the need for efficient HPV/HIV care to reduce immunosuppression. While p16INK4a and Ki-67 were utilized as cervical cancer biomarkers, there was no direct impact of HPV/HIV co-infection reported on these biomarkers required to be studied more especially in people living with HIV.

Study limitations

• oSelection Bias: The review may have excluded unpublished studies and gray literatures potentially misrepresenting some certain populations.
• oLack of quantitative meta-analysis: without meta-analysis, the study relies on descriptive synthesis limiting precision and the ability to assess heterogeneity statistically.
• oHeterogeneity Across Studies: Variations in study design, population characteristics, and diagnostic methods limited comparability and generalizability.
• oLimited Data on Biomarkers: Few studies assessed cervical cancer biomarkers (e.g., Ki-67, p16, p53), with inconsistent methods across studies.
• oReporting Bias: Published studies may favor significant findings, possibly skewing interpretations.

Abbreviations

ART

Antiretroviral Therapy

CD4

Cluster of Differentiation

CIN

Cervical Intraepithelial Neoplasia

COX-2

Cyclooxygenase-2

HIV

Human Immunodeficiency Virus

HPV

Human Papillomavirus

HR-HPV

High-Risk Human Papillomavirus

IL

Interleukin

iNOS

Inducible Nitric Oxide Synthase

MPO

Myeloperoxidase

PSA

Prostate-Specific Antigen

TGF-β1

Transforming Growth Factor Beta 1

TNF-α

Tumor Necrosis Factor Alpha

VEGF

Vascular Endothelial Growth Factor

Rb

Retinoblastoma

CDK

Cyclin-dependent kinase

ELISA

Enzymes linked immune absorbent assay

PCR

Polymerase chain reaction

DNA

Deoxyribose acid

PRISMA

Referred Reporting Items for Systematic Reviews and Meta-Analyses

Authors’ contributions

SDT, RSD, IVF: Writing – original draft, Conceptualization, Investigation, Visualization, IJE, MEE, SAM: Writing– review & editing. RSD, CJ, AC: Validation, data analysis, CD, TSS: Writing – review & editing Supervision. IBA, PMA: Supervision, Proof reading, Writing– review & editing of the manuscript.

Funding

No specific funding issuing from public, commercial, or nonprofit groups for the work.

Data availability

Data Availability Statement The authors confirm that the data substantiating the conclusions of this investigation can be found in the present manuscript and its Supplementary Information files. If different formats of raw data files are required, they can be obtained from the respective author upon a fair request.

Declarations

Ethical approval and consent to participate

No Ethical approval was required for the work.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.